two version of R2R are here HEAD master

71 files changed, 73522 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/__init__.py
new file mode 100644
index 00000000..da466144
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/__init__.py
@@ -0,0 +1,18 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from ._azure_machine_learning_workspaces import AzureMachineLearningWorkspaces
+from ._version import VERSION
+
+__version__ = VERSION
+__all__ = ['AzureMachineLearningWorkspaces']
+
+# `._patch.py` is used for handwritten extensions to the generated code
+# Example: https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md
+from ._patch import patch_sdk
+patch_sdk()
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_azure_machine_learning_workspaces.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_azure_machine_learning_workspaces.py
new file mode 100644
index 00000000..3feecd1d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_azure_machine_learning_workspaces.py
@@ -0,0 +1,195 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from copy import deepcopy
+from typing import TYPE_CHECKING
+
+from msrest import Deserializer, Serializer
+
+from azure.mgmt.core import ARMPipelineClient
+
+from . import models
+from ._configuration import AzureMachineLearningWorkspacesConfiguration
+from .operations import BatchDeploymentsOperations, BatchEndpointsOperations, CodeContainersOperations, CodeVersionsOperations, ComponentContainersOperations, ComponentVersionsOperations, ComputeOperations, DataContainersOperations, DataVersionsOperations, DatastoresOperations, EnvironmentContainersOperations, EnvironmentVersionsOperations, JobsOperations, ModelContainersOperations, ModelVersionsOperations, OnlineDeploymentsOperations, OnlineEndpointsOperations, Operations, PrivateEndpointConnectionsOperations, PrivateLinkResourcesOperations, QuotasOperations, SchedulesOperations, UsagesOperations, VirtualMachineSizesOperations, WorkspaceConnectionsOperations, WorkspaceFeaturesOperations, WorkspacesOperations
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any
+
+    from azure.core.credentials import TokenCredential
+    from azure.core.rest import HttpRequest, HttpResponse
+
+class AzureMachineLearningWorkspaces(object):    # pylint: disable=too-many-instance-attributes
+    """These APIs allow end users to operate on Azure Machine Learning Workspace resources.
+
+    :ivar batch_endpoints: BatchEndpointsOperations operations
+    :vartype batch_endpoints:
+     azure.mgmt.machinelearningservices.operations.BatchEndpointsOperations
+    :ivar batch_deployments: BatchDeploymentsOperations operations
+    :vartype batch_deployments:
+     azure.mgmt.machinelearningservices.operations.BatchDeploymentsOperations
+    :ivar code_containers: CodeContainersOperations operations
+    :vartype code_containers:
+     azure.mgmt.machinelearningservices.operations.CodeContainersOperations
+    :ivar code_versions: CodeVersionsOperations operations
+    :vartype code_versions: azure.mgmt.machinelearningservices.operations.CodeVersionsOperations
+    :ivar component_containers: ComponentContainersOperations operations
+    :vartype component_containers:
+     azure.mgmt.machinelearningservices.operations.ComponentContainersOperations
+    :ivar component_versions: ComponentVersionsOperations operations
+    :vartype component_versions:
+     azure.mgmt.machinelearningservices.operations.ComponentVersionsOperations
+    :ivar data_containers: DataContainersOperations operations
+    :vartype data_containers:
+     azure.mgmt.machinelearningservices.operations.DataContainersOperations
+    :ivar data_versions: DataVersionsOperations operations
+    :vartype data_versions: azure.mgmt.machinelearningservices.operations.DataVersionsOperations
+    :ivar datastores: DatastoresOperations operations
+    :vartype datastores: azure.mgmt.machinelearningservices.operations.DatastoresOperations
+    :ivar environment_containers: EnvironmentContainersOperations operations
+    :vartype environment_containers:
+     azure.mgmt.machinelearningservices.operations.EnvironmentContainersOperations
+    :ivar environment_versions: EnvironmentVersionsOperations operations
+    :vartype environment_versions:
+     azure.mgmt.machinelearningservices.operations.EnvironmentVersionsOperations
+    :ivar jobs: JobsOperations operations
+    :vartype jobs: azure.mgmt.machinelearningservices.operations.JobsOperations
+    :ivar model_containers: ModelContainersOperations operations
+    :vartype model_containers:
+     azure.mgmt.machinelearningservices.operations.ModelContainersOperations
+    :ivar model_versions: ModelVersionsOperations operations
+    :vartype model_versions: azure.mgmt.machinelearningservices.operations.ModelVersionsOperations
+    :ivar online_endpoints: OnlineEndpointsOperations operations
+    :vartype online_endpoints:
+     azure.mgmt.machinelearningservices.operations.OnlineEndpointsOperations
+    :ivar online_deployments: OnlineDeploymentsOperations operations
+    :vartype online_deployments:
+     azure.mgmt.machinelearningservices.operations.OnlineDeploymentsOperations
+    :ivar schedules: SchedulesOperations operations
+    :vartype schedules: azure.mgmt.machinelearningservices.operations.SchedulesOperations
+    :ivar operations: Operations operations
+    :vartype operations: azure.mgmt.machinelearningservices.operations.Operations
+    :ivar workspaces: WorkspacesOperations operations
+    :vartype workspaces: azure.mgmt.machinelearningservices.operations.WorkspacesOperations
+    :ivar usages: UsagesOperations operations
+    :vartype usages: azure.mgmt.machinelearningservices.operations.UsagesOperations
+    :ivar virtual_machine_sizes: VirtualMachineSizesOperations operations
+    :vartype virtual_machine_sizes:
+     azure.mgmt.machinelearningservices.operations.VirtualMachineSizesOperations
+    :ivar quotas: QuotasOperations operations
+    :vartype quotas: azure.mgmt.machinelearningservices.operations.QuotasOperations
+    :ivar compute: ComputeOperations operations
+    :vartype compute: azure.mgmt.machinelearningservices.operations.ComputeOperations
+    :ivar private_endpoint_connections: PrivateEndpointConnectionsOperations operations
+    :vartype private_endpoint_connections:
+     azure.mgmt.machinelearningservices.operations.PrivateEndpointConnectionsOperations
+    :ivar private_link_resources: PrivateLinkResourcesOperations operations
+    :vartype private_link_resources:
+     azure.mgmt.machinelearningservices.operations.PrivateLinkResourcesOperations
+    :ivar workspace_connections: WorkspaceConnectionsOperations operations
+    :vartype workspace_connections:
+     azure.mgmt.machinelearningservices.operations.WorkspaceConnectionsOperations
+    :ivar workspace_features: WorkspaceFeaturesOperations operations
+    :vartype workspace_features:
+     azure.mgmt.machinelearningservices.operations.WorkspaceFeaturesOperations
+    :param credential: Credential needed for the client to connect to Azure.
+    :type credential: ~azure.core.credentials.TokenCredential
+    :param subscription_id: The ID of the target subscription.
+    :type subscription_id: str
+    :param base_url: Service URL. Default value is 'https://management.azure.com'.
+    :type base_url: str
+    :keyword api_version: Api Version. The default value is "2022-10-01". Note that overriding this
+     default value may result in unsupported behavior.
+    :paramtype api_version: str
+    :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+     Retry-After header is present.
+    """
+
+    def __init__(
+        self,
+        credential,  # type: "TokenCredential"
+        subscription_id,  # type: str
+        base_url="https://management.azure.com",  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        self._config = AzureMachineLearningWorkspacesConfiguration(credential=credential, subscription_id=subscription_id, **kwargs)
+        self._client = ARMPipelineClient(base_url=base_url, config=self._config, **kwargs)
+
+        client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}
+        self._serialize = Serializer(client_models)
+        self._deserialize = Deserializer(client_models)
+        self._serialize.client_side_validation = False
+        self.batch_endpoints = BatchEndpointsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.batch_deployments = BatchDeploymentsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.code_containers = CodeContainersOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.code_versions = CodeVersionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.component_containers = ComponentContainersOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.component_versions = ComponentVersionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.data_containers = DataContainersOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.data_versions = DataVersionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.datastores = DatastoresOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.environment_containers = EnvironmentContainersOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.environment_versions = EnvironmentVersionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.jobs = JobsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.model_containers = ModelContainersOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.model_versions = ModelVersionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.online_endpoints = OnlineEndpointsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.online_deployments = OnlineDeploymentsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.schedules = SchedulesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.operations = Operations(self._client, self._config, self._serialize, self._deserialize)
+        self.workspaces = WorkspacesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.usages = UsagesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.virtual_machine_sizes = VirtualMachineSizesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.quotas = QuotasOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.compute = ComputeOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.private_endpoint_connections = PrivateEndpointConnectionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.private_link_resources = PrivateLinkResourcesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.workspace_connections = WorkspaceConnectionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.workspace_features = WorkspaceFeaturesOperations(self._client, self._config, self._serialize, self._deserialize)
+
+
+    def _send_request(
+        self,
+        request,  # type: HttpRequest
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> HttpResponse
+        """Runs the network request through the client's chained policies.
+
+        >>> from azure.core.rest import HttpRequest
+        >>> request = HttpRequest("GET", "https://www.example.org/")
+        <HttpRequest [GET], url: 'https://www.example.org/'>
+        >>> response = client._send_request(request)
+        <HttpResponse: 200 OK>
+
+        For more information on this code flow, see https://aka.ms/azsdk/python/protocol/quickstart
+
+        :param request: The network request you want to make. Required.
+        :type request: ~azure.core.rest.HttpRequest
+        :keyword bool stream: Whether the response payload will be streamed. Defaults to False.
+        :return: The response of your network call. Does not do error handling on your response.
+        :rtype: ~azure.core.rest.HttpResponse
+        """
+
+        request_copy = deepcopy(request)
+        request_copy.url = self._client.format_url(request_copy.url)
+        return self._client.send_request(request_copy, **kwargs)
+
+    def close(self):
+        # type: () -> None
+        self._client.close()
+
+    def __enter__(self):
+        # type: () -> AzureMachineLearningWorkspaces
+        self._client.__enter__()
+        return self
+
+    def __exit__(self, *exc_details):
+        # type: (Any) -> None
+        self._client.__exit__(*exc_details)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_configuration.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_configuration.py
new file mode 100644
index 00000000..853a27ec
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_configuration.py
@@ -0,0 +1,76 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from typing import TYPE_CHECKING
+
+from azure.core.configuration import Configuration
+from azure.core.pipeline import policies
+from azure.mgmt.core.policies import ARMChallengeAuthenticationPolicy, ARMHttpLoggingPolicy
+
+from ._version import VERSION
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any
+
+    from azure.core.credentials import TokenCredential
+
+
+class AzureMachineLearningWorkspacesConfiguration(Configuration):  # pylint: disable=too-many-instance-attributes
+    """Configuration for AzureMachineLearningWorkspaces.
+
+    Note that all parameters used to create this instance are saved as instance
+    attributes.
+
+    :param credential: Credential needed for the client to connect to Azure.
+    :type credential: ~azure.core.credentials.TokenCredential
+    :param subscription_id: The ID of the target subscription.
+    :type subscription_id: str
+    :keyword api_version: Api Version. The default value is "2022-10-01". Note that overriding this
+     default value may result in unsupported behavior.
+    :paramtype api_version: str
+    """
+
+    def __init__(
+        self,
+        credential,  # type: "TokenCredential"
+        subscription_id,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        super(AzureMachineLearningWorkspacesConfiguration, self).__init__(**kwargs)
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        if credential is None:
+            raise ValueError("Parameter 'credential' must not be None.")
+        if subscription_id is None:
+            raise ValueError("Parameter 'subscription_id' must not be None.")
+
+        self.credential = credential
+        self.subscription_id = subscription_id
+        self.api_version = api_version
+        self.credential_scopes = kwargs.pop('credential_scopes', ['https://management.azure.com/.default'])
+        kwargs.setdefault('sdk_moniker', 'mgmt-machinelearningservices/{}'.format(VERSION))
+        self._configure(**kwargs)
+
+    def _configure(
+        self,
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs)
+        self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs)
+        self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs)
+        self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs)
+        self.http_logging_policy = kwargs.get('http_logging_policy') or ARMHttpLoggingPolicy(**kwargs)
+        self.retry_policy = kwargs.get('retry_policy') or policies.RetryPolicy(**kwargs)
+        self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs)
+        self.redirect_policy = kwargs.get('redirect_policy') or policies.RedirectPolicy(**kwargs)
+        self.authentication_policy = kwargs.get('authentication_policy')
+        if self.credential and not self.authentication_policy:
+            self.authentication_policy = ARMChallengeAuthenticationPolicy(self.credential, *self.credential_scopes, **kwargs)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_patch.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_patch.py
new file mode 100644
index 00000000..74e48ecd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_patch.py
@@ -0,0 +1,31 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+#
+# Copyright (c) Microsoft Corporation. All rights reserved.
+#
+# The MIT License (MIT)
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the ""Software""), to
+# deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+# sell copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+# IN THE SOFTWARE.
+#
+# --------------------------------------------------------------------------
+
+# This file is used for handwritten extensions to the generated code. Example:
+# https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md
+def patch_sdk():
+    pass
\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_vendor.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_vendor.py
new file mode 100644
index 00000000..138f663c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_vendor.py
@@ -0,0 +1,27 @@
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.core.pipeline.transport import HttpRequest
+
+def _convert_request(request, files=None):
+    data = request.content if not files else None
+    request = HttpRequest(method=request.method, url=request.url, headers=request.headers, data=data)
+    if files:
+        request.set_formdata_body(files)
+    return request
+
+def _format_url_section(template, **kwargs):
+    components = template.split("/")
+    while components:
+        try:
+            return template.format(**kwargs)
+        except KeyError as key:
+            formatted_components = template.split("/")
+            components = [
+                c for c in formatted_components if "{}".format(key.args[0]) not in c
+            ]
+            template = "/".join(components)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_version.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_version.py
new file mode 100644
index 00000000..eae7c95b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/_version.py
@@ -0,0 +1,9 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+VERSION = "0.1.0"
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/__init__.py
new file mode 100644
index 00000000..f67ccda9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/__init__.py
@@ -0,0 +1,15 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from ._azure_machine_learning_workspaces import AzureMachineLearningWorkspaces
+__all__ = ['AzureMachineLearningWorkspaces']
+
+# `._patch.py` is used for handwritten extensions to the generated code
+# Example: https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md
+from ._patch import patch_sdk
+patch_sdk()
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/_azure_machine_learning_workspaces.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/_azure_machine_learning_workspaces.py
new file mode 100644
index 00000000..dc532ff1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/_azure_machine_learning_workspaces.py
@@ -0,0 +1,191 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from copy import deepcopy
+from typing import Any, Awaitable, TYPE_CHECKING
+
+from msrest import Deserializer, Serializer
+
+from azure.core.rest import AsyncHttpResponse, HttpRequest
+from azure.mgmt.core import AsyncARMPipelineClient
+
+from .. import models
+from ._configuration import AzureMachineLearningWorkspacesConfiguration
+from .operations import BatchDeploymentsOperations, BatchEndpointsOperations, CodeContainersOperations, CodeVersionsOperations, ComponentContainersOperations, ComponentVersionsOperations, ComputeOperations, DataContainersOperations, DataVersionsOperations, DatastoresOperations, EnvironmentContainersOperations, EnvironmentVersionsOperations, JobsOperations, ModelContainersOperations, ModelVersionsOperations, OnlineDeploymentsOperations, OnlineEndpointsOperations, Operations, PrivateEndpointConnectionsOperations, PrivateLinkResourcesOperations, QuotasOperations, SchedulesOperations, UsagesOperations, VirtualMachineSizesOperations, WorkspaceConnectionsOperations, WorkspaceFeaturesOperations, WorkspacesOperations
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from azure.core.credentials_async import AsyncTokenCredential
+
+class AzureMachineLearningWorkspaces:    # pylint: disable=too-many-instance-attributes
+    """These APIs allow end users to operate on Azure Machine Learning Workspace resources.
+
+    :ivar batch_endpoints: BatchEndpointsOperations operations
+    :vartype batch_endpoints:
+     azure.mgmt.machinelearningservices.aio.operations.BatchEndpointsOperations
+    :ivar batch_deployments: BatchDeploymentsOperations operations
+    :vartype batch_deployments:
+     azure.mgmt.machinelearningservices.aio.operations.BatchDeploymentsOperations
+    :ivar code_containers: CodeContainersOperations operations
+    :vartype code_containers:
+     azure.mgmt.machinelearningservices.aio.operations.CodeContainersOperations
+    :ivar code_versions: CodeVersionsOperations operations
+    :vartype code_versions:
+     azure.mgmt.machinelearningservices.aio.operations.CodeVersionsOperations
+    :ivar component_containers: ComponentContainersOperations operations
+    :vartype component_containers:
+     azure.mgmt.machinelearningservices.aio.operations.ComponentContainersOperations
+    :ivar component_versions: ComponentVersionsOperations operations
+    :vartype component_versions:
+     azure.mgmt.machinelearningservices.aio.operations.ComponentVersionsOperations
+    :ivar data_containers: DataContainersOperations operations
+    :vartype data_containers:
+     azure.mgmt.machinelearningservices.aio.operations.DataContainersOperations
+    :ivar data_versions: DataVersionsOperations operations
+    :vartype data_versions:
+     azure.mgmt.machinelearningservices.aio.operations.DataVersionsOperations
+    :ivar datastores: DatastoresOperations operations
+    :vartype datastores: azure.mgmt.machinelearningservices.aio.operations.DatastoresOperations
+    :ivar environment_containers: EnvironmentContainersOperations operations
+    :vartype environment_containers:
+     azure.mgmt.machinelearningservices.aio.operations.EnvironmentContainersOperations
+    :ivar environment_versions: EnvironmentVersionsOperations operations
+    :vartype environment_versions:
+     azure.mgmt.machinelearningservices.aio.operations.EnvironmentVersionsOperations
+    :ivar jobs: JobsOperations operations
+    :vartype jobs: azure.mgmt.machinelearningservices.aio.operations.JobsOperations
+    :ivar model_containers: ModelContainersOperations operations
+    :vartype model_containers:
+     azure.mgmt.machinelearningservices.aio.operations.ModelContainersOperations
+    :ivar model_versions: ModelVersionsOperations operations
+    :vartype model_versions:
+     azure.mgmt.machinelearningservices.aio.operations.ModelVersionsOperations
+    :ivar online_endpoints: OnlineEndpointsOperations operations
+    :vartype online_endpoints:
+     azure.mgmt.machinelearningservices.aio.operations.OnlineEndpointsOperations
+    :ivar online_deployments: OnlineDeploymentsOperations operations
+    :vartype online_deployments:
+     azure.mgmt.machinelearningservices.aio.operations.OnlineDeploymentsOperations
+    :ivar schedules: SchedulesOperations operations
+    :vartype schedules: azure.mgmt.machinelearningservices.aio.operations.SchedulesOperations
+    :ivar operations: Operations operations
+    :vartype operations: azure.mgmt.machinelearningservices.aio.operations.Operations
+    :ivar workspaces: WorkspacesOperations operations
+    :vartype workspaces: azure.mgmt.machinelearningservices.aio.operations.WorkspacesOperations
+    :ivar usages: UsagesOperations operations
+    :vartype usages: azure.mgmt.machinelearningservices.aio.operations.UsagesOperations
+    :ivar virtual_machine_sizes: VirtualMachineSizesOperations operations
+    :vartype virtual_machine_sizes:
+     azure.mgmt.machinelearningservices.aio.operations.VirtualMachineSizesOperations
+    :ivar quotas: QuotasOperations operations
+    :vartype quotas: azure.mgmt.machinelearningservices.aio.operations.QuotasOperations
+    :ivar compute: ComputeOperations operations
+    :vartype compute: azure.mgmt.machinelearningservices.aio.operations.ComputeOperations
+    :ivar private_endpoint_connections: PrivateEndpointConnectionsOperations operations
+    :vartype private_endpoint_connections:
+     azure.mgmt.machinelearningservices.aio.operations.PrivateEndpointConnectionsOperations
+    :ivar private_link_resources: PrivateLinkResourcesOperations operations
+    :vartype private_link_resources:
+     azure.mgmt.machinelearningservices.aio.operations.PrivateLinkResourcesOperations
+    :ivar workspace_connections: WorkspaceConnectionsOperations operations
+    :vartype workspace_connections:
+     azure.mgmt.machinelearningservices.aio.operations.WorkspaceConnectionsOperations
+    :ivar workspace_features: WorkspaceFeaturesOperations operations
+    :vartype workspace_features:
+     azure.mgmt.machinelearningservices.aio.operations.WorkspaceFeaturesOperations
+    :param credential: Credential needed for the client to connect to Azure.
+    :type credential: ~azure.core.credentials_async.AsyncTokenCredential
+    :param subscription_id: The ID of the target subscription.
+    :type subscription_id: str
+    :param base_url: Service URL. Default value is 'https://management.azure.com'.
+    :type base_url: str
+    :keyword api_version: Api Version. The default value is "2022-10-01". Note that overriding this
+     default value may result in unsupported behavior.
+    :paramtype api_version: str
+    :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+     Retry-After header is present.
+    """
+
+    def __init__(
+        self,
+        credential: "AsyncTokenCredential",
+        subscription_id: str,
+        base_url: str = "https://management.azure.com",
+        **kwargs: Any
+    ) -> None:
+        self._config = AzureMachineLearningWorkspacesConfiguration(credential=credential, subscription_id=subscription_id, **kwargs)
+        self._client = AsyncARMPipelineClient(base_url=base_url, config=self._config, **kwargs)
+
+        client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}
+        self._serialize = Serializer(client_models)
+        self._deserialize = Deserializer(client_models)
+        self._serialize.client_side_validation = False
+        self.batch_endpoints = BatchEndpointsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.batch_deployments = BatchDeploymentsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.code_containers = CodeContainersOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.code_versions = CodeVersionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.component_containers = ComponentContainersOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.component_versions = ComponentVersionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.data_containers = DataContainersOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.data_versions = DataVersionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.datastores = DatastoresOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.environment_containers = EnvironmentContainersOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.environment_versions = EnvironmentVersionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.jobs = JobsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.model_containers = ModelContainersOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.model_versions = ModelVersionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.online_endpoints = OnlineEndpointsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.online_deployments = OnlineDeploymentsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.schedules = SchedulesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.operations = Operations(self._client, self._config, self._serialize, self._deserialize)
+        self.workspaces = WorkspacesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.usages = UsagesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.virtual_machine_sizes = VirtualMachineSizesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.quotas = QuotasOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.compute = ComputeOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.private_endpoint_connections = PrivateEndpointConnectionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.private_link_resources = PrivateLinkResourcesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.workspace_connections = WorkspaceConnectionsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.workspace_features = WorkspaceFeaturesOperations(self._client, self._config, self._serialize, self._deserialize)
+
+
+    def _send_request(
+        self,
+        request: HttpRequest,
+        **kwargs: Any
+    ) -> Awaitable[AsyncHttpResponse]:
+        """Runs the network request through the client's chained policies.
+
+        >>> from azure.core.rest import HttpRequest
+        >>> request = HttpRequest("GET", "https://www.example.org/")
+        <HttpRequest [GET], url: 'https://www.example.org/'>
+        >>> response = await client._send_request(request)
+        <AsyncHttpResponse: 200 OK>
+
+        For more information on this code flow, see https://aka.ms/azsdk/python/protocol/quickstart
+
+        :param request: The network request you want to make. Required.
+        :type request: ~azure.core.rest.HttpRequest
+        :keyword bool stream: Whether the response payload will be streamed. Defaults to False.
+        :return: The response of your network call. Does not do error handling on your response.
+        :rtype: ~azure.core.rest.AsyncHttpResponse
+        """
+
+        request_copy = deepcopy(request)
+        request_copy.url = self._client.format_url(request_copy.url)
+        return self._client.send_request(request_copy, **kwargs)
+
+    async def close(self) -> None:
+        await self._client.close()
+
+    async def __aenter__(self) -> "AzureMachineLearningWorkspaces":
+        await self._client.__aenter__()
+        return self
+
+    async def __aexit__(self, *exc_details) -> None:
+        await self._client.__aexit__(*exc_details)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/_configuration.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/_configuration.py
new file mode 100644
index 00000000..aaac6cd4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/_configuration.py
@@ -0,0 +1,72 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from typing import Any, TYPE_CHECKING
+
+from azure.core.configuration import Configuration
+from azure.core.pipeline import policies
+from azure.mgmt.core.policies import ARMHttpLoggingPolicy, AsyncARMChallengeAuthenticationPolicy
+
+from .._version import VERSION
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from azure.core.credentials_async import AsyncTokenCredential
+
+
+class AzureMachineLearningWorkspacesConfiguration(Configuration):  # pylint: disable=too-many-instance-attributes
+    """Configuration for AzureMachineLearningWorkspaces.
+
+    Note that all parameters used to create this instance are saved as instance
+    attributes.
+
+    :param credential: Credential needed for the client to connect to Azure.
+    :type credential: ~azure.core.credentials_async.AsyncTokenCredential
+    :param subscription_id: The ID of the target subscription.
+    :type subscription_id: str
+    :keyword api_version: Api Version. The default value is "2022-10-01". Note that overriding this
+     default value may result in unsupported behavior.
+    :paramtype api_version: str
+    """
+
+    def __init__(
+        self,
+        credential: "AsyncTokenCredential",
+        subscription_id: str,
+        **kwargs: Any
+    ) -> None:
+        super(AzureMachineLearningWorkspacesConfiguration, self).__init__(**kwargs)
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        if credential is None:
+            raise ValueError("Parameter 'credential' must not be None.")
+        if subscription_id is None:
+            raise ValueError("Parameter 'subscription_id' must not be None.")
+
+        self.credential = credential
+        self.subscription_id = subscription_id
+        self.api_version = api_version
+        self.credential_scopes = kwargs.pop('credential_scopes', ['https://management.azure.com/.default'])
+        kwargs.setdefault('sdk_moniker', 'mgmt-machinelearningservices/{}'.format(VERSION))
+        self._configure(**kwargs)
+
+    def _configure(
+        self,
+        **kwargs: Any
+    ) -> None:
+        self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs)
+        self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs)
+        self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs)
+        self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs)
+        self.http_logging_policy = kwargs.get('http_logging_policy') or ARMHttpLoggingPolicy(**kwargs)
+        self.retry_policy = kwargs.get('retry_policy') or policies.AsyncRetryPolicy(**kwargs)
+        self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs)
+        self.redirect_policy = kwargs.get('redirect_policy') or policies.AsyncRedirectPolicy(**kwargs)
+        self.authentication_policy = kwargs.get('authentication_policy')
+        if self.credential and not self.authentication_policy:
+            self.authentication_policy = AsyncARMChallengeAuthenticationPolicy(self.credential, *self.credential_scopes, **kwargs)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/_patch.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/_patch.py
new file mode 100644
index 00000000..74e48ecd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/_patch.py
@@ -0,0 +1,31 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+#
+# Copyright (c) Microsoft Corporation. All rights reserved.
+#
+# The MIT License (MIT)
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the ""Software""), to
+# deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+# sell copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+# IN THE SOFTWARE.
+#
+# --------------------------------------------------------------------------
+
+# This file is used for handwritten extensions to the generated code. Example:
+# https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md
+def patch_sdk():
+    pass
\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/__init__.py
new file mode 100644
index 00000000..96b72908
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/__init__.py
@@ -0,0 +1,65 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from ._batch_endpoints_operations import BatchEndpointsOperations
+from ._batch_deployments_operations import BatchDeploymentsOperations
+from ._code_containers_operations import CodeContainersOperations
+from ._code_versions_operations import CodeVersionsOperations
+from ._component_containers_operations import ComponentContainersOperations
+from ._component_versions_operations import ComponentVersionsOperations
+from ._data_containers_operations import DataContainersOperations
+from ._data_versions_operations import DataVersionsOperations
+from ._datastores_operations import DatastoresOperations
+from ._environment_containers_operations import EnvironmentContainersOperations
+from ._environment_versions_operations import EnvironmentVersionsOperations
+from ._jobs_operations import JobsOperations
+from ._model_containers_operations import ModelContainersOperations
+from ._model_versions_operations import ModelVersionsOperations
+from ._online_endpoints_operations import OnlineEndpointsOperations
+from ._online_deployments_operations import OnlineDeploymentsOperations
+from ._schedules_operations import SchedulesOperations
+from ._operations import Operations
+from ._workspaces_operations import WorkspacesOperations
+from ._usages_operations import UsagesOperations
+from ._virtual_machine_sizes_operations import VirtualMachineSizesOperations
+from ._quotas_operations import QuotasOperations
+from ._compute_operations import ComputeOperations
+from ._private_endpoint_connections_operations import PrivateEndpointConnectionsOperations
+from ._private_link_resources_operations import PrivateLinkResourcesOperations
+from ._workspace_connections_operations import WorkspaceConnectionsOperations
+from ._workspace_features_operations import WorkspaceFeaturesOperations
+
+__all__ = [
+    'BatchEndpointsOperations',
+    'BatchDeploymentsOperations',
+    'CodeContainersOperations',
+    'CodeVersionsOperations',
+    'ComponentContainersOperations',
+    'ComponentVersionsOperations',
+    'DataContainersOperations',
+    'DataVersionsOperations',
+    'DatastoresOperations',
+    'EnvironmentContainersOperations',
+    'EnvironmentVersionsOperations',
+    'JobsOperations',
+    'ModelContainersOperations',
+    'ModelVersionsOperations',
+    'OnlineEndpointsOperations',
+    'OnlineDeploymentsOperations',
+    'SchedulesOperations',
+    'Operations',
+    'WorkspacesOperations',
+    'UsagesOperations',
+    'VirtualMachineSizesOperations',
+    'QuotasOperations',
+    'ComputeOperations',
+    'PrivateEndpointConnectionsOperations',
+    'PrivateLinkResourcesOperations',
+    'WorkspaceConnectionsOperations',
+    'WorkspaceFeaturesOperations',
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_batch_deployments_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_batch_deployments_operations.py
new file mode 100644
index 00000000..08103260
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_batch_deployments_operations.py
@@ -0,0 +1,642 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._batch_deployments_operations import build_create_or_update_request_initial, build_delete_request_initial, build_get_request, build_list_request, build_update_request_initial
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class BatchDeploymentsOperations:
+    """BatchDeploymentsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.BatchDeploymentTrackedResourceArmPaginatedResult"]:
+        """Lists Batch inference deployments in the workspace.
+
+        Lists Batch inference deployments in the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Endpoint name.
+        :type endpoint_name: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Top of list.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either BatchDeploymentTrackedResourceArmPaginatedResult
+         or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.BatchDeploymentTrackedResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDeploymentTrackedResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("BatchDeploymentTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments"}  # type: ignore
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete Batch Inference deployment (asynchronous).
+
+        Delete Batch Inference deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference deployment identifier.
+        :type deployment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        **kwargs: Any
+    ) -> "_models.BatchDeployment":
+        """Gets a batch inference deployment by id.
+
+        Gets a batch inference deployment by id.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: The identifier for the Batch deployments.
+        :type deployment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: BatchDeployment, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.BatchDeployment
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDeployment"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('BatchDeployment', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    async def _update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        body: "_models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties",
+        **kwargs: Any
+    ) -> Optional["_models.BatchDeployment"]:
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.BatchDeployment"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('BatchDeployment', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        body: "_models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.BatchDeployment"]:
+        """Update a batch inference deployment (asynchronous).
+
+        Update a batch inference deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: The identifier for the Batch inference deployment.
+        :type deployment_name: str
+        :param body: Batch inference deployment definition object.
+        :type body:
+         ~azure.mgmt.machinelearningservices.models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either BatchDeployment or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.BatchDeployment]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDeployment"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('BatchDeployment', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        body: "_models.BatchDeployment",
+        **kwargs: Any
+    ) -> "_models.BatchDeployment":
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDeployment"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'BatchDeployment')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('BatchDeployment', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('BatchDeployment', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        body: "_models.BatchDeployment",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.BatchDeployment"]:
+        """Creates/updates a batch inference deployment (asynchronous).
+
+        Creates/updates a batch inference deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: The identifier for the Batch inference deployment.
+        :type deployment_name: str
+        :param body: Batch inference deployment definition object.
+        :type body: ~azure.mgmt.machinelearningservices.models.BatchDeployment
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either BatchDeployment or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.BatchDeployment]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDeployment"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('BatchDeployment', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_batch_endpoints_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_batch_endpoints_operations.py
new file mode 100644
index 00000000..6c944b29
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_batch_endpoints_operations.py
@@ -0,0 +1,675 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._batch_endpoints_operations import build_create_or_update_request_initial, build_delete_request_initial, build_get_request, build_list_keys_request, build_list_request, build_update_request_initial
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class BatchEndpointsOperations:
+    """BatchEndpointsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        count: Optional[int] = None,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.BatchEndpointTrackedResourceArmPaginatedResult"]:
+        """Lists Batch inference endpoint in the workspace.
+
+        Lists Batch inference endpoint in the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param count: Number of endpoints to be retrieved in a page of results.
+        :type count: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either BatchEndpointTrackedResourceArmPaginatedResult or
+         the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.BatchEndpointTrackedResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEndpointTrackedResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    count=count,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    count=count,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("BatchEndpointTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints"}  # type: ignore
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete Batch Inference Endpoint (asynchronous).
+
+        Delete Batch Inference Endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        **kwargs: Any
+    ) -> "_models.BatchEndpoint":
+        """Gets a batch inference endpoint by name.
+
+        Gets a batch inference endpoint by name.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Name for the Batch Endpoint.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: BatchEndpoint, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.BatchEndpoint
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEndpoint"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+
+    async def _update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        body: "_models.PartialMinimalTrackedResourceWithIdentity",
+        **kwargs: Any
+    ) -> Optional["_models.BatchEndpoint"]:
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.BatchEndpoint"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'PartialMinimalTrackedResourceWithIdentity')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        body: "_models.PartialMinimalTrackedResourceWithIdentity",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.BatchEndpoint"]:
+        """Update a batch inference endpoint (asynchronous).
+
+        Update a batch inference endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Name for the Batch inference endpoint.
+        :type endpoint_name: str
+        :param body: Mutable batch inference endpoint definition object.
+        :type body:
+         ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithIdentity
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either BatchEndpoint or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.BatchEndpoint]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEndpoint"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        body: "_models.BatchEndpoint",
+        **kwargs: Any
+    ) -> "_models.BatchEndpoint":
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEndpoint"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'BatchEndpoint')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        body: "_models.BatchEndpoint",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.BatchEndpoint"]:
+        """Creates a batch inference endpoint (asynchronous).
+
+        Creates a batch inference endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Name for the Batch inference endpoint.
+        :type endpoint_name: str
+        :param body: Batch inference endpoint definition object.
+        :type body: ~azure.mgmt.machinelearningservices.models.BatchEndpoint
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either BatchEndpoint or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.BatchEndpoint]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEndpoint"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+    @distributed_trace_async
+    async def list_keys(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        **kwargs: Any
+    ) -> "_models.EndpointAuthKeys":
+        """Lists batch Inference Endpoint keys.
+
+        Lists batch Inference Endpoint keys.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EndpointAuthKeys, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EndpointAuthKeys"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self.list_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('EndpointAuthKeys', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/listkeys"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_code_containers_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_code_containers_operations.py
new file mode 100644
index 00000000..9936d55e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_code_containers_operations.py
@@ -0,0 +1,339 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._code_containers_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class CodeContainersOperations:
+    """CodeContainersOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.CodeContainerResourceArmPaginatedResult"]:
+        """List containers.
+
+        List containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either CodeContainerResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.CodeContainerResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeContainerResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("CodeContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> "_models.CodeContainer":
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('CodeContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        body: "_models.CodeContainer",
+        **kwargs: Any
+    ) -> "_models.CodeContainer":
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param body: Container entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'CodeContainer')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('CodeContainer', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('CodeContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_code_versions_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_code_versions_operations.py
new file mode 100644
index 00000000..eb13b0c6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_code_versions_operations.py
@@ -0,0 +1,366 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._code_versions_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class CodeVersionsOperations:
+    """CodeVersionsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.CodeVersionResourceArmPaginatedResult"]:
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Maximum number of records to return.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either CodeVersionResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.CodeVersionResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeVersionResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("CodeVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> "_models.CodeVersion":
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('CodeVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        body: "_models.CodeVersion",
+        **kwargs: Any
+    ) -> "_models.CodeVersion":
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :param body: Version entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeVersion
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'CodeVersion')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('CodeVersion', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('CodeVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions/{version}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_component_containers_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_component_containers_operations.py
new file mode 100644
index 00000000..d685edaf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_component_containers_operations.py
@@ -0,0 +1,344 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._component_containers_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class ComponentContainersOperations:
+    """ComponentContainersOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, "_models.ListViewType"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ComponentContainerResourceArmPaginatedResult"]:
+        """List component containers.
+
+        List component containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ComponentContainerResourceArmPaginatedResult or
+         the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ComponentContainerResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentContainerResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ComponentContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> "_models.ComponentContainer":
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ComponentContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        body: "_models.ComponentContainer",
+        **kwargs: Any
+    ) -> "_models.ComponentContainer":
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param body: Container entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'ComponentContainer')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('ComponentContainer', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('ComponentContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_component_versions_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_component_versions_operations.py
new file mode 100644
index 00000000..488b2f9b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_component_versions_operations.py
@@ -0,0 +1,371 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._component_versions_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class ComponentVersionsOperations:
+    """ComponentVersionsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, "_models.ListViewType"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ComponentVersionResourceArmPaginatedResult"]:
+        """List component versions.
+
+        List component versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Component name.
+        :type name: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Maximum number of records to return.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ComponentVersionResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ComponentVersionResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentVersionResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ComponentVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> "_models.ComponentVersion":
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ComponentVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        body: "_models.ComponentVersion",
+        **kwargs: Any
+    ) -> "_models.ComponentVersion":
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :param body: Version entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentVersion
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'ComponentVersion')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('ComponentVersion', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('ComponentVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions/{version}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_compute_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_compute_operations.py
new file mode 100644
index 00000000..450657b6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_compute_operations.py
@@ -0,0 +1,1077 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._compute_operations import build_create_or_update_request_initial, build_delete_request_initial, build_get_request, build_list_keys_request, build_list_nodes_request, build_list_request, build_restart_request_initial, build_start_request_initial, build_stop_request_initial, build_update_request_initial
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class ComputeOperations:
+    """ComputeOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PaginatedComputeResourcesList"]:
+        """Gets computes in specified workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PaginatedComputeResourcesList or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedComputeResourcesList]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedComputeResourcesList"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("PaginatedComputeResourcesList", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes"}  # type: ignore
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        **kwargs: Any
+    ) -> "_models.ComputeResource":
+        """Gets compute definition by its name. Any secrets (storage keys, service credentials, etc) are
+        not returned - use 'keys' nested resource to get them.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComputeResource, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComputeResource
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ComputeResource', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        parameters: "_models.ComputeResource",
+        **kwargs: Any
+    ) -> "_models.ComputeResource":
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'ComputeResource')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('ComputeResource', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('ComputeResource', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        parameters: "_models.ComputeResource",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.ComputeResource"]:
+        """Creates or updates compute. This call will overwrite a compute if it exists. This is a
+        nonrecoverable operation. If your intent is to create a new compute, do a GET first to verify
+        that it does not exist yet.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :param parameters: Payload with Machine Learning compute definition.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.ComputeResource
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ComputeResource or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ComputeResource]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                parameters=parameters,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('ComputeResource', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+    async def _update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        parameters: "_models.ClusterUpdateParameters",
+        **kwargs: Any
+    ) -> "_models.ComputeResource":
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'ClusterUpdateParameters')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ComputeResource', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        parameters: "_models.ClusterUpdateParameters",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.ComputeResource"]:
+        """Updates properties of a compute. This call will overwrite a compute if it exists. This is a
+        nonrecoverable operation.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :param parameters: Additional parameters for cluster update.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.ClusterUpdateParameters
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ComputeResource or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ComputeResource]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                parameters=parameters,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('ComputeResource', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        underlying_resource_action: Union[str, "_models.UnderlyingResourceAction"],
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            underlying_resource_action=underlying_resource_action,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        underlying_resource_action: Union[str, "_models.UnderlyingResourceAction"],
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Deletes specified Machine Learning compute.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :param underlying_resource_action: Delete the underlying compute if 'Delete', or detach the
+         underlying compute from workspace if 'Detach'.
+        :type underlying_resource_action: str or
+         ~azure.mgmt.machinelearningservices.models.UnderlyingResourceAction
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                underlying_resource_action=underlying_resource_action,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+    @distributed_trace
+    def list_nodes(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.AmlComputeNodesInformation"]:
+        """Get the details (e.g IP address, port etc) of all the compute nodes in the compute.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either AmlComputeNodesInformation or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.AmlComputeNodesInformation]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.AmlComputeNodesInformation"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_nodes_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    compute_name=compute_name,
+                    api_version=api_version,
+                    template_url=self.list_nodes.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_nodes_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    compute_name=compute_name,
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("AmlComputeNodesInformation", pipeline_response)
+            list_of_elem = deserialized.nodes
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list_nodes.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listNodes"}  # type: ignore
+
+    @distributed_trace_async
+    async def list_keys(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        **kwargs: Any
+    ) -> "_models.ComputeSecrets":
+        """Gets secrets related to Machine Learning compute (storage keys, service credentials, etc).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComputeSecrets, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComputeSecrets
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeSecrets"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            template_url=self.list_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ComputeSecrets', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listKeys"}  # type: ignore
+
+
+    async def _start_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_start_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            template_url=self._start_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    _start_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/start"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_start(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Posts a start action to a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._start_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_start.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/start"}  # type: ignore
+
+    async def _stop_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_stop_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            template_url=self._stop_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    _stop_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/stop"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_stop(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Posts a stop action to a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._stop_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_stop.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/stop"}  # type: ignore
+
+    async def _restart_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_restart_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            template_url=self._restart_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    _restart_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/restart"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_restart(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Posts a restart action to a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._restart_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_restart.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/restart"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_data_containers_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_data_containers_operations.py
new file mode 100644
index 00000000..f28716d9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_data_containers_operations.py
@@ -0,0 +1,344 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._data_containers_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class DataContainersOperations:
+    """DataContainersOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, "_models.ListViewType"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.DataContainerResourceArmPaginatedResult"]:
+        """List data containers.
+
+        List data containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either DataContainerResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.DataContainerResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainerResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("DataContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> "_models.DataContainer":
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('DataContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        body: "_models.DataContainer",
+        **kwargs: Any
+    ) -> "_models.DataContainer":
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param body: Container entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.DataContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'DataContainer')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('DataContainer', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('DataContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_data_versions_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_data_versions_operations.py
new file mode 100644
index 00000000..a231f467
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_data_versions_operations.py
@@ -0,0 +1,380 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._data_versions_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class DataVersionsOperations:
+    """DataVersionsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        tags: Optional[str] = None,
+        list_view_type: Optional[Union[str, "_models.ListViewType"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.DataVersionBaseResourceArmPaginatedResult"]:
+        """List data versions in the data container.
+
+        List data versions in the data container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Data container's name.
+        :type name: str
+        :param order_by: Please choose OrderBy value from ['createdtime', 'modifiedtime'].
+        :type order_by: str
+        :param top: Top count of results, top count cannot be greater than the page size.
+                                       If topCount > page size, results with be default page size count
+         will be returned.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param tags: Comma-separated list of tag names (and optionally values). Example:
+         tag1,tag2=value2.
+        :type tags: str
+        :param list_view_type: [ListViewType.ActiveOnly, ListViewType.ArchivedOnly,
+         ListViewType.All]View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either DataVersionBaseResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.DataVersionBaseResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataVersionBaseResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    tags=tags,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    tags=tags,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("DataVersionBaseResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> "_models.DataVersionBase":
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataVersionBase, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionBase
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataVersionBase"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('DataVersionBase', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        body: "_models.DataVersionBase",
+        **kwargs: Any
+    ) -> "_models.DataVersionBase":
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :param body: Version entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.DataVersionBase
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataVersionBase, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionBase
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataVersionBase"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'DataVersionBase')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('DataVersionBase', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('DataVersionBase', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions/{version}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_datastores_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_datastores_operations.py
new file mode 100644
index 00000000..7e539cf1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_datastores_operations.py
@@ -0,0 +1,438 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, List, Optional, TypeVar
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._datastores_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request, build_list_secrets_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class DatastoresOperations:
+    """DatastoresOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        count: Optional[int] = 30,
+        is_default: Optional[bool] = None,
+        names: Optional[List[str]] = None,
+        search_text: Optional[str] = None,
+        order_by: Optional[str] = None,
+        order_by_asc: Optional[bool] = False,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.DatastoreResourceArmPaginatedResult"]:
+        """List datastores.
+
+        List datastores.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param count: Maximum number of results to return.
+        :type count: int
+        :param is_default: Filter down to the workspace default datastore.
+        :type is_default: bool
+        :param names: Names of datastores to return.
+        :type names: list[str]
+        :param search_text: Text to search for in the datastore names.
+        :type search_text: str
+        :param order_by: Order by property (createdtime | modifiedtime | name).
+        :type order_by: str
+        :param order_by_asc: Order by property in ascending order.
+        :type order_by_asc: bool
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either DatastoreResourceArmPaginatedResult or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.DatastoreResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatastoreResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    count=count,
+                    is_default=is_default,
+                    names=names,
+                    search_text=search_text,
+                    order_by=order_by,
+                    order_by_asc=order_by_asc,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    count=count,
+                    is_default=is_default,
+                    names=names,
+                    search_text=search_text,
+                    order_by=order_by,
+                    order_by_asc=order_by_asc,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("DatastoreResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete datastore.
+
+        Delete datastore.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Datastore name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> "_models.Datastore":
+        """Get datastore.
+
+        Get datastore.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Datastore name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Datastore, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Datastore
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Datastore"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('Datastore', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        body: "_models.Datastore",
+        skip_validation: Optional[bool] = False,
+        **kwargs: Any
+    ) -> "_models.Datastore":
+        """Create or update datastore.
+
+        Create or update datastore.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Datastore name.
+        :type name: str
+        :param body: Datastore entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.Datastore
+        :param skip_validation: Flag to skip validation.
+        :type skip_validation: bool
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Datastore, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Datastore
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Datastore"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'Datastore')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            skip_validation=skip_validation,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('Datastore', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('Datastore', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def list_secrets(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> "_models.DatastoreSecrets":
+        """Get datastore secrets.
+
+        Get datastore secrets.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Datastore name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DatastoreSecrets, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DatastoreSecrets
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatastoreSecrets"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_secrets_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.list_secrets.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('DatastoreSecrets', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_secrets.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}/listSecrets"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_environment_containers_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_environment_containers_operations.py
new file mode 100644
index 00000000..ff98446f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_environment_containers_operations.py
@@ -0,0 +1,344 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._environment_containers_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class EnvironmentContainersOperations:
+    """EnvironmentContainersOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, "_models.ListViewType"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.EnvironmentContainerResourceArmPaginatedResult"]:
+        """List environment containers.
+
+        List environment containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either EnvironmentContainerResourceArmPaginatedResult or
+         the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.EnvironmentContainerResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentContainerResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("EnvironmentContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> "_models.EnvironmentContainer":
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('EnvironmentContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        body: "_models.EnvironmentContainer",
+        **kwargs: Any
+    ) -> "_models.EnvironmentContainer":
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param body: Container entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'EnvironmentContainer')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('EnvironmentContainer', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('EnvironmentContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_environment_versions_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_environment_versions_operations.py
new file mode 100644
index 00000000..93a89dcc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_environment_versions_operations.py
@@ -0,0 +1,371 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._environment_versions_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class EnvironmentVersionsOperations:
+    """EnvironmentVersionsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, "_models.ListViewType"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.EnvironmentVersionResourceArmPaginatedResult"]:
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Maximum number of records to return.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either EnvironmentVersionResourceArmPaginatedResult or
+         the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.EnvironmentVersionResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentVersionResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("EnvironmentVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> "_models.EnvironmentVersion":
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('EnvironmentVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        body: "_models.EnvironmentVersion",
+        **kwargs: Any
+    ) -> "_models.EnvironmentVersion":
+        """Creates or updates an EnvironmentVersion.
+
+        Creates or updates an EnvironmentVersion.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Name of EnvironmentVersion. This is case-sensitive.
+        :type name: str
+        :param version: Version of EnvironmentVersion.
+        :type version: str
+        :param body: Definition of EnvironmentVersion.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'EnvironmentVersion')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('EnvironmentVersion', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('EnvironmentVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions/{version}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_jobs_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_jobs_operations.py
new file mode 100644
index 00000000..699989cb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_jobs_operations.py
@@ -0,0 +1,532 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._jobs_operations import build_cancel_request_initial, build_create_or_update_request, build_delete_request_initial, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class JobsOperations:
+    """JobsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        job_type: Optional[str] = None,
+        tag: Optional[str] = None,
+        list_view_type: Optional[Union[str, "_models.ListViewType"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.JobBaseResourceArmPaginatedResult"]:
+        """Lists Jobs in the workspace.
+
+        Lists Jobs in the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param job_type: Type of job to be returned.
+        :type job_type: str
+        :param tag: Jobs returned will have this tag key.
+        :type tag: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either JobBaseResourceArmPaginatedResult or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.JobBaseResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.JobBaseResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    job_type=job_type,
+                    tag=tag,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    job_type=job_type,
+                    tag=tag,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("JobBaseResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs"}  # type: ignore
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Deletes a Job (asynchronous).
+
+        Deletes a Job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param id: The name and identifier for the Job. This is case-sensitive.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                id=id,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}"}  # type: ignore
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        **kwargs: Any
+    ) -> "_models.JobBase":
+        """Gets a Job by name/id.
+
+        Gets a Job by name/id.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param id: The name and identifier for the Job. This is case-sensitive.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: JobBase, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.JobBase
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.JobBase"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('JobBase', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: "_models.JobBase",
+        **kwargs: Any
+    ) -> "_models.JobBase":
+        """Creates and executes a Job.
+
+        Creates and executes a Job.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param id: The name and identifier for the Job. This is case-sensitive.
+        :type id: str
+        :param body: Job definition object.
+        :type body: ~azure.mgmt.machinelearningservices.models.JobBase
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: JobBase, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.JobBase
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.JobBase"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'JobBase')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('JobBase', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('JobBase', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}"}  # type: ignore
+
+
+    async def _cancel_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_cancel_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            api_version=api_version,
+            template_url=self._cancel_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _cancel_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}/cancel"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_cancel(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Cancels a Job (asynchronous).
+
+        Cancels a Job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param id: The name and identifier for the Job. This is case-sensitive.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._cancel_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                id=id,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_cancel.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}/cancel"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_model_containers_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_model_containers_operations.py
new file mode 100644
index 00000000..38e3c197
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_model_containers_operations.py
@@ -0,0 +1,349 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._model_containers_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class ModelContainersOperations:
+    """ModelContainersOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        count: Optional[int] = None,
+        list_view_type: Optional[Union[str, "_models.ListViewType"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ModelContainerResourceArmPaginatedResult"]:
+        """List model containers.
+
+        List model containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param count: Maximum number of results to return.
+        :type count: int
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ModelContainerResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ModelContainerResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelContainerResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    count=count,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    count=count,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ModelContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> "_models.ModelContainer":
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ModelContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        body: "_models.ModelContainer",
+        **kwargs: Any
+    ) -> "_models.ModelContainer":
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param body: Container entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'ModelContainer')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('ModelContainer', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('ModelContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_model_versions_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_model_versions_operations.py
new file mode 100644
index 00000000..5b2084f2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_model_versions_operations.py
@@ -0,0 +1,403 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._model_versions_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class ModelVersionsOperations:
+    """ModelVersionsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        skip: Optional[str] = None,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        version: Optional[str] = None,
+        description: Optional[str] = None,
+        offset: Optional[int] = None,
+        tags: Optional[str] = None,
+        properties: Optional[str] = None,
+        feed: Optional[str] = None,
+        list_view_type: Optional[Union[str, "_models.ListViewType"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ModelVersionResourceArmPaginatedResult"]:
+        """List model versions.
+
+        List model versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Model name. This is case-sensitive.
+        :type name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Maximum number of records to return.
+        :type top: int
+        :param version: Model version.
+        :type version: str
+        :param description: Model description.
+        :type description: str
+        :param offset: Number of initial results to skip.
+        :type offset: int
+        :param tags: Comma-separated list of tag names (and optionally values). Example:
+         tag1,tag2=value2.
+        :type tags: str
+        :param properties: Comma-separated list of property names (and optionally values). Example:
+         prop1,prop2=value2.
+        :type properties: str
+        :param feed: Name of the feed.
+        :type feed: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ModelVersionResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ModelVersionResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelVersionResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    skip=skip,
+                    order_by=order_by,
+                    top=top,
+                    version=version,
+                    description=description,
+                    offset=offset,
+                    tags=tags,
+                    properties=properties,
+                    feed=feed,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    skip=skip,
+                    order_by=order_by,
+                    top=top,
+                    version=version,
+                    description=description,
+                    offset=offset,
+                    tags=tags,
+                    properties=properties,
+                    feed=feed,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ModelVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions"}  # type: ignore
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> "_models.ModelVersion":
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ModelVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        body: "_models.ModelVersion",
+        **kwargs: Any
+    ) -> "_models.ModelVersion":
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :param body: Version entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelVersion
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'ModelVersion')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('ModelVersion', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('ModelVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions/{version}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_online_deployments_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_online_deployments_operations.py
new file mode 100644
index 00000000..96e90855
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_online_deployments_operations.py
@@ -0,0 +1,823 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._online_deployments_operations import build_create_or_update_request_initial, build_delete_request_initial, build_get_logs_request, build_get_request, build_list_request, build_list_skus_request, build_update_request_initial
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class OnlineDeploymentsOperations:
+    """OnlineDeploymentsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.OnlineDeploymentTrackedResourceArmPaginatedResult"]:
+        """List Inference Endpoint Deployments.
+
+        List Inference Endpoint Deployments.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Top of list.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either OnlineDeploymentTrackedResourceArmPaginatedResult
+         or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.OnlineDeploymentTrackedResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineDeploymentTrackedResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("OnlineDeploymentTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments"}  # type: ignore
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete Inference Endpoint Deployment (asynchronous).
+
+        Delete Inference Endpoint Deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference Endpoint Deployment name.
+        :type deployment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        **kwargs: Any
+    ) -> "_models.OnlineDeployment":
+        """Get Inference Deployment Deployment.
+
+        Get Inference Deployment Deployment.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference Endpoint Deployment name.
+        :type deployment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: OnlineDeployment, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.OnlineDeployment
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineDeployment"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    async def _update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        body: "_models.PartialMinimalTrackedResourceWithSku",
+        **kwargs: Any
+    ) -> Optional["_models.OnlineDeployment"]:
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.OnlineDeployment"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'PartialMinimalTrackedResourceWithSku')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        body: "_models.PartialMinimalTrackedResourceWithSku",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.OnlineDeployment"]:
+        """Update Online Deployment (asynchronous).
+
+        Update Online Deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference Endpoint Deployment name.
+        :type deployment_name: str
+        :param body: Online Endpoint entity to apply during operation.
+        :type body: ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithSku
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either OnlineDeployment or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.OnlineDeployment]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineDeployment"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        body: "_models.OnlineDeployment",
+        **kwargs: Any
+    ) -> "_models.OnlineDeployment":
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineDeployment"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'OnlineDeployment')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        body: "_models.OnlineDeployment",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.OnlineDeployment"]:
+        """Create or update Inference Endpoint Deployment (asynchronous).
+
+        Create or update Inference Endpoint Deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference Endpoint Deployment name.
+        :type deployment_name: str
+        :param body: Inference Endpoint entity to apply during operation.
+        :type body: ~azure.mgmt.machinelearningservices.models.OnlineDeployment
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either OnlineDeployment or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.OnlineDeployment]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineDeployment"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+    @distributed_trace_async
+    async def get_logs(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        body: "_models.DeploymentLogsRequest",
+        **kwargs: Any
+    ) -> "_models.DeploymentLogs":
+        """Polls an Endpoint operation.
+
+        Polls an Endpoint operation.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: The name and identifier for the endpoint.
+        :type deployment_name: str
+        :param body: The request containing parameters for retrieving logs.
+        :type body: ~azure.mgmt.machinelearningservices.models.DeploymentLogsRequest
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DeploymentLogs, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DeploymentLogs
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DeploymentLogs"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'DeploymentLogsRequest')
+
+        request = build_get_logs_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.get_logs.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('DeploymentLogs', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_logs.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}/getLogs"}  # type: ignore
+
+
+    @distributed_trace
+    def list_skus(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        deployment_name: str,
+        count: Optional[int] = None,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.SkuResourceArmPaginatedResult"]:
+        """List Inference Endpoint Deployment Skus.
+
+        List Inference Endpoint Deployment Skus.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference Endpoint Deployment name.
+        :type deployment_name: str
+        :param count: Number of Skus to be retrieved in a page of results.
+        :type count: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either SkuResourceArmPaginatedResult or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.SkuResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.SkuResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_skus_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    deployment_name=deployment_name,
+                    api_version=api_version,
+                    count=count,
+                    skip=skip,
+                    template_url=self.list_skus.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_skus_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    deployment_name=deployment_name,
+                    api_version=api_version,
+                    count=count,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("SkuResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list_skus.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}/skus"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_online_endpoints_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_online_endpoints_operations.py
new file mode 100644
index 00000000..d63f3eba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_online_endpoints_operations.py
@@ -0,0 +1,897 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._online_endpoints_operations import build_create_or_update_request_initial, build_delete_request_initial, build_get_request, build_get_token_request, build_list_keys_request, build_list_request, build_regenerate_keys_request_initial, build_update_request_initial
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class OnlineEndpointsOperations:
+    """OnlineEndpointsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: Optional[str] = None,
+        count: Optional[int] = None,
+        compute_type: Optional[Union[str, "_models.EndpointComputeType"]] = None,
+        skip: Optional[str] = None,
+        tags: Optional[str] = None,
+        properties: Optional[str] = None,
+        order_by: Optional[Union[str, "_models.OrderString"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.OnlineEndpointTrackedResourceArmPaginatedResult"]:
+        """List Online Endpoints.
+
+        List Online Endpoints.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Name of the endpoint.
+        :type name: str
+        :param count: Number of endpoints to be retrieved in a page of results.
+        :type count: int
+        :param compute_type: EndpointComputeType to be filtered by.
+        :type compute_type: str or ~azure.mgmt.machinelearningservices.models.EndpointComputeType
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param tags: A set of tags with which to filter the returned models. It is a comma separated
+         string of tags key or tags key=value. Example: tagKey1,tagKey2,tagKey3=value3 .
+        :type tags: str
+        :param properties: A set of properties with which to filter the returned models. It is a comma
+         separated string of properties key and/or properties key=value Example:
+         propKey1,propKey2,propKey3=value3 .
+        :type properties: str
+        :param order_by: The option to order the response.
+        :type order_by: str or ~azure.mgmt.machinelearningservices.models.OrderString
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either OnlineEndpointTrackedResourceArmPaginatedResult or
+         the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.OnlineEndpointTrackedResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineEndpointTrackedResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    name=name,
+                    count=count,
+                    compute_type=compute_type,
+                    skip=skip,
+                    tags=tags,
+                    properties=properties,
+                    order_by=order_by,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    name=name,
+                    count=count,
+                    compute_type=compute_type,
+                    skip=skip,
+                    tags=tags,
+                    properties=properties,
+                    order_by=order_by,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("OnlineEndpointTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints"}  # type: ignore
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete Online Endpoint (asynchronous).
+
+        Delete Online Endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        **kwargs: Any
+    ) -> "_models.OnlineEndpoint":
+        """Get Online Endpoint.
+
+        Get Online Endpoint.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: OnlineEndpoint, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.OnlineEndpoint
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineEndpoint"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+
+    async def _update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        body: "_models.PartialMinimalTrackedResourceWithIdentity",
+        **kwargs: Any
+    ) -> Optional["_models.OnlineEndpoint"]:
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.OnlineEndpoint"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'PartialMinimalTrackedResourceWithIdentity')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        body: "_models.PartialMinimalTrackedResourceWithIdentity",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.OnlineEndpoint"]:
+        """Update Online Endpoint (asynchronous).
+
+        Update Online Endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :param body: Online Endpoint entity to apply during operation.
+        :type body:
+         ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithIdentity
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either OnlineEndpoint or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.OnlineEndpoint]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineEndpoint"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        body: "_models.OnlineEndpoint",
+        **kwargs: Any
+    ) -> "_models.OnlineEndpoint":
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineEndpoint"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'OnlineEndpoint')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        body: "_models.OnlineEndpoint",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.OnlineEndpoint"]:
+        """Create or update Online Endpoint (asynchronous).
+
+        Create or update Online Endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :param body: Online Endpoint entity to apply during operation.
+        :type body: ~azure.mgmt.machinelearningservices.models.OnlineEndpoint
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either OnlineEndpoint or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.OnlineEndpoint]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineEndpoint"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+    @distributed_trace_async
+    async def list_keys(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        **kwargs: Any
+    ) -> "_models.EndpointAuthKeys":
+        """List EndpointAuthKeys for an Endpoint using Key-based authentication.
+
+        List EndpointAuthKeys for an Endpoint using Key-based authentication.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EndpointAuthKeys, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EndpointAuthKeys"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self.list_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('EndpointAuthKeys', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/listKeys"}  # type: ignore
+
+
+    async def _regenerate_keys_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        body: "_models.RegenerateEndpointKeysRequest",
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'RegenerateEndpointKeysRequest')
+
+        request = build_regenerate_keys_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._regenerate_keys_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _regenerate_keys_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/regenerateKeys"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_regenerate_keys(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        body: "_models.RegenerateEndpointKeysRequest",
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Regenerate EndpointAuthKeys for an Endpoint using Key-based authentication (asynchronous).
+
+        Regenerate EndpointAuthKeys for an Endpoint using Key-based authentication (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :param body: RegenerateKeys request .
+        :type body: ~azure.mgmt.machinelearningservices.models.RegenerateEndpointKeysRequest
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._regenerate_keys_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_regenerate_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/regenerateKeys"}  # type: ignore
+
+    @distributed_trace_async
+    async def get_token(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        endpoint_name: str,
+        **kwargs: Any
+    ) -> "_models.EndpointAuthToken":
+        """Retrieve a valid AAD token for an Endpoint using AMLToken-based authentication.
+
+        Retrieve a valid AAD token for an Endpoint using AMLToken-based authentication.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EndpointAuthToken, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EndpointAuthToken
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EndpointAuthToken"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_token_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self.get_token.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('EndpointAuthToken', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_token.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/token"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_operations.py
new file mode 100644
index 00000000..fa5ac123
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_operations.py
@@ -0,0 +1,117 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._operations import build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class Operations:
+    """Operations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.AmlOperationListResult"]:
+        """Lists all of the available Azure Machine Learning Workspaces REST API operations.
+
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either AmlOperationListResult or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.AmlOperationListResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.AmlOperationListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    api_version=api_version,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("AmlOperationListResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/providers/Microsoft.MachineLearningServices/operations"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_private_endpoint_connections_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_private_endpoint_connections_operations.py
new file mode 100644
index 00000000..ef722839
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_private_endpoint_connections_operations.py
@@ -0,0 +1,325 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._private_endpoint_connections_operations import build_create_or_update_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class PrivateEndpointConnectionsOperations:
+    """PrivateEndpointConnectionsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PrivateEndpointConnectionListResult"]:
+        """List all the private endpoint connections associated with the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PrivateEndpointConnectionListResult or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PrivateEndpointConnectionListResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PrivateEndpointConnectionListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    subscription_id=self._config.subscription_id,
+                    api_version=api_version,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    subscription_id=self._config.subscription_id,
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("PrivateEndpointConnectionListResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections"}  # type: ignore
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        private_endpoint_connection_name: str,
+        **kwargs: Any
+    ) -> "_models.PrivateEndpointConnection":
+        """Gets the specified private endpoint connection associated with the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param private_endpoint_connection_name: The name of the private endpoint connection associated
+         with the workspace.
+        :type private_endpoint_connection_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: PrivateEndpointConnection, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PrivateEndpointConnection"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            private_endpoint_connection_name=private_endpoint_connection_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('PrivateEndpointConnection', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections/{privateEndpointConnectionName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        private_endpoint_connection_name: str,
+        properties: "_models.PrivateEndpointConnection",
+        **kwargs: Any
+    ) -> "_models.PrivateEndpointConnection":
+        """Update the state of specified private endpoint connection associated with the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param private_endpoint_connection_name: The name of the private endpoint connection associated
+         with the workspace.
+        :type private_endpoint_connection_name: str
+        :param properties: The private endpoint connection properties.
+        :type properties: ~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: PrivateEndpointConnection, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PrivateEndpointConnection"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(properties, 'PrivateEndpointConnection')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            private_endpoint_connection_name=private_endpoint_connection_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('PrivateEndpointConnection', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections/{privateEndpointConnectionName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        private_endpoint_connection_name: str,
+        **kwargs: Any
+    ) -> None:
+        """Deletes the specified private endpoint connection associated with the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param private_endpoint_connection_name: The name of the private endpoint connection associated
+         with the workspace.
+        :type private_endpoint_connection_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            private_endpoint_connection_name=private_endpoint_connection_name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections/{privateEndpointConnectionName}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_private_link_resources_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_private_link_resources_operations.py
new file mode 100644
index 00000000..2764e32b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_private_link_resources_operations.py
@@ -0,0 +1,103 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, Callable, Dict, Optional, TypeVar
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._private_link_resources_operations import build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class PrivateLinkResourcesOperations:
+    """PrivateLinkResourcesOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace_async
+    async def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> "_models.PrivateLinkResourceListResult":
+        """Gets the private link resources that need to be created for a workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: PrivateLinkResourceListResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.PrivateLinkResourceListResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PrivateLinkResourceListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('PrivateLinkResourceListResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateLinkResources"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_quotas_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_quotas_operations.py
new file mode 100644
index 00000000..343c09b1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_quotas_operations.py
@@ -0,0 +1,186 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._quotas_operations import build_list_request, build_update_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class QuotasOperations:
+    """QuotasOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace_async
+    async def update(
+        self,
+        location: str,
+        parameters: "_models.QuotaUpdateParameters",
+        **kwargs: Any
+    ) -> "_models.UpdateWorkspaceQuotasResult":
+        """Update quota for each VM family in workspace.
+
+        :param location: The location for update quota is queried.
+        :type location: str
+        :param parameters: Quota update parameters.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.QuotaUpdateParameters
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: UpdateWorkspaceQuotasResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.UpdateWorkspaceQuotasResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.UpdateWorkspaceQuotasResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'QuotaUpdateParameters')
+
+        request = build_update_request(
+            location=location,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('UpdateWorkspaceQuotasResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    update.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/updateQuotas"}  # type: ignore
+
+
+    @distributed_trace
+    def list(
+        self,
+        location: str,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ListWorkspaceQuotas"]:
+        """Gets the currently assigned Workspace Quotas based on VMFamily.
+
+        :param location: The location for which resource usage is queried.
+        :type location: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ListWorkspaceQuotas or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ListWorkspaceQuotas]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListWorkspaceQuotas"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    location=location,
+                    api_version=api_version,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    location=location,
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ListWorkspaceQuotas", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/quotas"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_schedules_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_schedules_operations.py
new file mode 100644
index 00000000..b22e9282
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_schedules_operations.py
@@ -0,0 +1,467 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._schedules_operations import build_create_or_update_request_initial, build_delete_request_initial, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class SchedulesOperations:
+    """SchedulesOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, "_models.ScheduleListViewType"]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ScheduleResourceArmPaginatedResult"]:
+        """List schedules in specified workspace.
+
+        List schedules in specified workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: Status filter for schedule.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ScheduleListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ScheduleResourceArmPaginatedResult or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ScheduleResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ScheduleResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ScheduleResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules"}  # type: ignore
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete schedule.
+
+        Delete schedule.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Schedule name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                name=name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}"}  # type: ignore
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> "_models.Schedule":
+        """Get schedule.
+
+        Get schedule.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Schedule name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Schedule, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Schedule
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Schedule"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('Schedule', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}"}  # type: ignore
+
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        body: "_models.Schedule",
+        **kwargs: Any
+    ) -> "_models.Schedule":
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Schedule"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'Schedule')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('Schedule', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('Schedule', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        body: "_models.Schedule",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.Schedule"]:
+        """Create or update schedule.
+
+        Create or update schedule.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Schedule name.
+        :type name: str
+        :param body: Schedule definition.
+        :type body: ~azure.mgmt.machinelearningservices.models.Schedule
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Schedule or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Schedule]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Schedule"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                name=name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('Schedule', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_usages_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_usages_operations.py
new file mode 100644
index 00000000..4df7cfc3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_usages_operations.py
@@ -0,0 +1,124 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._usages_operations import build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class UsagesOperations:
+    """UsagesOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        location: str,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ListUsagesResult"]:
+        """Gets the current usage information as well as limits for AML resources for given subscription
+        and location.
+
+        :param location: The location for which resource usage is queried.
+        :type location: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ListUsagesResult or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ListUsagesResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListUsagesResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    location=location,
+                    api_version=api_version,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    location=location,
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ListUsagesResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/usages"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_virtual_machine_sizes_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_virtual_machine_sizes_operations.py
new file mode 100644
index 00000000..d267835e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_virtual_machine_sizes_operations.py
@@ -0,0 +1,99 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, Callable, Dict, Optional, TypeVar
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._virtual_machine_sizes_operations import build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class VirtualMachineSizesOperations:
+    """VirtualMachineSizesOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace_async
+    async def list(
+        self,
+        location: str,
+        **kwargs: Any
+    ) -> "_models.VirtualMachineSizeListResult":
+        """Returns supported VM Sizes in a location.
+
+        :param location: The location upon which virtual-machine-sizes is queried.
+        :type location: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: VirtualMachineSizeListResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.VirtualMachineSizeListResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.VirtualMachineSizeListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_request(
+            location=location,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.list.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('VirtualMachineSizeListResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/vmSizes"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_workspace_connections_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_workspace_connections_operations.py
new file mode 100644
index 00000000..5ac9915e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_workspace_connections_operations.py
@@ -0,0 +1,333 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._workspace_connections_operations import build_create_request, build_delete_request, build_get_request, build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class WorkspaceConnectionsOperations:
+    """WorkspaceConnectionsOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace_async
+    async def create(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        connection_name: str,
+        parameters: "_models.WorkspaceConnectionPropertiesV2BasicResource",
+        **kwargs: Any
+    ) -> "_models.WorkspaceConnectionPropertiesV2BasicResource":
+        """create.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param connection_name: Friendly name of the workspace connection.
+        :type connection_name: str
+        :param parameters: The object for creating or updating a new workspace connection.
+        :type parameters:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: WorkspaceConnectionPropertiesV2BasicResource, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.WorkspaceConnectionPropertiesV2BasicResource"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'WorkspaceConnectionPropertiesV2BasicResource')
+
+        request = build_create_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            connection_name=connection_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('WorkspaceConnectionPropertiesV2BasicResource', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections/{connectionName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        connection_name: str,
+        **kwargs: Any
+    ) -> "_models.WorkspaceConnectionPropertiesV2BasicResource":
+        """get.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param connection_name: Friendly name of the workspace connection.
+        :type connection_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: WorkspaceConnectionPropertiesV2BasicResource, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.WorkspaceConnectionPropertiesV2BasicResource"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            connection_name=connection_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('WorkspaceConnectionPropertiesV2BasicResource', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections/{connectionName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        connection_name: str,
+        **kwargs: Any
+    ) -> None:
+        """delete.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param connection_name: Friendly name of the workspace connection.
+        :type connection_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            connection_name=connection_name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections/{connectionName}"}  # type: ignore
+
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        target: Optional[str] = None,
+        category: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult"]:
+        """list.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param target: Target of the workspace connection.
+        :type target: str
+        :param category: Category of the workspace connection.
+        :type category: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either
+         WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    target=target,
+                    category=category,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    target=target,
+                    category=category,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_workspace_features_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_workspace_features_operations.py
new file mode 100644
index 00000000..eb45522c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_workspace_features_operations.py
@@ -0,0 +1,129 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._workspace_features_operations import build_list_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class WorkspaceFeaturesOperations:
+    """WorkspaceFeaturesOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ListAmlUserFeatureResult"]:
+        """Lists all enabled features for a workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ListAmlUserFeatureResult or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ListAmlUserFeatureResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListAmlUserFeatureResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ListAmlUserFeatureResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/features"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_workspaces_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_workspaces_operations.py
new file mode 100644
index 00000000..a388db0d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/aio/operations/_workspaces_operations.py
@@ -0,0 +1,1281 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import Any, AsyncIterable, Callable, Dict, Optional, TypeVar, Union
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._workspaces_operations import build_create_or_update_request_initial, build_delete_request_initial, build_diagnose_request_initial, build_get_request, build_list_by_resource_group_request, build_list_by_subscription_request, build_list_keys_request, build_list_notebook_access_token_request, build_list_notebook_keys_request, build_list_outbound_network_dependencies_endpoints_request, build_list_storage_account_keys_request, build_prepare_notebook_request_initial, build_resync_keys_request_initial, build_update_request_initial
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class WorkspacesOperations:
+    """WorkspacesOperations async operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer) -> None:
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> "_models.Workspace":
+        """Gets the properties of the specified machine learning workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Workspace, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Workspace
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Workspace"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('Workspace', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        parameters: "_models.Workspace",
+        **kwargs: Any
+    ) -> Optional["_models.Workspace"]:
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.Workspace"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'Workspace')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        if response.status_code == 200:
+            deserialized = self._deserialize('Workspace', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        parameters: "_models.Workspace",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.Workspace"]:
+        """Creates or updates a workspace with the specified parameters.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param parameters: The parameters for creating or updating a machine learning workspace.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.Workspace
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Workspace or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Workspace]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Workspace"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                parameters=parameters,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('Workspace', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Deletes a machine learning workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+    async def _update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        parameters: "_models.WorkspaceUpdateParameters",
+        **kwargs: Any
+    ) -> Optional["_models.Workspace"]:
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.Workspace"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'WorkspaceUpdateParameters')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        if response.status_code == 200:
+            deserialized = self._deserialize('Workspace', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        parameters: "_models.WorkspaceUpdateParameters",
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.Workspace"]:
+        """Updates a machine learning workspace with the specified parameters.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param parameters: The parameters for updating a machine learning workspace.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.WorkspaceUpdateParameters
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Workspace or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Workspace]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Workspace"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                parameters=parameters,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('Workspace', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+    @distributed_trace
+    def list_by_resource_group(
+        self,
+        resource_group_name: str,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.WorkspaceListResult"]:
+        """Lists all the available machine learning workspaces under the specified resource group.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either WorkspaceListResult or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.WorkspaceListResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.WorkspaceListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_by_resource_group_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=self.list_by_resource_group.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_by_resource_group_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("WorkspaceListResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list_by_resource_group.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces"}  # type: ignore
+
+    async def _diagnose_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        parameters: Optional["_models.DiagnoseWorkspaceParameters"] = None,
+        **kwargs: Any
+    ) -> Optional["_models.DiagnoseResponseResult"]:
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.DiagnoseResponseResult"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if parameters is not None:
+            _json = self._serialize.body(parameters, 'DiagnoseWorkspaceParameters')
+        else:
+            _json = None
+
+        request = build_diagnose_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._diagnose_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('DiagnoseResponseResult', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _diagnose_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/diagnose"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_diagnose(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        parameters: Optional["_models.DiagnoseWorkspaceParameters"] = None,
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.DiagnoseResponseResult"]:
+        """Diagnose workspace setup issue.
+
+        Diagnose workspace setup issue.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param parameters: The parameter of diagnosing workspace health.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.DiagnoseWorkspaceParameters
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either DiagnoseResponseResult or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.DiagnoseResponseResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DiagnoseResponseResult"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._diagnose_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                parameters=parameters,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('DiagnoseResponseResult', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_diagnose.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/diagnose"}  # type: ignore
+
+    @distributed_trace_async
+    async def list_keys(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> "_models.ListWorkspaceKeysResult":
+        """Lists all the keys associated with this workspace. This includes keys for the storage account,
+        app insights and password for container registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ListWorkspaceKeysResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ListWorkspaceKeysResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListWorkspaceKeysResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ListWorkspaceKeysResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listKeys"}  # type: ignore
+
+
+    async def _resync_keys_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> None:
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_resync_keys_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self._resync_keys_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    _resync_keys_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/resyncKeys"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_resync_keys(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Resync all the keys associated with this workspace. This includes keys for the storage account,
+        app insights and password for container registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._resync_keys_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_resync_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/resyncKeys"}  # type: ignore
+
+    @distributed_trace
+    def list_by_subscription(
+        self,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.WorkspaceListResult"]:
+        """Lists all the available machine learning workspaces under the specified subscription.
+
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either WorkspaceListResult or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.WorkspaceListResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.WorkspaceListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_by_subscription_request(
+                    subscription_id=self._config.subscription_id,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=self.list_by_subscription.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_by_subscription_request(
+                    subscription_id=self._config.subscription_id,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("WorkspaceListResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return AsyncItemPaged(
+            get_next, extract_data
+        )
+    list_by_subscription.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/workspaces"}  # type: ignore
+
+    @distributed_trace_async
+    async def list_notebook_access_token(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> "_models.NotebookAccessTokenResult":
+        """return notebook access token and refresh token.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: NotebookAccessTokenResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.NotebookAccessTokenResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.NotebookAccessTokenResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_notebook_access_token_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list_notebook_access_token.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('NotebookAccessTokenResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_notebook_access_token.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listNotebookAccessToken"}  # type: ignore
+
+
+    async def _prepare_notebook_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> Optional["_models.NotebookResourceInfo"]:
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.NotebookResourceInfo"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_prepare_notebook_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self._prepare_notebook_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        if response.status_code == 200:
+            deserialized = self._deserialize('NotebookResourceInfo', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _prepare_notebook_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/prepareNotebook"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_prepare_notebook(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.NotebookResourceInfo"]:
+        """Prepare a notebook.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either NotebookResourceInfo or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.NotebookResourceInfo]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.NotebookResourceInfo"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = await self._prepare_notebook_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('NotebookResourceInfo', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
+        elif polling is False: polling_method = AsyncNoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_prepare_notebook.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/prepareNotebook"}  # type: ignore
+
+    @distributed_trace_async
+    async def list_storage_account_keys(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> "_models.ListStorageAccountKeysResult":
+        """List storage account keys of a workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ListStorageAccountKeysResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ListStorageAccountKeysResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListStorageAccountKeysResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_storage_account_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list_storage_account_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ListStorageAccountKeysResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_storage_account_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listStorageAccountKeys"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def list_notebook_keys(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> "_models.ListNotebookKeysResult":
+        """List keys of a notebook.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ListNotebookKeysResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ListNotebookKeysResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListNotebookKeysResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_notebook_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list_notebook_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ListNotebookKeysResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_notebook_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listNotebookKeys"}  # type: ignore
+
+
+    @distributed_trace_async
+    async def list_outbound_network_dependencies_endpoints(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> "_models.ExternalFQDNResponse":
+        """Called by Client (Portal, CLI, etc) to get a list of all external outbound dependencies (FQDNs)
+        programmatically.
+
+        Called by Client (Portal, CLI, etc) to get a list of all external outbound dependencies (FQDNs)
+        programmatically.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ExternalFQDNResponse, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ExternalFQDNResponse
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ExternalFQDNResponse"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_outbound_network_dependencies_endpoints_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list_outbound_network_dependencies_endpoints.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ExternalFQDNResponse', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_outbound_network_dependencies_endpoints.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/outboundNetworkDependenciesEndpoints"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/__init__.py
new file mode 100644
index 00000000..02fca55b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/__init__.py
@@ -0,0 +1,1328 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+try:
+    from ._models_py3 import AKS
+    from ._models_py3 import AKSSchema
+    from ._models_py3 import AKSSchemaProperties
+    from ._models_py3 import AccountKeyDatastoreCredentials
+    from ._models_py3 import AccountKeyDatastoreSecrets
+    from ._models_py3 import AksComputeSecrets
+    from ._models_py3 import AksComputeSecretsProperties
+    from ._models_py3 import AksNetworkingConfiguration
+    from ._models_py3 import AmlCompute
+    from ._models_py3 import AmlComputeNodeInformation
+    from ._models_py3 import AmlComputeNodesInformation
+    from ._models_py3 import AmlComputeProperties
+    from ._models_py3 import AmlComputeSchema
+    from ._models_py3 import AmlOperation
+    from ._models_py3 import AmlOperationDisplay
+    from ._models_py3 import AmlOperationListResult
+    from ._models_py3 import AmlToken
+    from ._models_py3 import AmlUserFeature
+    from ._models_py3 import AssetBase
+    from ._models_py3 import AssetContainer
+    from ._models_py3 import AssetJobInput
+    from ._models_py3 import AssetJobOutput
+    from ._models_py3 import AssetReferenceBase
+    from ._models_py3 import AssignedUser
+    from ._models_py3 import AutoForecastHorizon
+    from ._models_py3 import AutoMLJob
+    from ._models_py3 import AutoMLVertical
+    from ._models_py3 import AutoNCrossValidations
+    from ._models_py3 import AutoPauseProperties
+    from ._models_py3 import AutoScaleProperties
+    from ._models_py3 import AutoSeasonality
+    from ._models_py3 import AutoTargetLags
+    from ._models_py3 import AutoTargetRollingWindowSize
+    from ._models_py3 import AzureBlobDatastore
+    from ._models_py3 import AzureDataLakeGen1Datastore
+    from ._models_py3 import AzureDataLakeGen2Datastore
+    from ._models_py3 import AzureFileDatastore
+    from ._models_py3 import BanditPolicy
+    from ._models_py3 import BatchDeployment
+    from ._models_py3 import BatchDeploymentProperties
+    from ._models_py3 import BatchDeploymentTrackedResourceArmPaginatedResult
+    from ._models_py3 import BatchEndpoint
+    from ._models_py3 import BatchEndpointDefaults
+    from ._models_py3 import BatchEndpointProperties
+    from ._models_py3 import BatchEndpointTrackedResourceArmPaginatedResult
+    from ._models_py3 import BatchRetrySettings
+    from ._models_py3 import BayesianSamplingAlgorithm
+    from ._models_py3 import BuildContext
+    from ._models_py3 import CertificateDatastoreCredentials
+    from ._models_py3 import CertificateDatastoreSecrets
+    from ._models_py3 import Classification
+    from ._models_py3 import ClassificationTrainingSettings
+    from ._models_py3 import ClusterUpdateParameters
+    from ._models_py3 import CodeConfiguration
+    from ._models_py3 import CodeContainer
+    from ._models_py3 import CodeContainerProperties
+    from ._models_py3 import CodeContainerResourceArmPaginatedResult
+    from ._models_py3 import CodeVersion
+    from ._models_py3 import CodeVersionProperties
+    from ._models_py3 import CodeVersionResourceArmPaginatedResult
+    from ._models_py3 import ColumnTransformer
+    from ._models_py3 import CommandJob
+    from ._models_py3 import CommandJobLimits
+    from ._models_py3 import ComponentContainer
+    from ._models_py3 import ComponentContainerProperties
+    from ._models_py3 import ComponentContainerResourceArmPaginatedResult
+    from ._models_py3 import ComponentVersion
+    from ._models_py3 import ComponentVersionProperties
+    from ._models_py3 import ComponentVersionResourceArmPaginatedResult
+    from ._models_py3 import Compute
+    from ._models_py3 import ComputeInstance
+    from ._models_py3 import ComputeInstanceApplication
+    from ._models_py3 import ComputeInstanceConnectivityEndpoints
+    from ._models_py3 import ComputeInstanceContainer
+    from ._models_py3 import ComputeInstanceCreatedBy
+    from ._models_py3 import ComputeInstanceDataDisk
+    from ._models_py3 import ComputeInstanceDataMount
+    from ._models_py3 import ComputeInstanceEnvironmentInfo
+    from ._models_py3 import ComputeInstanceLastOperation
+    from ._models_py3 import ComputeInstanceProperties
+    from ._models_py3 import ComputeInstanceSchema
+    from ._models_py3 import ComputeInstanceSshSettings
+    from ._models_py3 import ComputeInstanceVersion
+    from ._models_py3 import ComputeResource
+    from ._models_py3 import ComputeResourceSchema
+    from ._models_py3 import ComputeSchedules
+    from ._models_py3 import ComputeSecrets
+    from ._models_py3 import ComputeStartStopSchedule
+    from ._models_py3 import ContainerResourceRequirements
+    from ._models_py3 import ContainerResourceSettings
+    from ._models_py3 import CosmosDbSettings
+    from ._models_py3 import CronTrigger
+    from ._models_py3 import CustomForecastHorizon
+    from ._models_py3 import CustomModelJobInput
+    from ._models_py3 import CustomModelJobOutput
+    from ._models_py3 import CustomNCrossValidations
+    from ._models_py3 import CustomSeasonality
+    from ._models_py3 import CustomTargetLags
+    from ._models_py3 import CustomTargetRollingWindowSize
+    from ._models_py3 import DataContainer
+    from ._models_py3 import DataContainerProperties
+    from ._models_py3 import DataContainerResourceArmPaginatedResult
+    from ._models_py3 import DataFactory
+    from ._models_py3 import DataLakeAnalytics
+    from ._models_py3 import DataLakeAnalyticsSchema
+    from ._models_py3 import DataLakeAnalyticsSchemaProperties
+    from ._models_py3 import DataPathAssetReference
+    from ._models_py3 import DataVersionBase
+    from ._models_py3 import DataVersionBaseProperties
+    from ._models_py3 import DataVersionBaseResourceArmPaginatedResult
+    from ._models_py3 import Databricks
+    from ._models_py3 import DatabricksComputeSecrets
+    from ._models_py3 import DatabricksComputeSecretsProperties
+    from ._models_py3 import DatabricksProperties
+    from ._models_py3 import DatabricksSchema
+    from ._models_py3 import Datastore
+    from ._models_py3 import DatastoreCredentials
+    from ._models_py3 import DatastoreProperties
+    from ._models_py3 import DatastoreResourceArmPaginatedResult
+    from ._models_py3 import DatastoreSecrets
+    from ._models_py3 import DefaultScaleSettings
+    from ._models_py3 import DeploymentLogs
+    from ._models_py3 import DeploymentLogsRequest
+    from ._models_py3 import DeploymentResourceConfiguration
+    from ._models_py3 import DiagnoseRequestProperties
+    from ._models_py3 import DiagnoseResponseResult
+    from ._models_py3 import DiagnoseResponseResultValue
+    from ._models_py3 import DiagnoseResult
+    from ._models_py3 import DiagnoseWorkspaceParameters
+    from ._models_py3 import DistributionConfiguration
+    from ._models_py3 import EarlyTerminationPolicy
+    from ._models_py3 import EncryptionKeyVaultProperties
+    from ._models_py3 import EncryptionProperty
+    from ._models_py3 import EndpointAuthKeys
+    from ._models_py3 import EndpointAuthToken
+    from ._models_py3 import EndpointDeploymentPropertiesBase
+    from ._models_py3 import EndpointPropertiesBase
+    from ._models_py3 import EndpointScheduleAction
+    from ._models_py3 import EnvironmentContainer
+    from ._models_py3 import EnvironmentContainerProperties
+    from ._models_py3 import EnvironmentContainerResourceArmPaginatedResult
+    from ._models_py3 import EnvironmentVersion
+    from ._models_py3 import EnvironmentVersionProperties
+    from ._models_py3 import EnvironmentVersionResourceArmPaginatedResult
+    from ._models_py3 import ErrorAdditionalInfo
+    from ._models_py3 import ErrorDetail
+    from ._models_py3 import ErrorResponse
+    from ._models_py3 import EstimatedVMPrice
+    from ._models_py3 import EstimatedVMPrices
+    from ._models_py3 import ExternalFQDNResponse
+    from ._models_py3 import FQDNEndpoint
+    from ._models_py3 import FQDNEndpointDetail
+    from ._models_py3 import FQDNEndpoints
+    from ._models_py3 import FQDNEndpointsProperties
+    from ._models_py3 import FeaturizationSettings
+    from ._models_py3 import FlavorData
+    from ._models_py3 import ForecastHorizon
+    from ._models_py3 import Forecasting
+    from ._models_py3 import ForecastingSettings
+    from ._models_py3 import ForecastingTrainingSettings
+    from ._models_py3 import GridSamplingAlgorithm
+    from ._models_py3 import HDInsight
+    from ._models_py3 import HDInsightProperties
+    from ._models_py3 import HDInsightSchema
+    from ._models_py3 import IdAssetReference
+    from ._models_py3 import IdentityConfiguration
+    from ._models_py3 import IdentityForCmk
+    from ._models_py3 import ImageClassification
+    from ._models_py3 import ImageClassificationBase
+    from ._models_py3 import ImageClassificationMultilabel
+    from ._models_py3 import ImageInstanceSegmentation
+    from ._models_py3 import ImageLimitSettings
+    from ._models_py3 import ImageModelDistributionSettings
+    from ._models_py3 import ImageModelDistributionSettingsClassification
+    from ._models_py3 import ImageModelDistributionSettingsObjectDetection
+    from ._models_py3 import ImageModelSettings
+    from ._models_py3 import ImageModelSettingsClassification
+    from ._models_py3 import ImageModelSettingsObjectDetection
+    from ._models_py3 import ImageObjectDetection
+    from ._models_py3 import ImageObjectDetectionBase
+    from ._models_py3 import ImageSweepSettings
+    from ._models_py3 import ImageVertical
+    from ._models_py3 import InferenceContainerProperties
+    from ._models_py3 import InstanceTypeSchema
+    from ._models_py3 import InstanceTypeSchemaResources
+    from ._models_py3 import JobBase
+    from ._models_py3 import JobBaseProperties
+    from ._models_py3 import JobBaseResourceArmPaginatedResult
+    from ._models_py3 import JobInput
+    from ._models_py3 import JobLimits
+    from ._models_py3 import JobOutput
+    from ._models_py3 import JobResourceConfiguration
+    from ._models_py3 import JobScheduleAction
+    from ._models_py3 import JobService
+    from ._models_py3 import Kubernetes
+    from ._models_py3 import KubernetesOnlineDeployment
+    from ._models_py3 import KubernetesProperties
+    from ._models_py3 import KubernetesSchema
+    from ._models_py3 import ListAmlUserFeatureResult
+    from ._models_py3 import ListNotebookKeysResult
+    from ._models_py3 import ListStorageAccountKeysResult
+    from ._models_py3 import ListUsagesResult
+    from ._models_py3 import ListWorkspaceKeysResult
+    from ._models_py3 import ListWorkspaceQuotas
+    from ._models_py3 import LiteralJobInput
+    from ._models_py3 import MLFlowModelJobInput
+    from ._models_py3 import MLFlowModelJobOutput
+    from ._models_py3 import MLTableData
+    from ._models_py3 import MLTableJobInput
+    from ._models_py3 import MLTableJobOutput
+    from ._models_py3 import ManagedIdentity
+    from ._models_py3 import ManagedIdentityAuthTypeWorkspaceConnectionProperties
+    from ._models_py3 import ManagedOnlineDeployment
+    from ._models_py3 import ManagedServiceIdentity
+    from ._models_py3 import MedianStoppingPolicy
+    from ._models_py3 import ModelContainer
+    from ._models_py3 import ModelContainerProperties
+    from ._models_py3 import ModelContainerResourceArmPaginatedResult
+    from ._models_py3 import ModelVersion
+    from ._models_py3 import ModelVersionProperties
+    from ._models_py3 import ModelVersionResourceArmPaginatedResult
+    from ._models_py3 import Mpi
+    from ._models_py3 import NCrossValidations
+    from ._models_py3 import NlpVertical
+    from ._models_py3 import NlpVerticalFeaturizationSettings
+    from ._models_py3 import NlpVerticalLimitSettings
+    from ._models_py3 import NodeStateCounts
+    from ._models_py3 import NoneAuthTypeWorkspaceConnectionProperties
+    from ._models_py3 import NoneDatastoreCredentials
+    from ._models_py3 import NotebookAccessTokenResult
+    from ._models_py3 import NotebookPreparationError
+    from ._models_py3 import NotebookResourceInfo
+    from ._models_py3 import Objective
+    from ._models_py3 import OnlineDeployment
+    from ._models_py3 import OnlineDeploymentProperties
+    from ._models_py3 import OnlineDeploymentTrackedResourceArmPaginatedResult
+    from ._models_py3 import OnlineEndpoint
+    from ._models_py3 import OnlineEndpointProperties
+    from ._models_py3 import OnlineEndpointTrackedResourceArmPaginatedResult
+    from ._models_py3 import OnlineRequestSettings
+    from ._models_py3 import OnlineScaleSettings
+    from ._models_py3 import OutputPathAssetReference
+    from ._models_py3 import PATAuthTypeWorkspaceConnectionProperties
+    from ._models_py3 import PaginatedComputeResourcesList
+    from ._models_py3 import PartialBatchDeployment
+    from ._models_py3 import PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties
+    from ._models_py3 import PartialManagedServiceIdentity
+    from ._models_py3 import PartialMinimalTrackedResource
+    from ._models_py3 import PartialMinimalTrackedResourceWithIdentity
+    from ._models_py3 import PartialMinimalTrackedResourceWithSku
+    from ._models_py3 import PartialSku
+    from ._models_py3 import Password
+    from ._models_py3 import PersonalComputeInstanceSettings
+    from ._models_py3 import PipelineJob
+    from ._models_py3 import PrivateEndpoint
+    from ._models_py3 import PrivateEndpointConnection
+    from ._models_py3 import PrivateEndpointConnectionListResult
+    from ._models_py3 import PrivateLinkResource
+    from ._models_py3 import PrivateLinkResourceListResult
+    from ._models_py3 import PrivateLinkServiceConnectionState
+    from ._models_py3 import ProbeSettings
+    from ._models_py3 import PyTorch
+    from ._models_py3 import QuotaBaseProperties
+    from ._models_py3 import QuotaUpdateParameters
+    from ._models_py3 import RandomSamplingAlgorithm
+    from ._models_py3 import RecurrenceSchedule
+    from ._models_py3 import RecurrenceTrigger
+    from ._models_py3 import RegenerateEndpointKeysRequest
+    from ._models_py3 import RegistryListCredentialsResult
+    from ._models_py3 import Regression
+    from ._models_py3 import RegressionTrainingSettings
+    from ._models_py3 import Resource
+    from ._models_py3 import ResourceBase
+    from ._models_py3 import ResourceConfiguration
+    from ._models_py3 import ResourceId
+    from ._models_py3 import ResourceName
+    from ._models_py3 import ResourceQuota
+    from ._models_py3 import Route
+    from ._models_py3 import SASAuthTypeWorkspaceConnectionProperties
+    from ._models_py3 import SamplingAlgorithm
+    from ._models_py3 import SasDatastoreCredentials
+    from ._models_py3 import SasDatastoreSecrets
+    from ._models_py3 import ScaleSettings
+    from ._models_py3 import ScaleSettingsInformation
+    from ._models_py3 import Schedule
+    from ._models_py3 import ScheduleActionBase
+    from ._models_py3 import ScheduleBase
+    from ._models_py3 import ScheduleProperties
+    from ._models_py3 import ScheduleResourceArmPaginatedResult
+    from ._models_py3 import ScriptReference
+    from ._models_py3 import ScriptsToExecute
+    from ._models_py3 import Seasonality
+    from ._models_py3 import ServiceManagedResourcesSettings
+    from ._models_py3 import ServicePrincipalDatastoreCredentials
+    from ._models_py3 import ServicePrincipalDatastoreSecrets
+    from ._models_py3 import SetupScripts
+    from ._models_py3 import SharedPrivateLinkResource
+    from ._models_py3 import Sku
+    from ._models_py3 import SkuCapacity
+    from ._models_py3 import SkuResource
+    from ._models_py3 import SkuResourceArmPaginatedResult
+    from ._models_py3 import SkuSetting
+    from ._models_py3 import SslConfiguration
+    from ._models_py3 import StackEnsembleSettings
+    from ._models_py3 import SweepJob
+    from ._models_py3 import SweepJobLimits
+    from ._models_py3 import SynapseSpark
+    from ._models_py3 import SynapseSparkProperties
+    from ._models_py3 import SystemData
+    from ._models_py3 import SystemService
+    from ._models_py3 import TableVertical
+    from ._models_py3 import TableVerticalFeaturizationSettings
+    from ._models_py3 import TableVerticalLimitSettings
+    from ._models_py3 import TargetLags
+    from ._models_py3 import TargetRollingWindowSize
+    from ._models_py3 import TargetUtilizationScaleSettings
+    from ._models_py3 import TensorFlow
+    from ._models_py3 import TextClassification
+    from ._models_py3 import TextClassificationMultilabel
+    from ._models_py3 import TextNer
+    from ._models_py3 import TrackedResource
+    from ._models_py3 import TrainingSettings
+    from ._models_py3 import TrialComponent
+    from ._models_py3 import TriggerBase
+    from ._models_py3 import TritonModelJobInput
+    from ._models_py3 import TritonModelJobOutput
+    from ._models_py3 import TruncationSelectionPolicy
+    from ._models_py3 import UpdateWorkspaceQuotas
+    from ._models_py3 import UpdateWorkspaceQuotasResult
+    from ._models_py3 import UriFileDataVersion
+    from ._models_py3 import UriFileJobInput
+    from ._models_py3 import UriFileJobOutput
+    from ._models_py3 import UriFolderDataVersion
+    from ._models_py3 import UriFolderJobInput
+    from ._models_py3 import UriFolderJobOutput
+    from ._models_py3 import Usage
+    from ._models_py3 import UsageName
+    from ._models_py3 import UserAccountCredentials
+    from ._models_py3 import UserAssignedIdentity
+    from ._models_py3 import UserIdentity
+    from ._models_py3 import UsernamePasswordAuthTypeWorkspaceConnectionProperties
+    from ._models_py3 import VirtualMachine
+    from ._models_py3 import VirtualMachineImage
+    from ._models_py3 import VirtualMachineSchema
+    from ._models_py3 import VirtualMachineSchemaProperties
+    from ._models_py3 import VirtualMachineSecrets
+    from ._models_py3 import VirtualMachineSecretsSchema
+    from ._models_py3 import VirtualMachineSize
+    from ._models_py3 import VirtualMachineSizeListResult
+    from ._models_py3 import VirtualMachineSshCredentials
+    from ._models_py3 import Workspace
+    from ._models_py3 import WorkspaceConnectionManagedIdentity
+    from ._models_py3 import WorkspaceConnectionPersonalAccessToken
+    from ._models_py3 import WorkspaceConnectionPropertiesV2
+    from ._models_py3 import WorkspaceConnectionPropertiesV2BasicResource
+    from ._models_py3 import WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult
+    from ._models_py3 import WorkspaceConnectionSharedAccessSignature
+    from ._models_py3 import WorkspaceConnectionUsernamePassword
+    from ._models_py3 import WorkspaceListResult
+    from ._models_py3 import WorkspaceUpdateParameters
+except (SyntaxError, ImportError):
+    from ._models import AKS  # type: ignore
+    from ._models import AKSSchema  # type: ignore
+    from ._models import AKSSchemaProperties  # type: ignore
+    from ._models import AccountKeyDatastoreCredentials  # type: ignore
+    from ._models import AccountKeyDatastoreSecrets  # type: ignore
+    from ._models import AksComputeSecrets  # type: ignore
+    from ._models import AksComputeSecretsProperties  # type: ignore
+    from ._models import AksNetworkingConfiguration  # type: ignore
+    from ._models import AmlCompute  # type: ignore
+    from ._models import AmlComputeNodeInformation  # type: ignore
+    from ._models import AmlComputeNodesInformation  # type: ignore
+    from ._models import AmlComputeProperties  # type: ignore
+    from ._models import AmlComputeSchema  # type: ignore
+    from ._models import AmlOperation  # type: ignore
+    from ._models import AmlOperationDisplay  # type: ignore
+    from ._models import AmlOperationListResult  # type: ignore
+    from ._models import AmlToken  # type: ignore
+    from ._models import AmlUserFeature  # type: ignore
+    from ._models import AssetBase  # type: ignore
+    from ._models import AssetContainer  # type: ignore
+    from ._models import AssetJobInput  # type: ignore
+    from ._models import AssetJobOutput  # type: ignore
+    from ._models import AssetReferenceBase  # type: ignore
+    from ._models import AssignedUser  # type: ignore
+    from ._models import AutoForecastHorizon  # type: ignore
+    from ._models import AutoMLJob  # type: ignore
+    from ._models import AutoMLVertical  # type: ignore
+    from ._models import AutoNCrossValidations  # type: ignore
+    from ._models import AutoPauseProperties  # type: ignore
+    from ._models import AutoScaleProperties  # type: ignore
+    from ._models import AutoSeasonality  # type: ignore
+    from ._models import AutoTargetLags  # type: ignore
+    from ._models import AutoTargetRollingWindowSize  # type: ignore
+    from ._models import AzureBlobDatastore  # type: ignore
+    from ._models import AzureDataLakeGen1Datastore  # type: ignore
+    from ._models import AzureDataLakeGen2Datastore  # type: ignore
+    from ._models import AzureFileDatastore  # type: ignore
+    from ._models import BanditPolicy  # type: ignore
+    from ._models import BatchDeployment  # type: ignore
+    from ._models import BatchDeploymentProperties  # type: ignore
+    from ._models import BatchDeploymentTrackedResourceArmPaginatedResult  # type: ignore
+    from ._models import BatchEndpoint  # type: ignore
+    from ._models import BatchEndpointDefaults  # type: ignore
+    from ._models import BatchEndpointProperties  # type: ignore
+    from ._models import BatchEndpointTrackedResourceArmPaginatedResult  # type: ignore
+    from ._models import BatchRetrySettings  # type: ignore
+    from ._models import BayesianSamplingAlgorithm  # type: ignore
+    from ._models import BuildContext  # type: ignore
+    from ._models import CertificateDatastoreCredentials  # type: ignore
+    from ._models import CertificateDatastoreSecrets  # type: ignore
+    from ._models import Classification  # type: ignore
+    from ._models import ClassificationTrainingSettings  # type: ignore
+    from ._models import ClusterUpdateParameters  # type: ignore
+    from ._models import CodeConfiguration  # type: ignore
+    from ._models import CodeContainer  # type: ignore
+    from ._models import CodeContainerProperties  # type: ignore
+    from ._models import CodeContainerResourceArmPaginatedResult  # type: ignore
+    from ._models import CodeVersion  # type: ignore
+    from ._models import CodeVersionProperties  # type: ignore
+    from ._models import CodeVersionResourceArmPaginatedResult  # type: ignore
+    from ._models import ColumnTransformer  # type: ignore
+    from ._models import CommandJob  # type: ignore
+    from ._models import CommandJobLimits  # type: ignore
+    from ._models import ComponentContainer  # type: ignore
+    from ._models import ComponentContainerProperties  # type: ignore
+    from ._models import ComponentContainerResourceArmPaginatedResult  # type: ignore
+    from ._models import ComponentVersion  # type: ignore
+    from ._models import ComponentVersionProperties  # type: ignore
+    from ._models import ComponentVersionResourceArmPaginatedResult  # type: ignore
+    from ._models import Compute  # type: ignore
+    from ._models import ComputeInstance  # type: ignore
+    from ._models import ComputeInstanceApplication  # type: ignore
+    from ._models import ComputeInstanceConnectivityEndpoints  # type: ignore
+    from ._models import ComputeInstanceContainer  # type: ignore
+    from ._models import ComputeInstanceCreatedBy  # type: ignore
+    from ._models import ComputeInstanceDataDisk  # type: ignore
+    from ._models import ComputeInstanceDataMount  # type: ignore
+    from ._models import ComputeInstanceEnvironmentInfo  # type: ignore
+    from ._models import ComputeInstanceLastOperation  # type: ignore
+    from ._models import ComputeInstanceProperties  # type: ignore
+    from ._models import ComputeInstanceSchema  # type: ignore
+    from ._models import ComputeInstanceSshSettings  # type: ignore
+    from ._models import ComputeInstanceVersion  # type: ignore
+    from ._models import ComputeResource  # type: ignore
+    from ._models import ComputeResourceSchema  # type: ignore
+    from ._models import ComputeSchedules  # type: ignore
+    from ._models import ComputeSecrets  # type: ignore
+    from ._models import ComputeStartStopSchedule  # type: ignore
+    from ._models import ContainerResourceRequirements  # type: ignore
+    from ._models import ContainerResourceSettings  # type: ignore
+    from ._models import CosmosDbSettings  # type: ignore
+    from ._models import CronTrigger  # type: ignore
+    from ._models import CustomForecastHorizon  # type: ignore
+    from ._models import CustomModelJobInput  # type: ignore
+    from ._models import CustomModelJobOutput  # type: ignore
+    from ._models import CustomNCrossValidations  # type: ignore
+    from ._models import CustomSeasonality  # type: ignore
+    from ._models import CustomTargetLags  # type: ignore
+    from ._models import CustomTargetRollingWindowSize  # type: ignore
+    from ._models import DataContainer  # type: ignore
+    from ._models import DataContainerProperties  # type: ignore
+    from ._models import DataContainerResourceArmPaginatedResult  # type: ignore
+    from ._models import DataFactory  # type: ignore
+    from ._models import DataLakeAnalytics  # type: ignore
+    from ._models import DataLakeAnalyticsSchema  # type: ignore
+    from ._models import DataLakeAnalyticsSchemaProperties  # type: ignore
+    from ._models import DataPathAssetReference  # type: ignore
+    from ._models import DataVersionBase  # type: ignore
+    from ._models import DataVersionBaseProperties  # type: ignore
+    from ._models import DataVersionBaseResourceArmPaginatedResult  # type: ignore
+    from ._models import Databricks  # type: ignore
+    from ._models import DatabricksComputeSecrets  # type: ignore
+    from ._models import DatabricksComputeSecretsProperties  # type: ignore
+    from ._models import DatabricksProperties  # type: ignore
+    from ._models import DatabricksSchema  # type: ignore
+    from ._models import Datastore  # type: ignore
+    from ._models import DatastoreCredentials  # type: ignore
+    from ._models import DatastoreProperties  # type: ignore
+    from ._models import DatastoreResourceArmPaginatedResult  # type: ignore
+    from ._models import DatastoreSecrets  # type: ignore
+    from ._models import DefaultScaleSettings  # type: ignore
+    from ._models import DeploymentLogs  # type: ignore
+    from ._models import DeploymentLogsRequest  # type: ignore
+    from ._models import DeploymentResourceConfiguration  # type: ignore
+    from ._models import DiagnoseRequestProperties  # type: ignore
+    from ._models import DiagnoseResponseResult  # type: ignore
+    from ._models import DiagnoseResponseResultValue  # type: ignore
+    from ._models import DiagnoseResult  # type: ignore
+    from ._models import DiagnoseWorkspaceParameters  # type: ignore
+    from ._models import DistributionConfiguration  # type: ignore
+    from ._models import EarlyTerminationPolicy  # type: ignore
+    from ._models import EncryptionKeyVaultProperties  # type: ignore
+    from ._models import EncryptionProperty  # type: ignore
+    from ._models import EndpointAuthKeys  # type: ignore
+    from ._models import EndpointAuthToken  # type: ignore
+    from ._models import EndpointDeploymentPropertiesBase  # type: ignore
+    from ._models import EndpointPropertiesBase  # type: ignore
+    from ._models import EndpointScheduleAction  # type: ignore
+    from ._models import EnvironmentContainer  # type: ignore
+    from ._models import EnvironmentContainerProperties  # type: ignore
+    from ._models import EnvironmentContainerResourceArmPaginatedResult  # type: ignore
+    from ._models import EnvironmentVersion  # type: ignore
+    from ._models import EnvironmentVersionProperties  # type: ignore
+    from ._models import EnvironmentVersionResourceArmPaginatedResult  # type: ignore
+    from ._models import ErrorAdditionalInfo  # type: ignore
+    from ._models import ErrorDetail  # type: ignore
+    from ._models import ErrorResponse  # type: ignore
+    from ._models import EstimatedVMPrice  # type: ignore
+    from ._models import EstimatedVMPrices  # type: ignore
+    from ._models import ExternalFQDNResponse  # type: ignore
+    from ._models import FQDNEndpoint  # type: ignore
+    from ._models import FQDNEndpointDetail  # type: ignore
+    from ._models import FQDNEndpoints  # type: ignore
+    from ._models import FQDNEndpointsProperties  # type: ignore
+    from ._models import FeaturizationSettings  # type: ignore
+    from ._models import FlavorData  # type: ignore
+    from ._models import ForecastHorizon  # type: ignore
+    from ._models import Forecasting  # type: ignore
+    from ._models import ForecastingSettings  # type: ignore
+    from ._models import ForecastingTrainingSettings  # type: ignore
+    from ._models import GridSamplingAlgorithm  # type: ignore
+    from ._models import HDInsight  # type: ignore
+    from ._models import HDInsightProperties  # type: ignore
+    from ._models import HDInsightSchema  # type: ignore
+    from ._models import IdAssetReference  # type: ignore
+    from ._models import IdentityConfiguration  # type: ignore
+    from ._models import IdentityForCmk  # type: ignore
+    from ._models import ImageClassification  # type: ignore
+    from ._models import ImageClassificationBase  # type: ignore
+    from ._models import ImageClassificationMultilabel  # type: ignore
+    from ._models import ImageInstanceSegmentation  # type: ignore
+    from ._models import ImageLimitSettings  # type: ignore
+    from ._models import ImageModelDistributionSettings  # type: ignore
+    from ._models import ImageModelDistributionSettingsClassification  # type: ignore
+    from ._models import ImageModelDistributionSettingsObjectDetection  # type: ignore
+    from ._models import ImageModelSettings  # type: ignore
+    from ._models import ImageModelSettingsClassification  # type: ignore
+    from ._models import ImageModelSettingsObjectDetection  # type: ignore
+    from ._models import ImageObjectDetection  # type: ignore
+    from ._models import ImageObjectDetectionBase  # type: ignore
+    from ._models import ImageSweepSettings  # type: ignore
+    from ._models import ImageVertical  # type: ignore
+    from ._models import InferenceContainerProperties  # type: ignore
+    from ._models import InstanceTypeSchema  # type: ignore
+    from ._models import InstanceTypeSchemaResources  # type: ignore
+    from ._models import JobBase  # type: ignore
+    from ._models import JobBaseProperties  # type: ignore
+    from ._models import JobBaseResourceArmPaginatedResult  # type: ignore
+    from ._models import JobInput  # type: ignore
+    from ._models import JobLimits  # type: ignore
+    from ._models import JobOutput  # type: ignore
+    from ._models import JobResourceConfiguration  # type: ignore
+    from ._models import JobScheduleAction  # type: ignore
+    from ._models import JobService  # type: ignore
+    from ._models import Kubernetes  # type: ignore
+    from ._models import KubernetesOnlineDeployment  # type: ignore
+    from ._models import KubernetesProperties  # type: ignore
+    from ._models import KubernetesSchema  # type: ignore
+    from ._models import ListAmlUserFeatureResult  # type: ignore
+    from ._models import ListNotebookKeysResult  # type: ignore
+    from ._models import ListStorageAccountKeysResult  # type: ignore
+    from ._models import ListUsagesResult  # type: ignore
+    from ._models import ListWorkspaceKeysResult  # type: ignore
+    from ._models import ListWorkspaceQuotas  # type: ignore
+    from ._models import LiteralJobInput  # type: ignore
+    from ._models import MLFlowModelJobInput  # type: ignore
+    from ._models import MLFlowModelJobOutput  # type: ignore
+    from ._models import MLTableData  # type: ignore
+    from ._models import MLTableJobInput  # type: ignore
+    from ._models import MLTableJobOutput  # type: ignore
+    from ._models import ManagedIdentity  # type: ignore
+    from ._models import ManagedIdentityAuthTypeWorkspaceConnectionProperties  # type: ignore
+    from ._models import ManagedOnlineDeployment  # type: ignore
+    from ._models import ManagedServiceIdentity  # type: ignore
+    from ._models import MedianStoppingPolicy  # type: ignore
+    from ._models import ModelContainer  # type: ignore
+    from ._models import ModelContainerProperties  # type: ignore
+    from ._models import ModelContainerResourceArmPaginatedResult  # type: ignore
+    from ._models import ModelVersion  # type: ignore
+    from ._models import ModelVersionProperties  # type: ignore
+    from ._models import ModelVersionResourceArmPaginatedResult  # type: ignore
+    from ._models import Mpi  # type: ignore
+    from ._models import NCrossValidations  # type: ignore
+    from ._models import NlpVertical  # type: ignore
+    from ._models import NlpVerticalFeaturizationSettings  # type: ignore
+    from ._models import NlpVerticalLimitSettings  # type: ignore
+    from ._models import NodeStateCounts  # type: ignore
+    from ._models import NoneAuthTypeWorkspaceConnectionProperties  # type: ignore
+    from ._models import NoneDatastoreCredentials  # type: ignore
+    from ._models import NotebookAccessTokenResult  # type: ignore
+    from ._models import NotebookPreparationError  # type: ignore
+    from ._models import NotebookResourceInfo  # type: ignore
+    from ._models import Objective  # type: ignore
+    from ._models import OnlineDeployment  # type: ignore
+    from ._models import OnlineDeploymentProperties  # type: ignore
+    from ._models import OnlineDeploymentTrackedResourceArmPaginatedResult  # type: ignore
+    from ._models import OnlineEndpoint  # type: ignore
+    from ._models import OnlineEndpointProperties  # type: ignore
+    from ._models import OnlineEndpointTrackedResourceArmPaginatedResult  # type: ignore
+    from ._models import OnlineRequestSettings  # type: ignore
+    from ._models import OnlineScaleSettings  # type: ignore
+    from ._models import OutputPathAssetReference  # type: ignore
+    from ._models import PATAuthTypeWorkspaceConnectionProperties  # type: ignore
+    from ._models import PaginatedComputeResourcesList  # type: ignore
+    from ._models import PartialBatchDeployment  # type: ignore
+    from ._models import PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties  # type: ignore
+    from ._models import PartialManagedServiceIdentity  # type: ignore
+    from ._models import PartialMinimalTrackedResource  # type: ignore
+    from ._models import PartialMinimalTrackedResourceWithIdentity  # type: ignore
+    from ._models import PartialMinimalTrackedResourceWithSku  # type: ignore
+    from ._models import PartialSku  # type: ignore
+    from ._models import Password  # type: ignore
+    from ._models import PersonalComputeInstanceSettings  # type: ignore
+    from ._models import PipelineJob  # type: ignore
+    from ._models import PrivateEndpoint  # type: ignore
+    from ._models import PrivateEndpointConnection  # type: ignore
+    from ._models import PrivateEndpointConnectionListResult  # type: ignore
+    from ._models import PrivateLinkResource  # type: ignore
+    from ._models import PrivateLinkResourceListResult  # type: ignore
+    from ._models import PrivateLinkServiceConnectionState  # type: ignore
+    from ._models import ProbeSettings  # type: ignore
+    from ._models import PyTorch  # type: ignore
+    from ._models import QuotaBaseProperties  # type: ignore
+    from ._models import QuotaUpdateParameters  # type: ignore
+    from ._models import RandomSamplingAlgorithm  # type: ignore
+    from ._models import RecurrenceSchedule  # type: ignore
+    from ._models import RecurrenceTrigger  # type: ignore
+    from ._models import RegenerateEndpointKeysRequest  # type: ignore
+    from ._models import RegistryListCredentialsResult  # type: ignore
+    from ._models import Regression  # type: ignore
+    from ._models import RegressionTrainingSettings  # type: ignore
+    from ._models import Resource  # type: ignore
+    from ._models import ResourceBase  # type: ignore
+    from ._models import ResourceConfiguration  # type: ignore
+    from ._models import ResourceId  # type: ignore
+    from ._models import ResourceName  # type: ignore
+    from ._models import ResourceQuota  # type: ignore
+    from ._models import Route  # type: ignore
+    from ._models import SASAuthTypeWorkspaceConnectionProperties  # type: ignore
+    from ._models import SamplingAlgorithm  # type: ignore
+    from ._models import SasDatastoreCredentials  # type: ignore
+    from ._models import SasDatastoreSecrets  # type: ignore
+    from ._models import ScaleSettings  # type: ignore
+    from ._models import ScaleSettingsInformation  # type: ignore
+    from ._models import Schedule  # type: ignore
+    from ._models import ScheduleActionBase  # type: ignore
+    from ._models import ScheduleBase  # type: ignore
+    from ._models import ScheduleProperties  # type: ignore
+    from ._models import ScheduleResourceArmPaginatedResult  # type: ignore
+    from ._models import ScriptReference  # type: ignore
+    from ._models import ScriptsToExecute  # type: ignore
+    from ._models import Seasonality  # type: ignore
+    from ._models import ServiceManagedResourcesSettings  # type: ignore
+    from ._models import ServicePrincipalDatastoreCredentials  # type: ignore
+    from ._models import ServicePrincipalDatastoreSecrets  # type: ignore
+    from ._models import SetupScripts  # type: ignore
+    from ._models import SharedPrivateLinkResource  # type: ignore
+    from ._models import Sku  # type: ignore
+    from ._models import SkuCapacity  # type: ignore
+    from ._models import SkuResource  # type: ignore
+    from ._models import SkuResourceArmPaginatedResult  # type: ignore
+    from ._models import SkuSetting  # type: ignore
+    from ._models import SslConfiguration  # type: ignore
+    from ._models import StackEnsembleSettings  # type: ignore
+    from ._models import SweepJob  # type: ignore
+    from ._models import SweepJobLimits  # type: ignore
+    from ._models import SynapseSpark  # type: ignore
+    from ._models import SynapseSparkProperties  # type: ignore
+    from ._models import SystemData  # type: ignore
+    from ._models import SystemService  # type: ignore
+    from ._models import TableVertical  # type: ignore
+    from ._models import TableVerticalFeaturizationSettings  # type: ignore
+    from ._models import TableVerticalLimitSettings  # type: ignore
+    from ._models import TargetLags  # type: ignore
+    from ._models import TargetRollingWindowSize  # type: ignore
+    from ._models import TargetUtilizationScaleSettings  # type: ignore
+    from ._models import TensorFlow  # type: ignore
+    from ._models import TextClassification  # type: ignore
+    from ._models import TextClassificationMultilabel  # type: ignore
+    from ._models import TextNer  # type: ignore
+    from ._models import TrackedResource  # type: ignore
+    from ._models import TrainingSettings  # type: ignore
+    from ._models import TrialComponent  # type: ignore
+    from ._models import TriggerBase  # type: ignore
+    from ._models import TritonModelJobInput  # type: ignore
+    from ._models import TritonModelJobOutput  # type: ignore
+    from ._models import TruncationSelectionPolicy  # type: ignore
+    from ._models import UpdateWorkspaceQuotas  # type: ignore
+    from ._models import UpdateWorkspaceQuotasResult  # type: ignore
+    from ._models import UriFileDataVersion  # type: ignore
+    from ._models import UriFileJobInput  # type: ignore
+    from ._models import UriFileJobOutput  # type: ignore
+    from ._models import UriFolderDataVersion  # type: ignore
+    from ._models import UriFolderJobInput  # type: ignore
+    from ._models import UriFolderJobOutput  # type: ignore
+    from ._models import Usage  # type: ignore
+    from ._models import UsageName  # type: ignore
+    from ._models import UserAccountCredentials  # type: ignore
+    from ._models import UserAssignedIdentity  # type: ignore
+    from ._models import UserIdentity  # type: ignore
+    from ._models import UsernamePasswordAuthTypeWorkspaceConnectionProperties  # type: ignore
+    from ._models import VirtualMachine  # type: ignore
+    from ._models import VirtualMachineImage  # type: ignore
+    from ._models import VirtualMachineSchema  # type: ignore
+    from ._models import VirtualMachineSchemaProperties  # type: ignore
+    from ._models import VirtualMachineSecrets  # type: ignore
+    from ._models import VirtualMachineSecretsSchema  # type: ignore
+    from ._models import VirtualMachineSize  # type: ignore
+    from ._models import VirtualMachineSizeListResult  # type: ignore
+    from ._models import VirtualMachineSshCredentials  # type: ignore
+    from ._models import Workspace  # type: ignore
+    from ._models import WorkspaceConnectionManagedIdentity  # type: ignore
+    from ._models import WorkspaceConnectionPersonalAccessToken  # type: ignore
+    from ._models import WorkspaceConnectionPropertiesV2  # type: ignore
+    from ._models import WorkspaceConnectionPropertiesV2BasicResource  # type: ignore
+    from ._models import WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult  # type: ignore
+    from ._models import WorkspaceConnectionSharedAccessSignature  # type: ignore
+    from ._models import WorkspaceConnectionUsernamePassword  # type: ignore
+    from ._models import WorkspaceListResult  # type: ignore
+    from ._models import WorkspaceUpdateParameters  # type: ignore
+
+from ._azure_machine_learning_workspaces_enums import (
+    AllocationState,
+    ApplicationSharingPolicy,
+    AutoRebuildSetting,
+    Autosave,
+    BatchLoggingLevel,
+    BatchOutputAction,
+    BillingCurrency,
+    BlockedTransformers,
+    Caching,
+    ClassificationModels,
+    ClassificationMultilabelPrimaryMetrics,
+    ClassificationPrimaryMetrics,
+    ClusterPurpose,
+    ComputeInstanceAuthorizationType,
+    ComputeInstanceState,
+    ComputePowerAction,
+    ComputeType,
+    ConnectionAuthType,
+    ConnectionCategory,
+    ContainerType,
+    CreatedByType,
+    CredentialsType,
+    DataType,
+    DatastoreType,
+    DeploymentProvisioningState,
+    DiagnoseResultLevel,
+    DistributionType,
+    EarlyTerminationPolicyType,
+    EgressPublicNetworkAccessType,
+    EncryptionStatus,
+    EndpointAuthMode,
+    EndpointComputeType,
+    EndpointProvisioningState,
+    EnvironmentType,
+    FeatureLags,
+    FeaturizationMode,
+    ForecastHorizonMode,
+    ForecastingModels,
+    ForecastingPrimaryMetrics,
+    Goal,
+    IdentityConfigurationType,
+    InputDeliveryMode,
+    InstanceSegmentationPrimaryMetrics,
+    JobInputType,
+    JobLimitsType,
+    JobOutputType,
+    JobStatus,
+    JobType,
+    KeyType,
+    LearningRateScheduler,
+    ListViewType,
+    LoadBalancerType,
+    LogVerbosity,
+    ManagedServiceIdentityType,
+    ModelSize,
+    MountAction,
+    MountState,
+    NCrossValidationsMode,
+    Network,
+    NodeState,
+    ObjectDetectionPrimaryMetrics,
+    OperatingSystemType,
+    OperationName,
+    OperationStatus,
+    OperationTrigger,
+    OrderString,
+    OsType,
+    OutputDeliveryMode,
+    PrivateEndpointConnectionProvisioningState,
+    PrivateEndpointServiceConnectionStatus,
+    ProvisioningState,
+    ProvisioningStatus,
+    PublicNetworkAccess,
+    PublicNetworkAccessType,
+    QuotaUnit,
+    RandomSamplingAlgorithmRule,
+    RecurrenceFrequency,
+    ReferenceType,
+    RegressionModels,
+    RegressionPrimaryMetrics,
+    RemoteLoginPortPublicAccess,
+    SamplingAlgorithmType,
+    ScaleType,
+    ScheduleActionType,
+    ScheduleListViewType,
+    ScheduleProvisioningState,
+    ScheduleProvisioningStatus,
+    ScheduleStatus,
+    SeasonalityMode,
+    SecretsType,
+    ServiceDataAccessAuthIdentity,
+    ShortSeriesHandlingConfiguration,
+    SkuScaleType,
+    SkuTier,
+    SourceType,
+    SshPublicAccess,
+    SslConfigStatus,
+    StackMetaLearnerType,
+    Status,
+    StochasticOptimizer,
+    StorageAccountType,
+    TargetAggregationFunction,
+    TargetLagsMode,
+    TargetRollingWindowSizeMode,
+    TaskType,
+    TriggerType,
+    UnderlyingResourceAction,
+    UnitOfMeasure,
+    UsageUnit,
+    UseStl,
+    VMPriceOSType,
+    VMTier,
+    ValidationMetricType,
+    ValueFormat,
+    VmPriority,
+    WeekDay,
+)
+
+__all__ = [
+    'AKS',
+    'AKSSchema',
+    'AKSSchemaProperties',
+    'AccountKeyDatastoreCredentials',
+    'AccountKeyDatastoreSecrets',
+    'AksComputeSecrets',
+    'AksComputeSecretsProperties',
+    'AksNetworkingConfiguration',
+    'AmlCompute',
+    'AmlComputeNodeInformation',
+    'AmlComputeNodesInformation',
+    'AmlComputeProperties',
+    'AmlComputeSchema',
+    'AmlOperation',
+    'AmlOperationDisplay',
+    'AmlOperationListResult',
+    'AmlToken',
+    'AmlUserFeature',
+    'AssetBase',
+    'AssetContainer',
+    'AssetJobInput',
+    'AssetJobOutput',
+    'AssetReferenceBase',
+    'AssignedUser',
+    'AutoForecastHorizon',
+    'AutoMLJob',
+    'AutoMLVertical',
+    'AutoNCrossValidations',
+    'AutoPauseProperties',
+    'AutoScaleProperties',
+    'AutoSeasonality',
+    'AutoTargetLags',
+    'AutoTargetRollingWindowSize',
+    'AzureBlobDatastore',
+    'AzureDataLakeGen1Datastore',
+    'AzureDataLakeGen2Datastore',
+    'AzureFileDatastore',
+    'BanditPolicy',
+    'BatchDeployment',
+    'BatchDeploymentProperties',
+    'BatchDeploymentTrackedResourceArmPaginatedResult',
+    'BatchEndpoint',
+    'BatchEndpointDefaults',
+    'BatchEndpointProperties',
+    'BatchEndpointTrackedResourceArmPaginatedResult',
+    'BatchRetrySettings',
+    'BayesianSamplingAlgorithm',
+    'BuildContext',
+    'CertificateDatastoreCredentials',
+    'CertificateDatastoreSecrets',
+    'Classification',
+    'ClassificationTrainingSettings',
+    'ClusterUpdateParameters',
+    'CodeConfiguration',
+    'CodeContainer',
+    'CodeContainerProperties',
+    'CodeContainerResourceArmPaginatedResult',
+    'CodeVersion',
+    'CodeVersionProperties',
+    'CodeVersionResourceArmPaginatedResult',
+    'ColumnTransformer',
+    'CommandJob',
+    'CommandJobLimits',
+    'ComponentContainer',
+    'ComponentContainerProperties',
+    'ComponentContainerResourceArmPaginatedResult',
+    'ComponentVersion',
+    'ComponentVersionProperties',
+    'ComponentVersionResourceArmPaginatedResult',
+    'Compute',
+    'ComputeInstance',
+    'ComputeInstanceApplication',
+    'ComputeInstanceConnectivityEndpoints',
+    'ComputeInstanceContainer',
+    'ComputeInstanceCreatedBy',
+    'ComputeInstanceDataDisk',
+    'ComputeInstanceDataMount',
+    'ComputeInstanceEnvironmentInfo',
+    'ComputeInstanceLastOperation',
+    'ComputeInstanceProperties',
+    'ComputeInstanceSchema',
+    'ComputeInstanceSshSettings',
+    'ComputeInstanceVersion',
+    'ComputeResource',
+    'ComputeResourceSchema',
+    'ComputeSchedules',
+    'ComputeSecrets',
+    'ComputeStartStopSchedule',
+    'ContainerResourceRequirements',
+    'ContainerResourceSettings',
+    'CosmosDbSettings',
+    'CronTrigger',
+    'CustomForecastHorizon',
+    'CustomModelJobInput',
+    'CustomModelJobOutput',
+    'CustomNCrossValidations',
+    'CustomSeasonality',
+    'CustomTargetLags',
+    'CustomTargetRollingWindowSize',
+    'DataContainer',
+    'DataContainerProperties',
+    'DataContainerResourceArmPaginatedResult',
+    'DataFactory',
+    'DataLakeAnalytics',
+    'DataLakeAnalyticsSchema',
+    'DataLakeAnalyticsSchemaProperties',
+    'DataPathAssetReference',
+    'DataVersionBase',
+    'DataVersionBaseProperties',
+    'DataVersionBaseResourceArmPaginatedResult',
+    'Databricks',
+    'DatabricksComputeSecrets',
+    'DatabricksComputeSecretsProperties',
+    'DatabricksProperties',
+    'DatabricksSchema',
+    'Datastore',
+    'DatastoreCredentials',
+    'DatastoreProperties',
+    'DatastoreResourceArmPaginatedResult',
+    'DatastoreSecrets',
+    'DefaultScaleSettings',
+    'DeploymentLogs',
+    'DeploymentLogsRequest',
+    'DeploymentResourceConfiguration',
+    'DiagnoseRequestProperties',
+    'DiagnoseResponseResult',
+    'DiagnoseResponseResultValue',
+    'DiagnoseResult',
+    'DiagnoseWorkspaceParameters',
+    'DistributionConfiguration',
+    'EarlyTerminationPolicy',
+    'EncryptionKeyVaultProperties',
+    'EncryptionProperty',
+    'EndpointAuthKeys',
+    'EndpointAuthToken',
+    'EndpointDeploymentPropertiesBase',
+    'EndpointPropertiesBase',
+    'EndpointScheduleAction',
+    'EnvironmentContainer',
+    'EnvironmentContainerProperties',
+    'EnvironmentContainerResourceArmPaginatedResult',
+    'EnvironmentVersion',
+    'EnvironmentVersionProperties',
+    'EnvironmentVersionResourceArmPaginatedResult',
+    'ErrorAdditionalInfo',
+    'ErrorDetail',
+    'ErrorResponse',
+    'EstimatedVMPrice',
+    'EstimatedVMPrices',
+    'ExternalFQDNResponse',
+    'FQDNEndpoint',
+    'FQDNEndpointDetail',
+    'FQDNEndpoints',
+    'FQDNEndpointsProperties',
+    'FeaturizationSettings',
+    'FlavorData',
+    'ForecastHorizon',
+    'Forecasting',
+    'ForecastingSettings',
+    'ForecastingTrainingSettings',
+    'GridSamplingAlgorithm',
+    'HDInsight',
+    'HDInsightProperties',
+    'HDInsightSchema',
+    'IdAssetReference',
+    'IdentityConfiguration',
+    'IdentityForCmk',
+    'ImageClassification',
+    'ImageClassificationBase',
+    'ImageClassificationMultilabel',
+    'ImageInstanceSegmentation',
+    'ImageLimitSettings',
+    'ImageModelDistributionSettings',
+    'ImageModelDistributionSettingsClassification',
+    'ImageModelDistributionSettingsObjectDetection',
+    'ImageModelSettings',
+    'ImageModelSettingsClassification',
+    'ImageModelSettingsObjectDetection',
+    'ImageObjectDetection',
+    'ImageObjectDetectionBase',
+    'ImageSweepSettings',
+    'ImageVertical',
+    'InferenceContainerProperties',
+    'InstanceTypeSchema',
+    'InstanceTypeSchemaResources',
+    'JobBase',
+    'JobBaseProperties',
+    'JobBaseResourceArmPaginatedResult',
+    'JobInput',
+    'JobLimits',
+    'JobOutput',
+    'JobResourceConfiguration',
+    'JobScheduleAction',
+    'JobService',
+    'Kubernetes',
+    'KubernetesOnlineDeployment',
+    'KubernetesProperties',
+    'KubernetesSchema',
+    'ListAmlUserFeatureResult',
+    'ListNotebookKeysResult',
+    'ListStorageAccountKeysResult',
+    'ListUsagesResult',
+    'ListWorkspaceKeysResult',
+    'ListWorkspaceQuotas',
+    'LiteralJobInput',
+    'MLFlowModelJobInput',
+    'MLFlowModelJobOutput',
+    'MLTableData',
+    'MLTableJobInput',
+    'MLTableJobOutput',
+    'ManagedIdentity',
+    'ManagedIdentityAuthTypeWorkspaceConnectionProperties',
+    'ManagedOnlineDeployment',
+    'ManagedServiceIdentity',
+    'MedianStoppingPolicy',
+    'ModelContainer',
+    'ModelContainerProperties',
+    'ModelContainerResourceArmPaginatedResult',
+    'ModelVersion',
+    'ModelVersionProperties',
+    'ModelVersionResourceArmPaginatedResult',
+    'Mpi',
+    'NCrossValidations',
+    'NlpVertical',
+    'NlpVerticalFeaturizationSettings',
+    'NlpVerticalLimitSettings',
+    'NodeStateCounts',
+    'NoneAuthTypeWorkspaceConnectionProperties',
+    'NoneDatastoreCredentials',
+    'NotebookAccessTokenResult',
+    'NotebookPreparationError',
+    'NotebookResourceInfo',
+    'Objective',
+    'OnlineDeployment',
+    'OnlineDeploymentProperties',
+    'OnlineDeploymentTrackedResourceArmPaginatedResult',
+    'OnlineEndpoint',
+    'OnlineEndpointProperties',
+    'OnlineEndpointTrackedResourceArmPaginatedResult',
+    'OnlineRequestSettings',
+    'OnlineScaleSettings',
+    'OutputPathAssetReference',
+    'PATAuthTypeWorkspaceConnectionProperties',
+    'PaginatedComputeResourcesList',
+    'PartialBatchDeployment',
+    'PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties',
+    'PartialManagedServiceIdentity',
+    'PartialMinimalTrackedResource',
+    'PartialMinimalTrackedResourceWithIdentity',
+    'PartialMinimalTrackedResourceWithSku',
+    'PartialSku',
+    'Password',
+    'PersonalComputeInstanceSettings',
+    'PipelineJob',
+    'PrivateEndpoint',
+    'PrivateEndpointConnection',
+    'PrivateEndpointConnectionListResult',
+    'PrivateLinkResource',
+    'PrivateLinkResourceListResult',
+    'PrivateLinkServiceConnectionState',
+    'ProbeSettings',
+    'PyTorch',
+    'QuotaBaseProperties',
+    'QuotaUpdateParameters',
+    'RandomSamplingAlgorithm',
+    'RecurrenceSchedule',
+    'RecurrenceTrigger',
+    'RegenerateEndpointKeysRequest',
+    'RegistryListCredentialsResult',
+    'Regression',
+    'RegressionTrainingSettings',
+    'Resource',
+    'ResourceBase',
+    'ResourceConfiguration',
+    'ResourceId',
+    'ResourceName',
+    'ResourceQuota',
+    'Route',
+    'SASAuthTypeWorkspaceConnectionProperties',
+    'SamplingAlgorithm',
+    'SasDatastoreCredentials',
+    'SasDatastoreSecrets',
+    'ScaleSettings',
+    'ScaleSettingsInformation',
+    'Schedule',
+    'ScheduleActionBase',
+    'ScheduleBase',
+    'ScheduleProperties',
+    'ScheduleResourceArmPaginatedResult',
+    'ScriptReference',
+    'ScriptsToExecute',
+    'Seasonality',
+    'ServiceManagedResourcesSettings',
+    'ServicePrincipalDatastoreCredentials',
+    'ServicePrincipalDatastoreSecrets',
+    'SetupScripts',
+    'SharedPrivateLinkResource',
+    'Sku',
+    'SkuCapacity',
+    'SkuResource',
+    'SkuResourceArmPaginatedResult',
+    'SkuSetting',
+    'SslConfiguration',
+    'StackEnsembleSettings',
+    'SweepJob',
+    'SweepJobLimits',
+    'SynapseSpark',
+    'SynapseSparkProperties',
+    'SystemData',
+    'SystemService',
+    'TableVertical',
+    'TableVerticalFeaturizationSettings',
+    'TableVerticalLimitSettings',
+    'TargetLags',
+    'TargetRollingWindowSize',
+    'TargetUtilizationScaleSettings',
+    'TensorFlow',
+    'TextClassification',
+    'TextClassificationMultilabel',
+    'TextNer',
+    'TrackedResource',
+    'TrainingSettings',
+    'TrialComponent',
+    'TriggerBase',
+    'TritonModelJobInput',
+    'TritonModelJobOutput',
+    'TruncationSelectionPolicy',
+    'UpdateWorkspaceQuotas',
+    'UpdateWorkspaceQuotasResult',
+    'UriFileDataVersion',
+    'UriFileJobInput',
+    'UriFileJobOutput',
+    'UriFolderDataVersion',
+    'UriFolderJobInput',
+    'UriFolderJobOutput',
+    'Usage',
+    'UsageName',
+    'UserAccountCredentials',
+    'UserAssignedIdentity',
+    'UserIdentity',
+    'UsernamePasswordAuthTypeWorkspaceConnectionProperties',
+    'VirtualMachine',
+    'VirtualMachineImage',
+    'VirtualMachineSchema',
+    'VirtualMachineSchemaProperties',
+    'VirtualMachineSecrets',
+    'VirtualMachineSecretsSchema',
+    'VirtualMachineSize',
+    'VirtualMachineSizeListResult',
+    'VirtualMachineSshCredentials',
+    'Workspace',
+    'WorkspaceConnectionManagedIdentity',
+    'WorkspaceConnectionPersonalAccessToken',
+    'WorkspaceConnectionPropertiesV2',
+    'WorkspaceConnectionPropertiesV2BasicResource',
+    'WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult',
+    'WorkspaceConnectionSharedAccessSignature',
+    'WorkspaceConnectionUsernamePassword',
+    'WorkspaceListResult',
+    'WorkspaceUpdateParameters',
+    'AllocationState',
+    'ApplicationSharingPolicy',
+    'AutoRebuildSetting',
+    'Autosave',
+    'BatchLoggingLevel',
+    'BatchOutputAction',
+    'BillingCurrency',
+    'BlockedTransformers',
+    'Caching',
+    'ClassificationModels',
+    'ClassificationMultilabelPrimaryMetrics',
+    'ClassificationPrimaryMetrics',
+    'ClusterPurpose',
+    'ComputeInstanceAuthorizationType',
+    'ComputeInstanceState',
+    'ComputePowerAction',
+    'ComputeType',
+    'ConnectionAuthType',
+    'ConnectionCategory',
+    'ContainerType',
+    'CreatedByType',
+    'CredentialsType',
+    'DataType',
+    'DatastoreType',
+    'DeploymentProvisioningState',
+    'DiagnoseResultLevel',
+    'DistributionType',
+    'EarlyTerminationPolicyType',
+    'EgressPublicNetworkAccessType',
+    'EncryptionStatus',
+    'EndpointAuthMode',
+    'EndpointComputeType',
+    'EndpointProvisioningState',
+    'EnvironmentType',
+    'FeatureLags',
+    'FeaturizationMode',
+    'ForecastHorizonMode',
+    'ForecastingModels',
+    'ForecastingPrimaryMetrics',
+    'Goal',
+    'IdentityConfigurationType',
+    'InputDeliveryMode',
+    'InstanceSegmentationPrimaryMetrics',
+    'JobInputType',
+    'JobLimitsType',
+    'JobOutputType',
+    'JobStatus',
+    'JobType',
+    'KeyType',
+    'LearningRateScheduler',
+    'ListViewType',
+    'LoadBalancerType',
+    'LogVerbosity',
+    'ManagedServiceIdentityType',
+    'ModelSize',
+    'MountAction',
+    'MountState',
+    'NCrossValidationsMode',
+    'Network',
+    'NodeState',
+    'ObjectDetectionPrimaryMetrics',
+    'OperatingSystemType',
+    'OperationName',
+    'OperationStatus',
+    'OperationTrigger',
+    'OrderString',
+    'OsType',
+    'OutputDeliveryMode',
+    'PrivateEndpointConnectionProvisioningState',
+    'PrivateEndpointServiceConnectionStatus',
+    'ProvisioningState',
+    'ProvisioningStatus',
+    'PublicNetworkAccess',
+    'PublicNetworkAccessType',
+    'QuotaUnit',
+    'RandomSamplingAlgorithmRule',
+    'RecurrenceFrequency',
+    'ReferenceType',
+    'RegressionModels',
+    'RegressionPrimaryMetrics',
+    'RemoteLoginPortPublicAccess',
+    'SamplingAlgorithmType',
+    'ScaleType',
+    'ScheduleActionType',
+    'ScheduleListViewType',
+    'ScheduleProvisioningState',
+    'ScheduleProvisioningStatus',
+    'ScheduleStatus',
+    'SeasonalityMode',
+    'SecretsType',
+    'ServiceDataAccessAuthIdentity',
+    'ShortSeriesHandlingConfiguration',
+    'SkuScaleType',
+    'SkuTier',
+    'SourceType',
+    'SshPublicAccess',
+    'SslConfigStatus',
+    'StackMetaLearnerType',
+    'Status',
+    'StochasticOptimizer',
+    'StorageAccountType',
+    'TargetAggregationFunction',
+    'TargetLagsMode',
+    'TargetRollingWindowSizeMode',
+    'TaskType',
+    'TriggerType',
+    'UnderlyingResourceAction',
+    'UnitOfMeasure',
+    'UsageUnit',
+    'UseStl',
+    'VMPriceOSType',
+    'VMTier',
+    'ValidationMetricType',
+    'ValueFormat',
+    'VmPriority',
+    'WeekDay',
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/_azure_machine_learning_workspaces_enums.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/_azure_machine_learning_workspaces_enums.py
new file mode 100644
index 00000000..53ff52db
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/_azure_machine_learning_workspaces_enums.py
@@ -0,0 +1,1344 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from enum import Enum
+from azure.core import CaseInsensitiveEnumMeta
+
+
+class AllocationState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Allocation state of the compute. Possible values are: steady - Indicates that the compute is
+    not resizing. There are no changes to the number of compute nodes in the compute in progress. A
+    compute enters this state when it is created and when no operations are being performed on the
+    compute to change the number of compute nodes. resizing - Indicates that the compute is
+    resizing; that is, compute nodes are being added to or removed from the compute.
+    """
+
+    STEADY = "Steady"
+    RESIZING = "Resizing"
+
+class ApplicationSharingPolicy(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Policy for sharing applications on this compute instance among users of parent workspace. If
+    Personal, only the creator can access applications on this compute instance. When Shared, any
+    workspace user can access applications on this instance depending on his/her assigned role.
+    """
+
+    PERSONAL = "Personal"
+    SHARED = "Shared"
+
+class AutoRebuildSetting(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """AutoRebuild setting for the derived image
+    """
+
+    DISABLED = "Disabled"
+    ON_BASE_IMAGE_UPDATE = "OnBaseImageUpdate"
+
+class Autosave(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Auto save settings.
+    """
+
+    NONE = "None"
+    LOCAL = "Local"
+    REMOTE = "Remote"
+
+class BatchLoggingLevel(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Log verbosity for batch inferencing.
+    Increasing verbosity order for logging is : Warning, Info and Debug.
+    The default value is Info.
+    """
+
+    INFO = "Info"
+    WARNING = "Warning"
+    DEBUG = "Debug"
+
+class BatchOutputAction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine how batch inferencing will handle output
+    """
+
+    SUMMARY_ONLY = "SummaryOnly"
+    APPEND_ROW = "AppendRow"
+
+class BillingCurrency(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Three lettered code specifying the currency of the VM price. Example: USD
+    """
+
+    USD = "USD"
+
+class BlockedTransformers(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum for all classification models supported by AutoML.
+    """
+
+    #: Target encoding for text data.
+    TEXT_TARGET_ENCODER = "TextTargetEncoder"
+    #: Ohe hot encoding creates a binary feature transformation.
+    ONE_HOT_ENCODER = "OneHotEncoder"
+    #: Target encoding for categorical data.
+    CAT_TARGET_ENCODER = "CatTargetEncoder"
+    #: Tf-Idf stands for, term-frequency times inverse document-frequency. This is a common term
+    #: weighting scheme for identifying information from documents.
+    TF_IDF = "TfIdf"
+    #: Weight of Evidence encoding is a technique used to encode categorical variables. It uses the
+    #: natural log of the P(1)/P(0) to create weights.
+    WO_E_TARGET_ENCODER = "WoETargetEncoder"
+    #: Label encoder converts labels/categorical variables in a numerical form.
+    LABEL_ENCODER = "LabelEncoder"
+    #: Word embedding helps represents words or phrases as a vector, or a series of numbers.
+    WORD_EMBEDDING = "WordEmbedding"
+    #: Naive Bayes is a classified that is used for classification of discrete features that are
+    #: categorically distributed.
+    NAIVE_BAYES = "NaiveBayes"
+    #: Count Vectorizer converts a collection of text documents to a matrix of token counts.
+    COUNT_VECTORIZER = "CountVectorizer"
+    #: Hashing One Hot Encoder can turn categorical variables into a limited number of new features.
+    #: This is often used for high-cardinality categorical features.
+    HASH_ONE_HOT_ENCODER = "HashOneHotEncoder"
+
+class Caching(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Caching type of Data Disk.
+    """
+
+    NONE = "None"
+    READ_ONLY = "ReadOnly"
+    READ_WRITE = "ReadWrite"
+
+class ClassificationModels(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum for all classification models supported by AutoML.
+    """
+
+    #: Logistic regression is a fundamental classification technique.
+    #: It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear
+    #: regression.
+    #: Logistic regression is fast and relatively uncomplicated, and it's convenient for you to
+    #: interpret the results.
+    #: Although it's essentially a method for binary classification, it can also be applied to
+    #: multiclass problems.
+    LOGISTIC_REGRESSION = "LogisticRegression"
+    #: SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
+    #: applications
+    #: to find the model parameters that correspond to the best fit between predicted and actual
+    #: outputs.
+    SGD = "SGD"
+    #: The multinomial Naive Bayes classifier is suitable for classification with discrete features
+    #: (e.g., word counts for text classification).
+    #: The multinomial distribution normally requires integer feature counts. However, in practice,
+    #: fractional counts such as tf-idf may also work.
+    MULTINOMIAL_NAIVE_BAYES = "MultinomialNaiveBayes"
+    #: Naive Bayes classifier for multivariate Bernoulli models.
+    BERNOULLI_NAIVE_BAYES = "BernoulliNaiveBayes"
+    #: A support vector machine (SVM) is a supervised machine learning model that uses classification
+    #: algorithms for two-group classification problems.
+    #: After giving an SVM model sets of labeled training data for each category, they're able to
+    #: categorize new text.
+    SVM = "SVM"
+    #: A support vector machine (SVM) is a supervised machine learning model that uses classification
+    #: algorithms for two-group classification problems.
+    #: After giving an SVM model sets of labeled training data for each category, they're able to
+    #: categorize new text.
+    #: Linear SVM performs best when input data is linear, i.e., data can be easily classified by
+    #: drawing the straight line between classified values on a plotted graph.
+    LINEAR_SVM = "LinearSVM"
+    #: K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
+    #: datapoints
+    #: which further means that the new data point will be assigned a value based on how closely it
+    #: matches the points in the training set.
+    KNN = "KNN"
+    #: Decision Trees are a non-parametric supervised learning method used for both classification and
+    #: regression tasks.
+    #: The goal is to create a model that predicts the value of a target variable by learning simple
+    #: decision rules inferred from the data features.
+    DECISION_TREE = "DecisionTree"
+    #: Random forest is a supervised learning algorithm.
+    #: The "forest"\\   it builds, is an ensemble of decision trees, usually trained with the
+    #: �bagging�\\   method.
+    #: The general idea of the bagging method is that a combination of learning models increases the
+    #: overall result.
+    RANDOM_FOREST = "RandomForest"
+    #: Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
+    #: decision trees. It is related to the widely used random forest algorithm.
+    EXTREME_RANDOM_TREES = "ExtremeRandomTrees"
+    #: LightGBM is a gradient boosting framework that uses tree based learning algorithms.
+    LIGHT_GBM = "LightGBM"
+    #: The technique of transiting week learners into a strong learner is called Boosting. The
+    #: gradient boosting algorithm process works on this theory of execution.
+    GRADIENT_BOOSTING = "GradientBoosting"
+    #: XGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where
+    #: target column values can be divided into distinct class values.
+    XG_BOOST_CLASSIFIER = "XGBoostClassifier"
+
+class ClassificationMultilabelPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Primary metrics for classification multilabel tasks.
+    """
+
+    #: AUC is the Area under the curve.
+    #: This metric represents arithmetic mean of the score for each class,
+    #: weighted by the number of true instances in each class.
+    AUC_WEIGHTED = "AUCWeighted"
+    #: Accuracy is the ratio of predictions that exactly match the true class labels.
+    ACCURACY = "Accuracy"
+    #: Normalized macro recall is recall macro-averaged and normalized, so that random
+    #: performance has a score of 0, and perfect performance has a score of 1.
+    NORM_MACRO_RECALL = "NormMacroRecall"
+    #: The arithmetic mean of the average precision score for each class, weighted by
+    #: the number of true instances in each class.
+    AVERAGE_PRECISION_SCORE_WEIGHTED = "AveragePrecisionScoreWeighted"
+    #: The arithmetic mean of precision for each class, weighted by number of true instances in each
+    #: class.
+    PRECISION_SCORE_WEIGHTED = "PrecisionScoreWeighted"
+    #: Intersection Over Union. Intersection of predictions divided by union of predictions.
+    IOU = "IOU"
+
+class ClassificationPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Primary metrics for classification tasks.
+    """
+
+    #: AUC is the Area under the curve.
+    #: This metric represents arithmetic mean of the score for each class,
+    #: weighted by the number of true instances in each class.
+    AUC_WEIGHTED = "AUCWeighted"
+    #: Accuracy is the ratio of predictions that exactly match the true class labels.
+    ACCURACY = "Accuracy"
+    #: Normalized macro recall is recall macro-averaged and normalized, so that random
+    #: performance has a score of 0, and perfect performance has a score of 1.
+    NORM_MACRO_RECALL = "NormMacroRecall"
+    #: The arithmetic mean of the average precision score for each class, weighted by
+    #: the number of true instances in each class.
+    AVERAGE_PRECISION_SCORE_WEIGHTED = "AveragePrecisionScoreWeighted"
+    #: The arithmetic mean of precision for each class, weighted by number of true instances in each
+    #: class.
+    PRECISION_SCORE_WEIGHTED = "PrecisionScoreWeighted"
+
+class ClusterPurpose(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Intended usage of the cluster
+    """
+
+    FAST_PROD = "FastProd"
+    DENSE_PROD = "DenseProd"
+    DEV_TEST = "DevTest"
+
+class ComputeInstanceAuthorizationType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The Compute Instance Authorization type. Available values are personal (default).
+    """
+
+    PERSONAL = "personal"
+
+class ComputeInstanceState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Current state of an ComputeInstance.
+    """
+
+    CREATING = "Creating"
+    CREATE_FAILED = "CreateFailed"
+    DELETING = "Deleting"
+    RUNNING = "Running"
+    RESTARTING = "Restarting"
+    JOB_RUNNING = "JobRunning"
+    SETTING_UP = "SettingUp"
+    SETUP_FAILED = "SetupFailed"
+    STARTING = "Starting"
+    STOPPED = "Stopped"
+    STOPPING = "Stopping"
+    USER_SETTING_UP = "UserSettingUp"
+    USER_SETUP_FAILED = "UserSetupFailed"
+    UNKNOWN = "Unknown"
+    UNUSABLE = "Unusable"
+
+class ComputePowerAction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The compute power action.
+    """
+
+    START = "Start"
+    STOP = "Stop"
+
+class ComputeType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The type of compute
+    """
+
+    AKS = "AKS"
+    KUBERNETES = "Kubernetes"
+    AML_COMPUTE = "AmlCompute"
+    COMPUTE_INSTANCE = "ComputeInstance"
+    DATA_FACTORY = "DataFactory"
+    VIRTUAL_MACHINE = "VirtualMachine"
+    HD_INSIGHT = "HDInsight"
+    DATABRICKS = "Databricks"
+    DATA_LAKE_ANALYTICS = "DataLakeAnalytics"
+    SYNAPSE_SPARK = "SynapseSpark"
+
+class ConnectionAuthType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Authentication type of the connection target
+    """
+
+    PAT = "PAT"
+    MANAGED_IDENTITY = "ManagedIdentity"
+    USERNAME_PASSWORD = "UsernamePassword"
+    NONE = "None"
+    SAS = "SAS"
+
+class ConnectionCategory(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Category of the connection
+    """
+
+    PYTHON_FEED = "PythonFeed"
+    CONTAINER_REGISTRY = "ContainerRegistry"
+    GIT = "Git"
+
+class ContainerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    STORAGE_INITIALIZER = "StorageInitializer"
+    INFERENCE_SERVER = "InferenceServer"
+
+class CreatedByType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The type of identity that created the resource.
+    """
+
+    USER = "User"
+    APPLICATION = "Application"
+    MANAGED_IDENTITY = "ManagedIdentity"
+    KEY = "Key"
+
+class CredentialsType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the datastore credentials type.
+    """
+
+    ACCOUNT_KEY = "AccountKey"
+    CERTIFICATE = "Certificate"
+    NONE = "None"
+    SAS = "Sas"
+    SERVICE_PRINCIPAL = "ServicePrincipal"
+
+class DatastoreType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the datastore contents type.
+    """
+
+    AZURE_BLOB = "AzureBlob"
+    AZURE_DATA_LAKE_GEN1 = "AzureDataLakeGen1"
+    AZURE_DATA_LAKE_GEN2 = "AzureDataLakeGen2"
+    AZURE_FILE = "AzureFile"
+
+class DataType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the type of data.
+    """
+
+    URI_FILE = "uri_file"
+    URI_FOLDER = "uri_folder"
+    MLTABLE = "mltable"
+
+class DeploymentProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Possible values for DeploymentProvisioningState.
+    """
+
+    CREATING = "Creating"
+    DELETING = "Deleting"
+    SCALING = "Scaling"
+    UPDATING = "Updating"
+    SUCCEEDED = "Succeeded"
+    FAILED = "Failed"
+    CANCELED = "Canceled"
+
+class DiagnoseResultLevel(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Level of workspace setup error
+    """
+
+    WARNING = "Warning"
+    ERROR = "Error"
+    INFORMATION = "Information"
+
+class DistributionType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the job distribution type.
+    """
+
+    PY_TORCH = "PyTorch"
+    TENSOR_FLOW = "TensorFlow"
+    MPI = "Mpi"
+
+class EarlyTerminationPolicyType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    BANDIT = "Bandit"
+    MEDIAN_STOPPING = "MedianStopping"
+    TRUNCATION_SELECTION = "TruncationSelection"
+
+class EgressPublicNetworkAccessType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine whether PublicNetworkAccess is Enabled or Disabled for egress of a
+    deployment.
+    """
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+class EncryptionStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Indicates whether or not the encryption is enabled for the workspace.
+    """
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+class EndpointAuthMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine endpoint authentication mode.
+    """
+
+    AML_TOKEN = "AMLToken"
+    KEY = "Key"
+    AAD_TOKEN = "AADToken"
+
+class EndpointComputeType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine endpoint compute type.
+    """
+
+    MANAGED = "Managed"
+    KUBERNETES = "Kubernetes"
+    AZURE_ML_COMPUTE = "AzureMLCompute"
+
+class EndpointProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """State of endpoint provisioning.
+    """
+
+    CREATING = "Creating"
+    DELETING = "Deleting"
+    SUCCEEDED = "Succeeded"
+    FAILED = "Failed"
+    UPDATING = "Updating"
+    CANCELED = "Canceled"
+
+class EnvironmentType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Environment type is either user created or curated by Azure ML service
+    """
+
+    CURATED = "Curated"
+    USER_CREATED = "UserCreated"
+
+class FeatureLags(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Flag for generating lags for the numeric features.
+    """
+
+    #: No feature lags generated.
+    NONE = "None"
+    #: System auto-generates feature lags.
+    AUTO = "Auto"
+
+class FeaturizationMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Featurization mode - determines data featurization mode.
+    """
+
+    #: Auto mode, system performs featurization without any custom featurization inputs.
+    AUTO = "Auto"
+    #: Custom featurization.
+    CUSTOM = "Custom"
+    #: Featurization off. 'Forecasting' task cannot use this value.
+    OFF = "Off"
+
+class ForecastHorizonMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine forecast horizon selection mode.
+    """
+
+    #: Forecast horizon to be determined automatically.
+    AUTO = "Auto"
+    #: Use the custom forecast horizon.
+    CUSTOM = "Custom"
+
+class ForecastingModels(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum for all forecasting models supported by AutoML.
+    """
+
+    #: Auto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and
+    #: statistical analysis to interpret the data and make future predictions.
+    #: This model aims to explain data by using time series data on its past values and uses linear
+    #: regression to make predictions.
+    AUTO_ARIMA = "AutoArima"
+    #: Prophet is a procedure for forecasting time series data based on an additive model where
+    #: non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
+    #: It works best with time series that have strong seasonal effects and several seasons of
+    #: historical data. Prophet is robust to missing data and shifts in the trend, and typically
+    #: handles outliers well.
+    PROPHET = "Prophet"
+    #: The Naive forecasting model makes predictions by carrying forward the latest target value for
+    #: each time-series in the training data.
+    NAIVE = "Naive"
+    #: The Seasonal Naive forecasting model makes predictions by carrying forward the latest season of
+    #: target values for each time-series in the training data.
+    SEASONAL_NAIVE = "SeasonalNaive"
+    #: The Average forecasting model makes predictions by carrying forward the average of the target
+    #: values for each time-series in the training data.
+    AVERAGE = "Average"
+    #: The Seasonal Average forecasting model makes predictions by carrying forward the average value
+    #: of the latest season of data for each time-series in the training data.
+    SEASONAL_AVERAGE = "SeasonalAverage"
+    #: Exponential smoothing is a time series forecasting method for univariate data that can be
+    #: extended to support data with a systematic trend or seasonal component.
+    EXPONENTIAL_SMOOTHING = "ExponentialSmoothing"
+    #: An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be
+    #: viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or
+    #: more moving average (MA) terms.
+    #: This method is suitable for forecasting when data is stationary/non stationary, and
+    #: multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.
+    ARIMAX = "Arimax"
+    #: TCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for
+    #: brief intro.
+    TCN_FORECASTER = "TCNForecaster"
+    #: Elastic net is a popular type of regularized linear regression that combines two popular
+    #: penalties, specifically the L1 and L2 penalty functions.
+    ELASTIC_NET = "ElasticNet"
+    #: The technique of transiting week learners into a strong learner is called Boosting. The
+    #: gradient boosting algorithm process works on this theory of execution.
+    GRADIENT_BOOSTING = "GradientBoosting"
+    #: Decision Trees are a non-parametric supervised learning method used for both classification and
+    #: regression tasks.
+    #: The goal is to create a model that predicts the value of a target variable by learning simple
+    #: decision rules inferred from the data features.
+    DECISION_TREE = "DecisionTree"
+    #: K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
+    #: datapoints
+    #: which further means that the new data point will be assigned a value based on how closely it
+    #: matches the points in the training set.
+    KNN = "KNN"
+    #: Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an
+    #: L1 prior as regularizer.
+    LASSO_LARS = "LassoLars"
+    #: SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
+    #: applications
+    #: to find the model parameters that correspond to the best fit between predicted and actual
+    #: outputs.
+    #: It's an inexact but powerful technique.
+    SGD = "SGD"
+    #: Random forest is a supervised learning algorithm.
+    #: The "forest"\\   it builds, is an ensemble of decision trees, usually trained with the
+    #: �bagging�\\   method.
+    #: The general idea of the bagging method is that a combination of learning models increases the
+    #: overall result.
+    RANDOM_FOREST = "RandomForest"
+    #: Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
+    #: decision trees. It is related to the widely used random forest algorithm.
+    EXTREME_RANDOM_TREES = "ExtremeRandomTrees"
+    #: LightGBM is a gradient boosting framework that uses tree based learning algorithms.
+    LIGHT_GBM = "LightGBM"
+    #: XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model
+    #: using ensemble of base learners.
+    XG_BOOST_REGRESSOR = "XGBoostRegressor"
+
+class ForecastingPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Primary metrics for Forecasting task.
+    """
+
+    #: The Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.
+    SPEARMAN_CORRELATION = "SpearmanCorrelation"
+    #: The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between
+    #: models with different scales.
+    NORMALIZED_ROOT_MEAN_SQUARED_ERROR = "NormalizedRootMeanSquaredError"
+    #: The R2 score is one of the performance evaluation measures for forecasting-based machine
+    #: learning models.
+    R2_SCORE = "R2Score"
+    #: The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute
+    #: Error (MAE) of (time) series with different scales.
+    NORMALIZED_MEAN_ABSOLUTE_ERROR = "NormalizedMeanAbsoluteError"
+
+class Goal(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Defines supported metric goals for hyperparameter tuning
+    """
+
+    MINIMIZE = "Minimize"
+    MAXIMIZE = "Maximize"
+
+class IdentityConfigurationType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine identity framework.
+    """
+
+    MANAGED = "Managed"
+    AML_TOKEN = "AMLToken"
+    USER_IDENTITY = "UserIdentity"
+
+class InputDeliveryMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the input data delivery mode.
+    """
+
+    READ_ONLY_MOUNT = "ReadOnlyMount"
+    READ_WRITE_MOUNT = "ReadWriteMount"
+    DOWNLOAD = "Download"
+    DIRECT = "Direct"
+    EVAL_MOUNT = "EvalMount"
+    EVAL_DOWNLOAD = "EvalDownload"
+
+class InstanceSegmentationPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Primary metrics for InstanceSegmentation tasks.
+    """
+
+    #: Mean Average Precision (MAP) is the average of AP (Average Precision).
+    #: AP is calculated for each class and averaged to get the MAP.
+    MEAN_AVERAGE_PRECISION = "MeanAveragePrecision"
+
+class JobInputType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the Job Input Type.
+    """
+
+    LITERAL = "literal"
+    URI_FILE = "uri_file"
+    URI_FOLDER = "uri_folder"
+    MLTABLE = "mltable"
+    CUSTOM_MODEL = "custom_model"
+    MLFLOW_MODEL = "mlflow_model"
+    TRITON_MODEL = "triton_model"
+
+class JobLimitsType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    COMMAND = "Command"
+    SWEEP = "Sweep"
+
+class JobOutputType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the Job Output Type.
+    """
+
+    URI_FILE = "uri_file"
+    URI_FOLDER = "uri_folder"
+    MLTABLE = "mltable"
+    CUSTOM_MODEL = "custom_model"
+    MLFLOW_MODEL = "mlflow_model"
+    TRITON_MODEL = "triton_model"
+
+class JobStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The status of a job.
+    """
+
+    #: Run hasn't started yet.
+    NOT_STARTED = "NotStarted"
+    #: Run has started. The user has a run ID.
+    STARTING = "Starting"
+    #: (Not used currently) It will be used if ES is creating the compute target.
+    PROVISIONING = "Provisioning"
+    #: The run environment is being prepared.
+    PREPARING = "Preparing"
+    #: The job is queued in the compute target. For example, in BatchAI the job is in queued state,
+    #: while waiting for all required nodes to be ready.
+    QUEUED = "Queued"
+    #: The job started to run in the compute target.
+    RUNNING = "Running"
+    #: Job is completed in the target. It is in output collection state now.
+    FINALIZING = "Finalizing"
+    #: Cancellation has been requested for the job.
+    CANCEL_REQUESTED = "CancelRequested"
+    #: Job completed successfully. This reflects that both the job itself and output collection states
+    #: completed successfully.
+    COMPLETED = "Completed"
+    #: Job failed.
+    FAILED = "Failed"
+    #: Following cancellation request, the job is now successfully canceled.
+    CANCELED = "Canceled"
+    #: When heartbeat is enabled, if the run isn't updating any information to RunHistory then the run
+    #: goes to NotResponding state.
+    #: NotResponding is the only state that is exempt from strict transition orders. A run can go from
+    #: NotResponding to any of the previous states.
+    NOT_RESPONDING = "NotResponding"
+    #: The job is paused by users. Some adjustment to labeling jobs can be made only in paused state.
+    PAUSED = "Paused"
+    #: Default job status if not mapped to all other statuses.
+    UNKNOWN = "Unknown"
+
+class JobType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the type of job.
+    """
+
+    AUTO_ML = "AutoML"
+    COMMAND = "Command"
+    SWEEP = "Sweep"
+    PIPELINE = "Pipeline"
+
+class KeyType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    PRIMARY = "Primary"
+    SECONDARY = "Secondary"
+
+class LearningRateScheduler(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Learning rate scheduler enum.
+    """
+
+    #: No learning rate scheduler selected.
+    NONE = "None"
+    #: Cosine Annealing With Warmup.
+    WARMUP_COSINE = "WarmupCosine"
+    #: Step learning rate scheduler.
+    STEP = "Step"
+
+class ListViewType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    ACTIVE_ONLY = "ActiveOnly"
+    ARCHIVED_ONLY = "ArchivedOnly"
+    ALL = "All"
+
+class LoadBalancerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Load Balancer Type
+    """
+
+    PUBLIC_IP = "PublicIp"
+    INTERNAL_LOAD_BALANCER = "InternalLoadBalancer"
+
+class LogVerbosity(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum for setting log verbosity.
+    """
+
+    #: No logs emitted.
+    NOT_SET = "NotSet"
+    #: Debug and above log statements logged.
+    DEBUG = "Debug"
+    #: Info and above log statements logged.
+    INFO = "Info"
+    #: Warning and above log statements logged.
+    WARNING = "Warning"
+    #: Error and above log statements logged.
+    ERROR = "Error"
+    #: Only critical statements logged.
+    CRITICAL = "Critical"
+
+class ManagedServiceIdentityType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Type of managed service identity (where both SystemAssigned and UserAssigned types are
+    allowed).
+    """
+
+    NONE = "None"
+    SYSTEM_ASSIGNED = "SystemAssigned"
+    USER_ASSIGNED = "UserAssigned"
+    SYSTEM_ASSIGNED_USER_ASSIGNED = "SystemAssigned,UserAssigned"
+
+class ModelSize(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Image model size.
+    """
+
+    #: No value selected.
+    NONE = "None"
+    #: Small size.
+    SMALL = "Small"
+    #: Medium size.
+    MEDIUM = "Medium"
+    #: Large size.
+    LARGE = "Large"
+    #: Extra large size.
+    EXTRA_LARGE = "ExtraLarge"
+
+class MountAction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Mount Action.
+    """
+
+    MOUNT = "Mount"
+    UNMOUNT = "Unmount"
+
+class MountState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Mount state.
+    """
+
+    MOUNT_REQUESTED = "MountRequested"
+    MOUNTED = "Mounted"
+    MOUNT_FAILED = "MountFailed"
+    UNMOUNT_REQUESTED = "UnmountRequested"
+    UNMOUNT_FAILED = "UnmountFailed"
+    UNMOUNTED = "Unmounted"
+
+class NCrossValidationsMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Determines how N-Cross validations value is determined.
+    """
+
+    #: Determine N-Cross validations value automatically. Supported only for 'Forecasting' AutoML
+    #: task.
+    AUTO = "Auto"
+    #: Use custom N-Cross validations value.
+    CUSTOM = "Custom"
+
+class Network(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """network of this container.
+    """
+
+    BRIDGE = "Bridge"
+    HOST = "Host"
+
+class NodeState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """State of the compute node. Values are idle, running, preparing, unusable, leaving and
+    preempted.
+    """
+
+    IDLE = "idle"
+    RUNNING = "running"
+    PREPARING = "preparing"
+    UNUSABLE = "unusable"
+    LEAVING = "leaving"
+    PREEMPTED = "preempted"
+
+class ObjectDetectionPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Primary metrics for Image ObjectDetection task.
+    """
+
+    #: Mean Average Precision (MAP) is the average of AP (Average Precision).
+    #: AP is calculated for each class and averaged to get the MAP.
+    MEAN_AVERAGE_PRECISION = "MeanAveragePrecision"
+
+class OperatingSystemType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The type of operating system.
+    """
+
+    LINUX = "Linux"
+    WINDOWS = "Windows"
+
+class OperationName(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Name of the last operation.
+    """
+
+    CREATE = "Create"
+    START = "Start"
+    STOP = "Stop"
+    RESTART = "Restart"
+    REIMAGE = "Reimage"
+    DELETE = "Delete"
+
+class OperationStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Operation status.
+    """
+
+    IN_PROGRESS = "InProgress"
+    SUCCEEDED = "Succeeded"
+    CREATE_FAILED = "CreateFailed"
+    START_FAILED = "StartFailed"
+    STOP_FAILED = "StopFailed"
+    RESTART_FAILED = "RestartFailed"
+    REIMAGE_FAILED = "ReimageFailed"
+    DELETE_FAILED = "DeleteFailed"
+
+class OperationTrigger(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Trigger of operation.
+    """
+
+    USER = "User"
+    SCHEDULE = "Schedule"
+    IDLE_SHUTDOWN = "IdleShutdown"
+
+class OrderString(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    CREATED_AT_DESC = "CreatedAtDesc"
+    CREATED_AT_ASC = "CreatedAtAsc"
+    UPDATED_AT_DESC = "UpdatedAtDesc"
+    UPDATED_AT_ASC = "UpdatedAtAsc"
+
+class OsType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Compute OS Type
+    """
+
+    LINUX = "Linux"
+    WINDOWS = "Windows"
+
+class OutputDeliveryMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Output data delivery mode enums.
+    """
+
+    READ_WRITE_MOUNT = "ReadWriteMount"
+    UPLOAD = "Upload"
+
+class PrivateEndpointConnectionProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The current provisioning state.
+    """
+
+    SUCCEEDED = "Succeeded"
+    CREATING = "Creating"
+    DELETING = "Deleting"
+    FAILED = "Failed"
+
+class PrivateEndpointServiceConnectionStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The private endpoint connection status.
+    """
+
+    PENDING = "Pending"
+    APPROVED = "Approved"
+    REJECTED = "Rejected"
+    DISCONNECTED = "Disconnected"
+    TIMEOUT = "Timeout"
+
+class ProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The current deployment state of workspace resource. The provisioningState is to indicate states
+    for resource provisioning.
+    """
+
+    UNKNOWN = "Unknown"
+    UPDATING = "Updating"
+    CREATING = "Creating"
+    DELETING = "Deleting"
+    SUCCEEDED = "Succeeded"
+    FAILED = "Failed"
+    CANCELED = "Canceled"
+
+class ProvisioningStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The current deployment state of schedule.
+    """
+
+    COMPLETED = "Completed"
+    PROVISIONING = "Provisioning"
+    FAILED = "Failed"
+
+class PublicNetworkAccess(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Whether requests from Public Network are allowed.
+    """
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+class PublicNetworkAccessType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine whether PublicNetworkAccess is Enabled or Disabled.
+    """
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+class QuotaUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """An enum describing the unit of quota measurement.
+    """
+
+    COUNT = "Count"
+
+class RandomSamplingAlgorithmRule(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The specific type of random algorithm
+    """
+
+    RANDOM = "Random"
+    SOBOL = "Sobol"
+
+class RecurrenceFrequency(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to describe the frequency of a recurrence schedule
+    """
+
+    #: Minute frequency.
+    MINUTE = "Minute"
+    #: Hour frequency.
+    HOUR = "Hour"
+    #: Day frequency.
+    DAY = "Day"
+    #: Week frequency.
+    WEEK = "Week"
+    #: Month frequency.
+    MONTH = "Month"
+
+class ReferenceType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine which reference method to use for an asset.
+    """
+
+    ID = "Id"
+    DATA_PATH = "DataPath"
+    OUTPUT_PATH = "OutputPath"
+
+class RegressionModels(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum for all Regression models supported by AutoML.
+    """
+
+    #: Elastic net is a popular type of regularized linear regression that combines two popular
+    #: penalties, specifically the L1 and L2 penalty functions.
+    ELASTIC_NET = "ElasticNet"
+    #: The technique of transiting week learners into a strong learner is called Boosting. The
+    #: gradient boosting algorithm process works on this theory of execution.
+    GRADIENT_BOOSTING = "GradientBoosting"
+    #: Decision Trees are a non-parametric supervised learning method used for both classification and
+    #: regression tasks.
+    #: The goal is to create a model that predicts the value of a target variable by learning simple
+    #: decision rules inferred from the data features.
+    DECISION_TREE = "DecisionTree"
+    #: K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
+    #: datapoints
+    #: which further means that the new data point will be assigned a value based on how closely it
+    #: matches the points in the training set.
+    KNN = "KNN"
+    #: Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an
+    #: L1 prior as regularizer.
+    LASSO_LARS = "LassoLars"
+    #: SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
+    #: applications
+    #: to find the model parameters that correspond to the best fit between predicted and actual
+    #: outputs.
+    #: It's an inexact but powerful technique.
+    SGD = "SGD"
+    #: Random forest is a supervised learning algorithm.
+    #: The "forest"\\   it builds, is an ensemble of decision trees, usually trained with the
+    #: �bagging�\\   method.
+    #: The general idea of the bagging method is that a combination of learning models increases the
+    #: overall result.
+    RANDOM_FOREST = "RandomForest"
+    #: Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
+    #: decision trees. It is related to the widely used random forest algorithm.
+    EXTREME_RANDOM_TREES = "ExtremeRandomTrees"
+    #: LightGBM is a gradient boosting framework that uses tree based learning algorithms.
+    LIGHT_GBM = "LightGBM"
+    #: XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model
+    #: using ensemble of base learners.
+    XG_BOOST_REGRESSOR = "XGBoostRegressor"
+
+class RegressionPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Primary metrics for Regression task.
+    """
+
+    #: The Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.
+    SPEARMAN_CORRELATION = "SpearmanCorrelation"
+    #: The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between
+    #: models with different scales.
+    NORMALIZED_ROOT_MEAN_SQUARED_ERROR = "NormalizedRootMeanSquaredError"
+    #: The R2 score is one of the performance evaluation measures for forecasting-based machine
+    #: learning models.
+    R2_SCORE = "R2Score"
+    #: The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute
+    #: Error (MAE) of (time) series with different scales.
+    NORMALIZED_MEAN_ABSOLUTE_ERROR = "NormalizedMeanAbsoluteError"
+
+class RemoteLoginPortPublicAccess(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """State of the public SSH port. Possible values are: Disabled - Indicates that the public ssh
+    port is closed on all nodes of the cluster. Enabled - Indicates that the public ssh port is
+    open on all nodes of the cluster. NotSpecified - Indicates that the public ssh port is closed
+    on all nodes of the cluster if VNet is defined, else is open all public nodes. It can be
+    default only during cluster creation time, after creation it will be either enabled or
+    disabled.
+    """
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+    NOT_SPECIFIED = "NotSpecified"
+
+class SamplingAlgorithmType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    GRID = "Grid"
+    RANDOM = "Random"
+    BAYESIAN = "Bayesian"
+
+class ScaleType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    DEFAULT = "Default"
+    TARGET_UTILIZATION = "TargetUtilization"
+
+class ScheduleActionType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    CREATE_JOB = "CreateJob"
+    INVOKE_BATCH_ENDPOINT = "InvokeBatchEndpoint"
+
+class ScheduleListViewType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    ENABLED_ONLY = "EnabledOnly"
+    DISABLED_ONLY = "DisabledOnly"
+    ALL = "All"
+
+class ScheduleProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The current deployment state of schedule.
+    """
+
+    COMPLETED = "Completed"
+    PROVISIONING = "Provisioning"
+    FAILED = "Failed"
+
+class ScheduleProvisioningStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    CREATING = "Creating"
+    UPDATING = "Updating"
+    DELETING = "Deleting"
+    SUCCEEDED = "Succeeded"
+    FAILED = "Failed"
+    CANCELED = "Canceled"
+
+class ScheduleStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Is the schedule enabled or disabled?
+    """
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+class SeasonalityMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Forecasting seasonality mode.
+    """
+
+    #: Seasonality to be determined automatically.
+    AUTO = "Auto"
+    #: Use the custom seasonality value.
+    CUSTOM = "Custom"
+
+class SecretsType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the datastore secrets type.
+    """
+
+    ACCOUNT_KEY = "AccountKey"
+    CERTIFICATE = "Certificate"
+    SAS = "Sas"
+    SERVICE_PRINCIPAL = "ServicePrincipal"
+
+class ServiceDataAccessAuthIdentity(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    #: Do not use any identity for service data access.
+    NONE = "None"
+    #: Use the system assigned managed identity of the Workspace to authenticate service data access.
+    WORKSPACE_SYSTEM_ASSIGNED_IDENTITY = "WorkspaceSystemAssignedIdentity"
+    #: Use the user assigned managed identity of the Workspace to authenticate service data access.
+    WORKSPACE_USER_ASSIGNED_IDENTITY = "WorkspaceUserAssignedIdentity"
+
+class ShortSeriesHandlingConfiguration(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The parameter defining how if AutoML should handle short time series.
+    """
+
+    #: Represents no/null value.
+    NONE = "None"
+    #: Short series will be padded if there are no long series, otherwise short series will be
+    #: dropped.
+    AUTO = "Auto"
+    #: All the short series will be padded.
+    PAD = "Pad"
+    #: All the short series will be dropped.
+    DROP = "Drop"
+
+class SkuScaleType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Node scaling setting for the compute sku.
+    """
+
+    #: Automatically scales node count.
+    AUTOMATIC = "Automatic"
+    #: Node count scaled upon user request.
+    MANUAL = "Manual"
+    #: Fixed set of nodes.
+    NONE = "None"
+
+class SkuTier(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """This field is required to be implemented by the Resource Provider if the service has more than
+    one tier, but is not required on a PUT.
+    """
+
+    FREE = "Free"
+    BASIC = "Basic"
+    STANDARD = "Standard"
+    PREMIUM = "Premium"
+
+class SourceType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Data source type.
+    """
+
+    DATASET = "Dataset"
+    DATASTORE = "Datastore"
+    URI = "URI"
+
+class SshPublicAccess(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """State of the public SSH port. Possible values are: Disabled - Indicates that the public ssh
+    port is closed on this instance. Enabled - Indicates that the public ssh port is open and
+    accessible according to the VNet/subnet policy if applicable.
+    """
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+class SslConfigStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enable or disable ssl for scoring
+    """
+
+    DISABLED = "Disabled"
+    ENABLED = "Enabled"
+    AUTO = "Auto"
+
+class StackMetaLearnerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The meta-learner is a model trained on the output of the individual heterogeneous models.
+    Default meta-learners are LogisticRegression for classification tasks (or LogisticRegressionCV
+    if cross-validation is enabled) and ElasticNet for regression/forecasting tasks (or
+    ElasticNetCV if cross-validation is enabled).
+    This parameter can be one of the following strings: LogisticRegression, LogisticRegressionCV,
+    LightGBMClassifier, ElasticNet, ElasticNetCV, LightGBMRegressor, or LinearRegression
+    """
+
+    NONE = "None"
+    #: Default meta-learners are LogisticRegression for classification tasks.
+    LOGISTIC_REGRESSION = "LogisticRegression"
+    #: Default meta-learners are LogisticRegression for classification task when CV is on.
+    LOGISTIC_REGRESSION_CV = "LogisticRegressionCV"
+    LIGHT_GBM_CLASSIFIER = "LightGBMClassifier"
+    #: Default meta-learners are LogisticRegression for regression task.
+    ELASTIC_NET = "ElasticNet"
+    #: Default meta-learners are LogisticRegression for regression task when CV is on.
+    ELASTIC_NET_CV = "ElasticNetCV"
+    LIGHT_GBM_REGRESSOR = "LightGBMRegressor"
+    LINEAR_REGRESSION = "LinearRegression"
+
+class Status(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Status of update workspace quota.
+    """
+
+    UNDEFINED = "Undefined"
+    SUCCESS = "Success"
+    FAILURE = "Failure"
+    INVALID_QUOTA_BELOW_CLUSTER_MINIMUM = "InvalidQuotaBelowClusterMinimum"
+    INVALID_QUOTA_EXCEEDS_SUBSCRIPTION_LIMIT = "InvalidQuotaExceedsSubscriptionLimit"
+    INVALID_VM_FAMILY_NAME = "InvalidVMFamilyName"
+    OPERATION_NOT_SUPPORTED_FOR_SKU = "OperationNotSupportedForSku"
+    OPERATION_NOT_ENABLED_FOR_REGION = "OperationNotEnabledForRegion"
+
+class StochasticOptimizer(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Stochastic optimizer for image models.
+    """
+
+    #: No optimizer selected.
+    NONE = "None"
+    #: Stochastic Gradient Descent optimizer.
+    SGD = "Sgd"
+    #: Adam is algorithm the optimizes stochastic objective functions based on adaptive estimates of
+    #: moments.
+    ADAM = "Adam"
+    #: AdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.
+    ADAMW = "Adamw"
+
+class StorageAccountType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """type of this storage account.
+    """
+
+    STANDARD_LRS = "Standard_LRS"
+    PREMIUM_LRS = "Premium_LRS"
+
+class TargetAggregationFunction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Target aggregate function.
+    """
+
+    #: Represent no value set.
+    NONE = "None"
+    SUM = "Sum"
+    MAX = "Max"
+    MIN = "Min"
+    MEAN = "Mean"
+
+class TargetLagsMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Target lags selection modes.
+    """
+
+    #: Target lags to be determined automatically.
+    AUTO = "Auto"
+    #: Use the custom target lags.
+    CUSTOM = "Custom"
+
+class TargetRollingWindowSizeMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Target rolling windows size mode.
+    """
+
+    #: Determine rolling windows size automatically.
+    AUTO = "Auto"
+    #: Use the specified rolling window size.
+    CUSTOM = "Custom"
+
+class TaskType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """AutoMLJob Task type.
+    """
+
+    #: Classification in machine learning and statistics is a supervised learning approach in which
+    #: the computer program learns from the data given to it and make new observations or
+    #: classifications.
+    CLASSIFICATION = "Classification"
+    #: Regression means to predict the value using the input data. Regression models are used to
+    #: predict a continuous value.
+    REGRESSION = "Regression"
+    #: Forecasting is a special kind of regression task that deals with time-series data and creates
+    #: forecasting model
+    #: that can be used to predict the near future values based on the inputs.
+    FORECASTING = "Forecasting"
+    #: Image Classification. Multi-class image classification is used when an image is classified with
+    #: only a single label
+    #: from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog'
+    #: or a 'duck'.
+    IMAGE_CLASSIFICATION = "ImageClassification"
+    #: Image Classification Multilabel. Multi-label image classification is used when an image could
+    #: have one or more labels
+    #: from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.
+    IMAGE_CLASSIFICATION_MULTILABEL = "ImageClassificationMultilabel"
+    #: Image Object Detection. Object detection is used to identify objects in an image and locate
+    #: each object with a
+    #: bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.
+    IMAGE_OBJECT_DETECTION = "ImageObjectDetection"
+    #: Image Instance Segmentation. Instance segmentation is used to identify objects in an image at
+    #: the pixel level,
+    #: drawing a polygon around each object in the image.
+    IMAGE_INSTANCE_SEGMENTATION = "ImageInstanceSegmentation"
+    #: Text classification (also known as text tagging or text categorization) is the process of
+    #: sorting texts into categories.
+    #: Categories are mutually exclusive.
+    TEXT_CLASSIFICATION = "TextClassification"
+    #: Multilabel classification task assigns each sample to a group (zero or more) of target labels.
+    TEXT_CLASSIFICATION_MULTILABEL = "TextClassificationMultilabel"
+    #: Text Named Entity Recognition a.k.a. TextNER.
+    #: Named Entity Recognition (NER) is the ability to take free-form text and identify the
+    #: occurrences of entities such as people, locations, organizations, and more.
+    TEXT_NER = "TextNER"
+
+class TriggerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    RECURRENCE = "Recurrence"
+    CRON = "Cron"
+
+class UnderlyingResourceAction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+
+    DELETE = "Delete"
+    DETACH = "Detach"
+
+class UnitOfMeasure(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The unit of time measurement for the specified VM price. Example: OneHour
+    """
+
+    ONE_HOUR = "OneHour"
+
+class UsageUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """An enum describing the unit of usage measurement.
+    """
+
+    COUNT = "Count"
+
+class UseStl(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Configure STL Decomposition of the time-series target column.
+    """
+
+    #: No stl decomposition.
+    NONE = "None"
+    SEASON = "Season"
+    SEASON_TREND = "SeasonTrend"
+
+class ValidationMetricType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Metric computation method to use for validation metrics in image tasks.
+    """
+
+    #: No metric.
+    NONE = "None"
+    #: Coco metric.
+    COCO = "Coco"
+    #: Voc metric.
+    VOC = "Voc"
+    #: CocoVoc metric.
+    COCO_VOC = "CocoVoc"
+
+class ValueFormat(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """format for the workspace connection value
+    """
+
+    JSON = "JSON"
+
+class VMPriceOSType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Operating system type used by the VM.
+    """
+
+    LINUX = "Linux"
+    WINDOWS = "Windows"
+
+class VmPriority(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Virtual Machine priority
+    """
+
+    DEDICATED = "Dedicated"
+    LOW_PRIORITY = "LowPriority"
+
+class VMTier(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The type of the VM.
+    """
+
+    STANDARD = "Standard"
+    LOW_PRIORITY = "LowPriority"
+    SPOT = "Spot"
+
+class WeekDay(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum of weekday
+    """
+
+    #: Monday weekday.
+    MONDAY = "Monday"
+    #: Tuesday weekday.
+    TUESDAY = "Tuesday"
+    #: Wednesday weekday.
+    WEDNESDAY = "Wednesday"
+    #: Thursday weekday.
+    THURSDAY = "Thursday"
+    #: Friday weekday.
+    FRIDAY = "Friday"
+    #: Saturday weekday.
+    SATURDAY = "Saturday"
+    #: Sunday weekday.
+    SUNDAY = "Sunday"
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/_models.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/_models.py
new file mode 100644
index 00000000..fd44c8cb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/_models.py
@@ -0,0 +1,19694 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.core.exceptions import HttpResponseError
+import msrest.serialization
+
+
+class DatastoreCredentials(msrest.serialization.Model):
+    """Base definition for datastore credentials.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AccountKeyDatastoreCredentials, CertificateDatastoreCredentials, NoneDatastoreCredentials, SasDatastoreCredentials, ServicePrincipalDatastoreCredentials.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'credentials_type': {'AccountKey': 'AccountKeyDatastoreCredentials', 'Certificate': 'CertificateDatastoreCredentials', 'None': 'NoneDatastoreCredentials', 'Sas': 'SasDatastoreCredentials', 'ServicePrincipal': 'ServicePrincipalDatastoreCredentials'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(DatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = None  # type: Optional[str]
+
+
+class AccountKeyDatastoreCredentials(DatastoreCredentials):
+    """Account key datastore credentials configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    :ivar secrets: Required. [Required] Storage account secrets.
+    :vartype secrets: ~azure.mgmt.machinelearningservices.models.AccountKeyDatastoreSecrets
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+        'secrets': {'required': True},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+        'secrets': {'key': 'secrets', 'type': 'AccountKeyDatastoreSecrets'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword secrets: Required. [Required] Storage account secrets.
+        :paramtype secrets: ~azure.mgmt.machinelearningservices.models.AccountKeyDatastoreSecrets
+        """
+        super(AccountKeyDatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = 'AccountKey'  # type: str
+        self.secrets = kwargs['secrets']
+
+
+class DatastoreSecrets(msrest.serialization.Model):
+    """Base definition for datastore secrets.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AccountKeyDatastoreSecrets, CertificateDatastoreSecrets, SasDatastoreSecrets, ServicePrincipalDatastoreSecrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate", "Sas",
+     "ServicePrincipal".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    """
+
+    _validation = {
+        'secrets_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'secrets_type': {'key': 'secretsType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'secrets_type': {'AccountKey': 'AccountKeyDatastoreSecrets', 'Certificate': 'CertificateDatastoreSecrets', 'Sas': 'SasDatastoreSecrets', 'ServicePrincipal': 'ServicePrincipalDatastoreSecrets'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(DatastoreSecrets, self).__init__(**kwargs)
+        self.secrets_type = None  # type: Optional[str]
+
+
+class AccountKeyDatastoreSecrets(DatastoreSecrets):
+    """Datastore account key secrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate", "Sas",
+     "ServicePrincipal".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    :ivar key: Storage account key.
+    :vartype key: str
+    """
+
+    _validation = {
+        'secrets_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'secrets_type': {'key': 'secretsType', 'type': 'str'},
+        'key': {'key': 'key', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword key: Storage account key.
+        :paramtype key: str
+        """
+        super(AccountKeyDatastoreSecrets, self).__init__(**kwargs)
+        self.secrets_type = 'AccountKey'  # type: str
+        self.key = kwargs.get('key', None)
+
+
+class AKSSchema(msrest.serialization.Model):
+    """AKSSchema.
+
+    :ivar properties: AKS properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'AKSSchemaProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: AKS properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
+        """
+        super(AKSSchema, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class Compute(msrest.serialization.Model):
+    """Machine Learning compute object.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AKS, AmlCompute, ComputeInstance, DataFactory, DataLakeAnalytics, Databricks, HDInsight, Kubernetes, SynapseSpark, VirtualMachine.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    _subtype_map = {
+        'compute_type': {'AKS': 'AKS', 'AmlCompute': 'AmlCompute', 'ComputeInstance': 'ComputeInstance', 'DataFactory': 'DataFactory', 'DataLakeAnalytics': 'DataLakeAnalytics', 'Databricks': 'Databricks', 'HDInsight': 'HDInsight', 'Kubernetes': 'Kubernetes', 'SynapseSpark': 'SynapseSpark', 'VirtualMachine': 'VirtualMachine'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(Compute, self).__init__(**kwargs)
+        self.compute_type = None  # type: Optional[str]
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = kwargs.get('description', None)
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = kwargs.get('resource_id', None)
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class AKS(Compute, AKSSchema):
+    """A Machine Learning compute based on AKS.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: AKS properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'AKSSchemaProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: AKS properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(AKS, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+        self.compute_type = 'AKS'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = kwargs.get('description', None)
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = kwargs.get('resource_id', None)
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class AksComputeSecretsProperties(msrest.serialization.Model):
+    """Properties of AksComputeSecrets.
+
+    :ivar user_kube_config: Content of kubeconfig file that can be used to connect to the
+     Kubernetes cluster.
+    :vartype user_kube_config: str
+    :ivar admin_kube_config: Content of kubeconfig file that can be used to connect to the
+     Kubernetes cluster.
+    :vartype admin_kube_config: str
+    :ivar image_pull_secret_name: Image registry pull secret.
+    :vartype image_pull_secret_name: str
+    """
+
+    _attribute_map = {
+        'user_kube_config': {'key': 'userKubeConfig', 'type': 'str'},
+        'admin_kube_config': {'key': 'adminKubeConfig', 'type': 'str'},
+        'image_pull_secret_name': {'key': 'imagePullSecretName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword user_kube_config: Content of kubeconfig file that can be used to connect to the
+         Kubernetes cluster.
+        :paramtype user_kube_config: str
+        :keyword admin_kube_config: Content of kubeconfig file that can be used to connect to the
+         Kubernetes cluster.
+        :paramtype admin_kube_config: str
+        :keyword image_pull_secret_name: Image registry pull secret.
+        :paramtype image_pull_secret_name: str
+        """
+        super(AksComputeSecretsProperties, self).__init__(**kwargs)
+        self.user_kube_config = kwargs.get('user_kube_config', None)
+        self.admin_kube_config = kwargs.get('admin_kube_config', None)
+        self.image_pull_secret_name = kwargs.get('image_pull_secret_name', None)
+
+
+class ComputeSecrets(msrest.serialization.Model):
+    """Secrets related to a Machine Learning compute. Might differ for every type of compute.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AksComputeSecrets, DatabricksComputeSecrets, VirtualMachineSecrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'compute_type': {'AKS': 'AksComputeSecrets', 'Databricks': 'DatabricksComputeSecrets', 'VirtualMachine': 'VirtualMachineSecrets'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ComputeSecrets, self).__init__(**kwargs)
+        self.compute_type = None  # type: Optional[str]
+
+
+class AksComputeSecrets(ComputeSecrets, AksComputeSecretsProperties):
+    """Secrets related to a Machine Learning compute based on AKS.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar user_kube_config: Content of kubeconfig file that can be used to connect to the
+     Kubernetes cluster.
+    :vartype user_kube_config: str
+    :ivar admin_kube_config: Content of kubeconfig file that can be used to connect to the
+     Kubernetes cluster.
+    :vartype admin_kube_config: str
+    :ivar image_pull_secret_name: Image registry pull secret.
+    :vartype image_pull_secret_name: str
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'user_kube_config': {'key': 'userKubeConfig', 'type': 'str'},
+        'admin_kube_config': {'key': 'adminKubeConfig', 'type': 'str'},
+        'image_pull_secret_name': {'key': 'imagePullSecretName', 'type': 'str'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword user_kube_config: Content of kubeconfig file that can be used to connect to the
+         Kubernetes cluster.
+        :paramtype user_kube_config: str
+        :keyword admin_kube_config: Content of kubeconfig file that can be used to connect to the
+         Kubernetes cluster.
+        :paramtype admin_kube_config: str
+        :keyword image_pull_secret_name: Image registry pull secret.
+        :paramtype image_pull_secret_name: str
+        """
+        super(AksComputeSecrets, self).__init__(**kwargs)
+        self.user_kube_config = kwargs.get('user_kube_config', None)
+        self.admin_kube_config = kwargs.get('admin_kube_config', None)
+        self.image_pull_secret_name = kwargs.get('image_pull_secret_name', None)
+        self.compute_type = 'AKS'  # type: str
+
+
+class AksNetworkingConfiguration(msrest.serialization.Model):
+    """Advance configuration for AKS networking.
+
+    :ivar subnet_id: Virtual network subnet resource ID the compute nodes belong to.
+    :vartype subnet_id: str
+    :ivar service_cidr: A CIDR notation IP range from which to assign service cluster IPs. It must
+     not overlap with any Subnet IP ranges.
+    :vartype service_cidr: str
+    :ivar dns_service_ip: An IP address assigned to the Kubernetes DNS service. It must be within
+     the Kubernetes service address range specified in serviceCidr.
+    :vartype dns_service_ip: str
+    :ivar docker_bridge_cidr: A CIDR notation IP range assigned to the Docker bridge network. It
+     must not overlap with any Subnet IP ranges or the Kubernetes service address range.
+    :vartype docker_bridge_cidr: str
+    """
+
+    _validation = {
+        'service_cidr': {'pattern': r'^([0-9]{1,3}\.){3}[0-9]{1,3}(\/([0-9]|[1-2][0-9]|3[0-2]))?$'},
+        'dns_service_ip': {'pattern': r'^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$'},
+        'docker_bridge_cidr': {'pattern': r'^([0-9]{1,3}\.){3}[0-9]{1,3}(\/([0-9]|[1-2][0-9]|3[0-2]))?$'},
+    }
+
+    _attribute_map = {
+        'subnet_id': {'key': 'subnetId', 'type': 'str'},
+        'service_cidr': {'key': 'serviceCidr', 'type': 'str'},
+        'dns_service_ip': {'key': 'dnsServiceIP', 'type': 'str'},
+        'docker_bridge_cidr': {'key': 'dockerBridgeCidr', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword subnet_id: Virtual network subnet resource ID the compute nodes belong to.
+        :paramtype subnet_id: str
+        :keyword service_cidr: A CIDR notation IP range from which to assign service cluster IPs. It
+         must not overlap with any Subnet IP ranges.
+        :paramtype service_cidr: str
+        :keyword dns_service_ip: An IP address assigned to the Kubernetes DNS service. It must be
+         within the Kubernetes service address range specified in serviceCidr.
+        :paramtype dns_service_ip: str
+        :keyword docker_bridge_cidr: A CIDR notation IP range assigned to the Docker bridge network. It
+         must not overlap with any Subnet IP ranges or the Kubernetes service address range.
+        :paramtype docker_bridge_cidr: str
+        """
+        super(AksNetworkingConfiguration, self).__init__(**kwargs)
+        self.subnet_id = kwargs.get('subnet_id', None)
+        self.service_cidr = kwargs.get('service_cidr', None)
+        self.dns_service_ip = kwargs.get('dns_service_ip', None)
+        self.docker_bridge_cidr = kwargs.get('docker_bridge_cidr', None)
+
+
+class AKSSchemaProperties(msrest.serialization.Model):
+    """AKS properties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar cluster_fqdn: Cluster full qualified domain name.
+    :vartype cluster_fqdn: str
+    :ivar system_services: System services.
+    :vartype system_services: list[~azure.mgmt.machinelearningservices.models.SystemService]
+    :ivar agent_count: Number of agents.
+    :vartype agent_count: int
+    :ivar agent_vm_size: Agent virtual machine size.
+    :vartype agent_vm_size: str
+    :ivar cluster_purpose: Intended usage of the cluster. Possible values include: "FastProd",
+     "DenseProd", "DevTest". Default value: "FastProd".
+    :vartype cluster_purpose: str or ~azure.mgmt.machinelearningservices.models.ClusterPurpose
+    :ivar ssl_configuration: SSL configuration.
+    :vartype ssl_configuration: ~azure.mgmt.machinelearningservices.models.SslConfiguration
+    :ivar aks_networking_configuration: AKS networking configuration for vnet.
+    :vartype aks_networking_configuration:
+     ~azure.mgmt.machinelearningservices.models.AksNetworkingConfiguration
+    :ivar load_balancer_type: Load Balancer Type. Possible values include: "PublicIp",
+     "InternalLoadBalancer". Default value: "PublicIp".
+    :vartype load_balancer_type: str or ~azure.mgmt.machinelearningservices.models.LoadBalancerType
+    :ivar load_balancer_subnet: Load Balancer Subnet.
+    :vartype load_balancer_subnet: str
+    """
+
+    _validation = {
+        'system_services': {'readonly': True},
+        'agent_count': {'minimum': 0},
+    }
+
+    _attribute_map = {
+        'cluster_fqdn': {'key': 'clusterFqdn', 'type': 'str'},
+        'system_services': {'key': 'systemServices', 'type': '[SystemService]'},
+        'agent_count': {'key': 'agentCount', 'type': 'int'},
+        'agent_vm_size': {'key': 'agentVmSize', 'type': 'str'},
+        'cluster_purpose': {'key': 'clusterPurpose', 'type': 'str'},
+        'ssl_configuration': {'key': 'sslConfiguration', 'type': 'SslConfiguration'},
+        'aks_networking_configuration': {'key': 'aksNetworkingConfiguration', 'type': 'AksNetworkingConfiguration'},
+        'load_balancer_type': {'key': 'loadBalancerType', 'type': 'str'},
+        'load_balancer_subnet': {'key': 'loadBalancerSubnet', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword cluster_fqdn: Cluster full qualified domain name.
+        :paramtype cluster_fqdn: str
+        :keyword agent_count: Number of agents.
+        :paramtype agent_count: int
+        :keyword agent_vm_size: Agent virtual machine size.
+        :paramtype agent_vm_size: str
+        :keyword cluster_purpose: Intended usage of the cluster. Possible values include: "FastProd",
+         "DenseProd", "DevTest". Default value: "FastProd".
+        :paramtype cluster_purpose: str or ~azure.mgmt.machinelearningservices.models.ClusterPurpose
+        :keyword ssl_configuration: SSL configuration.
+        :paramtype ssl_configuration: ~azure.mgmt.machinelearningservices.models.SslConfiguration
+        :keyword aks_networking_configuration: AKS networking configuration for vnet.
+        :paramtype aks_networking_configuration:
+         ~azure.mgmt.machinelearningservices.models.AksNetworkingConfiguration
+        :keyword load_balancer_type: Load Balancer Type. Possible values include: "PublicIp",
+         "InternalLoadBalancer". Default value: "PublicIp".
+        :paramtype load_balancer_type: str or
+         ~azure.mgmt.machinelearningservices.models.LoadBalancerType
+        :keyword load_balancer_subnet: Load Balancer Subnet.
+        :paramtype load_balancer_subnet: str
+        """
+        super(AKSSchemaProperties, self).__init__(**kwargs)
+        self.cluster_fqdn = kwargs.get('cluster_fqdn', None)
+        self.system_services = None
+        self.agent_count = kwargs.get('agent_count', None)
+        self.agent_vm_size = kwargs.get('agent_vm_size', None)
+        self.cluster_purpose = kwargs.get('cluster_purpose', "FastProd")
+        self.ssl_configuration = kwargs.get('ssl_configuration', None)
+        self.aks_networking_configuration = kwargs.get('aks_networking_configuration', None)
+        self.load_balancer_type = kwargs.get('load_balancer_type', "PublicIp")
+        self.load_balancer_subnet = kwargs.get('load_balancer_subnet', None)
+
+
+class AmlComputeSchema(msrest.serialization.Model):
+    """Properties(top level) of AmlCompute.
+
+    :ivar properties: Properties of AmlCompute.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'AmlComputeProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of AmlCompute.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
+        """
+        super(AmlComputeSchema, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class AmlCompute(Compute, AmlComputeSchema):
+    """An Azure Machine Learning compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: Properties of AmlCompute.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'AmlComputeProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of AmlCompute.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(AmlCompute, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+        self.compute_type = 'AmlCompute'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = kwargs.get('description', None)
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = kwargs.get('resource_id', None)
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class AmlComputeNodeInformation(msrest.serialization.Model):
+    """Compute node information related to a AmlCompute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar node_id: ID of the compute node.
+    :vartype node_id: str
+    :ivar private_ip_address: Private IP address of the compute node.
+    :vartype private_ip_address: str
+    :ivar public_ip_address: Public IP address of the compute node.
+    :vartype public_ip_address: str
+    :ivar port: SSH port number of the node.
+    :vartype port: int
+    :ivar node_state: State of the compute node. Values are idle, running, preparing, unusable,
+     leaving and preempted. Possible values include: "idle", "running", "preparing", "unusable",
+     "leaving", "preempted".
+    :vartype node_state: str or ~azure.mgmt.machinelearningservices.models.NodeState
+    :ivar run_id: ID of the Experiment running on the node, if any else null.
+    :vartype run_id: str
+    """
+
+    _validation = {
+        'node_id': {'readonly': True},
+        'private_ip_address': {'readonly': True},
+        'public_ip_address': {'readonly': True},
+        'port': {'readonly': True},
+        'node_state': {'readonly': True},
+        'run_id': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'node_id': {'key': 'nodeId', 'type': 'str'},
+        'private_ip_address': {'key': 'privateIpAddress', 'type': 'str'},
+        'public_ip_address': {'key': 'publicIpAddress', 'type': 'str'},
+        'port': {'key': 'port', 'type': 'int'},
+        'node_state': {'key': 'nodeState', 'type': 'str'},
+        'run_id': {'key': 'runId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AmlComputeNodeInformation, self).__init__(**kwargs)
+        self.node_id = None
+        self.private_ip_address = None
+        self.public_ip_address = None
+        self.port = None
+        self.node_state = None
+        self.run_id = None
+
+
+class AmlComputeNodesInformation(msrest.serialization.Model):
+    """Result of AmlCompute Nodes.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar nodes: The collection of returned AmlCompute nodes details.
+    :vartype nodes: list[~azure.mgmt.machinelearningservices.models.AmlComputeNodeInformation]
+    :ivar next_link: The continuation token.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'nodes': {'readonly': True},
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'nodes': {'key': 'nodes', 'type': '[AmlComputeNodeInformation]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AmlComputeNodesInformation, self).__init__(**kwargs)
+        self.nodes = None
+        self.next_link = None
+
+
+class AmlComputeProperties(msrest.serialization.Model):
+    """AML Compute properties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar os_type: Compute OS Type. Possible values include: "Linux", "Windows". Default value:
+     "Linux".
+    :vartype os_type: str or ~azure.mgmt.machinelearningservices.models.OsType
+    :ivar vm_size: Virtual Machine Size.
+    :vartype vm_size: str
+    :ivar vm_priority: Virtual Machine priority. Possible values include: "Dedicated",
+     "LowPriority".
+    :vartype vm_priority: str or ~azure.mgmt.machinelearningservices.models.VmPriority
+    :ivar virtual_machine_image: Virtual Machine image for AML Compute - windows only.
+    :vartype virtual_machine_image: ~azure.mgmt.machinelearningservices.models.VirtualMachineImage
+    :ivar isolated_network: Network is isolated or not.
+    :vartype isolated_network: bool
+    :ivar scale_settings: Scale settings for AML Compute.
+    :vartype scale_settings: ~azure.mgmt.machinelearningservices.models.ScaleSettings
+    :ivar user_account_credentials: Credentials for an administrator user account that will be
+     created on each compute node.
+    :vartype user_account_credentials:
+     ~azure.mgmt.machinelearningservices.models.UserAccountCredentials
+    :ivar subnet: Virtual network subnet resource ID the compute nodes belong to.
+    :vartype subnet: ~azure.mgmt.machinelearningservices.models.ResourceId
+    :ivar remote_login_port_public_access: State of the public SSH port. Possible values are:
+     Disabled - Indicates that the public ssh port is closed on all nodes of the cluster. Enabled -
+     Indicates that the public ssh port is open on all nodes of the cluster. NotSpecified -
+     Indicates that the public ssh port is closed on all nodes of the cluster if VNet is defined,
+     else is open all public nodes. It can be default only during cluster creation time, after
+     creation it will be either enabled or disabled. Possible values include: "Enabled", "Disabled",
+     "NotSpecified". Default value: "NotSpecified".
+    :vartype remote_login_port_public_access: str or
+     ~azure.mgmt.machinelearningservices.models.RemoteLoginPortPublicAccess
+    :ivar allocation_state: Allocation state of the compute. Possible values are: steady -
+     Indicates that the compute is not resizing. There are no changes to the number of compute nodes
+     in the compute in progress. A compute enters this state when it is created and when no
+     operations are being performed on the compute to change the number of compute nodes. resizing -
+     Indicates that the compute is resizing; that is, compute nodes are being added to or removed
+     from the compute. Possible values include: "Steady", "Resizing".
+    :vartype allocation_state: str or ~azure.mgmt.machinelearningservices.models.AllocationState
+    :ivar allocation_state_transition_time: The time at which the compute entered its current
+     allocation state.
+    :vartype allocation_state_transition_time: ~datetime.datetime
+    :ivar errors: Collection of errors encountered by various compute nodes during node setup.
+    :vartype errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar current_node_count: The number of compute nodes currently assigned to the compute.
+    :vartype current_node_count: int
+    :ivar target_node_count: The target number of compute nodes for the compute. If the
+     allocationState is resizing, this property denotes the target node count for the ongoing resize
+     operation. If the allocationState is steady, this property denotes the target node count for
+     the previous resize operation.
+    :vartype target_node_count: int
+    :ivar node_state_counts: Counts of various node states on the compute.
+    :vartype node_state_counts: ~azure.mgmt.machinelearningservices.models.NodeStateCounts
+    :ivar enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
+     values are: Possible values are: true - Indicates that the compute nodes will have public IPs
+     provisioned. false - Indicates that the compute nodes will have a private endpoint and no
+     public IPs.
+    :vartype enable_node_public_ip: bool
+    :ivar property_bag: A property bag containing additional properties.
+    :vartype property_bag: any
+    """
+
+    _validation = {
+        'allocation_state': {'readonly': True},
+        'allocation_state_transition_time': {'readonly': True},
+        'errors': {'readonly': True},
+        'current_node_count': {'readonly': True},
+        'target_node_count': {'readonly': True},
+        'node_state_counts': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'os_type': {'key': 'osType', 'type': 'str'},
+        'vm_size': {'key': 'vmSize', 'type': 'str'},
+        'vm_priority': {'key': 'vmPriority', 'type': 'str'},
+        'virtual_machine_image': {'key': 'virtualMachineImage', 'type': 'VirtualMachineImage'},
+        'isolated_network': {'key': 'isolatedNetwork', 'type': 'bool'},
+        'scale_settings': {'key': 'scaleSettings', 'type': 'ScaleSettings'},
+        'user_account_credentials': {'key': 'userAccountCredentials', 'type': 'UserAccountCredentials'},
+        'subnet': {'key': 'subnet', 'type': 'ResourceId'},
+        'remote_login_port_public_access': {'key': 'remoteLoginPortPublicAccess', 'type': 'str'},
+        'allocation_state': {'key': 'allocationState', 'type': 'str'},
+        'allocation_state_transition_time': {'key': 'allocationStateTransitionTime', 'type': 'iso-8601'},
+        'errors': {'key': 'errors', 'type': '[ErrorResponse]'},
+        'current_node_count': {'key': 'currentNodeCount', 'type': 'int'},
+        'target_node_count': {'key': 'targetNodeCount', 'type': 'int'},
+        'node_state_counts': {'key': 'nodeStateCounts', 'type': 'NodeStateCounts'},
+        'enable_node_public_ip': {'key': 'enableNodePublicIp', 'type': 'bool'},
+        'property_bag': {'key': 'propertyBag', 'type': 'object'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword os_type: Compute OS Type. Possible values include: "Linux", "Windows". Default value:
+         "Linux".
+        :paramtype os_type: str or ~azure.mgmt.machinelearningservices.models.OsType
+        :keyword vm_size: Virtual Machine Size.
+        :paramtype vm_size: str
+        :keyword vm_priority: Virtual Machine priority. Possible values include: "Dedicated",
+         "LowPriority".
+        :paramtype vm_priority: str or ~azure.mgmt.machinelearningservices.models.VmPriority
+        :keyword virtual_machine_image: Virtual Machine image for AML Compute - windows only.
+        :paramtype virtual_machine_image:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineImage
+        :keyword isolated_network: Network is isolated or not.
+        :paramtype isolated_network: bool
+        :keyword scale_settings: Scale settings for AML Compute.
+        :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.ScaleSettings
+        :keyword user_account_credentials: Credentials for an administrator user account that will be
+         created on each compute node.
+        :paramtype user_account_credentials:
+         ~azure.mgmt.machinelearningservices.models.UserAccountCredentials
+        :keyword subnet: Virtual network subnet resource ID the compute nodes belong to.
+        :paramtype subnet: ~azure.mgmt.machinelearningservices.models.ResourceId
+        :keyword remote_login_port_public_access: State of the public SSH port. Possible values are:
+         Disabled - Indicates that the public ssh port is closed on all nodes of the cluster. Enabled -
+         Indicates that the public ssh port is open on all nodes of the cluster. NotSpecified -
+         Indicates that the public ssh port is closed on all nodes of the cluster if VNet is defined,
+         else is open all public nodes. It can be default only during cluster creation time, after
+         creation it will be either enabled or disabled. Possible values include: "Enabled", "Disabled",
+         "NotSpecified". Default value: "NotSpecified".
+        :paramtype remote_login_port_public_access: str or
+         ~azure.mgmt.machinelearningservices.models.RemoteLoginPortPublicAccess
+        :keyword enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
+         values are: Possible values are: true - Indicates that the compute nodes will have public IPs
+         provisioned. false - Indicates that the compute nodes will have a private endpoint and no
+         public IPs.
+        :paramtype enable_node_public_ip: bool
+        :keyword property_bag: A property bag containing additional properties.
+        :paramtype property_bag: any
+        """
+        super(AmlComputeProperties, self).__init__(**kwargs)
+        self.os_type = kwargs.get('os_type', "Linux")
+        self.vm_size = kwargs.get('vm_size', None)
+        self.vm_priority = kwargs.get('vm_priority', None)
+        self.virtual_machine_image = kwargs.get('virtual_machine_image', None)
+        self.isolated_network = kwargs.get('isolated_network', None)
+        self.scale_settings = kwargs.get('scale_settings', None)
+        self.user_account_credentials = kwargs.get('user_account_credentials', None)
+        self.subnet = kwargs.get('subnet', None)
+        self.remote_login_port_public_access = kwargs.get('remote_login_port_public_access', "NotSpecified")
+        self.allocation_state = None
+        self.allocation_state_transition_time = None
+        self.errors = None
+        self.current_node_count = None
+        self.target_node_count = None
+        self.node_state_counts = None
+        self.enable_node_public_ip = kwargs.get('enable_node_public_ip', True)
+        self.property_bag = kwargs.get('property_bag', None)
+
+
+class AmlOperation(msrest.serialization.Model):
+    """Azure Machine Learning workspace REST API operation.
+
+    :ivar name: Operation name: {provider}/{resource}/{operation}.
+    :vartype name: str
+    :ivar display: Display name of operation.
+    :vartype display: ~azure.mgmt.machinelearningservices.models.AmlOperationDisplay
+    :ivar is_data_action: Indicates whether the operation applies to data-plane.
+    :vartype is_data_action: bool
+    """
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'display': {'key': 'display', 'type': 'AmlOperationDisplay'},
+        'is_data_action': {'key': 'isDataAction', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword name: Operation name: {provider}/{resource}/{operation}.
+        :paramtype name: str
+        :keyword display: Display name of operation.
+        :paramtype display: ~azure.mgmt.machinelearningservices.models.AmlOperationDisplay
+        :keyword is_data_action: Indicates whether the operation applies to data-plane.
+        :paramtype is_data_action: bool
+        """
+        super(AmlOperation, self).__init__(**kwargs)
+        self.name = kwargs.get('name', None)
+        self.display = kwargs.get('display', None)
+        self.is_data_action = kwargs.get('is_data_action', None)
+
+
+class AmlOperationDisplay(msrest.serialization.Model):
+    """Display name of operation.
+
+    :ivar provider: The resource provider name: Microsoft.MachineLearningExperimentation.
+    :vartype provider: str
+    :ivar resource: The resource on which the operation is performed.
+    :vartype resource: str
+    :ivar operation: The operation that users can perform.
+    :vartype operation: str
+    :ivar description: The description for the operation.
+    :vartype description: str
+    """
+
+    _attribute_map = {
+        'provider': {'key': 'provider', 'type': 'str'},
+        'resource': {'key': 'resource', 'type': 'str'},
+        'operation': {'key': 'operation', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword provider: The resource provider name: Microsoft.MachineLearningExperimentation.
+        :paramtype provider: str
+        :keyword resource: The resource on which the operation is performed.
+        :paramtype resource: str
+        :keyword operation: The operation that users can perform.
+        :paramtype operation: str
+        :keyword description: The description for the operation.
+        :paramtype description: str
+        """
+        super(AmlOperationDisplay, self).__init__(**kwargs)
+        self.provider = kwargs.get('provider', None)
+        self.resource = kwargs.get('resource', None)
+        self.operation = kwargs.get('operation', None)
+        self.description = kwargs.get('description', None)
+
+
+class AmlOperationListResult(msrest.serialization.Model):
+    """An array of operations supported by the resource provider.
+
+    :ivar value: List of AML workspace operations supported by the AML workspace resource provider.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.AmlOperation]
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[AmlOperation]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: List of AML workspace operations supported by the AML workspace resource
+         provider.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.AmlOperation]
+        """
+        super(AmlOperationListResult, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+
+
+class IdentityConfiguration(msrest.serialization.Model):
+    """Base definition for identity configuration.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AmlToken, ManagedIdentity, UserIdentity.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar identity_type: Required. [Required] Specifies the type of identity framework.Constant
+     filled by server. Possible values include: "Managed", "AMLToken", "UserIdentity".
+    :vartype identity_type: str or
+     ~azure.mgmt.machinelearningservices.models.IdentityConfigurationType
+    """
+
+    _validation = {
+        'identity_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'identity_type': {'key': 'identityType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'identity_type': {'AMLToken': 'AmlToken', 'Managed': 'ManagedIdentity', 'UserIdentity': 'UserIdentity'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(IdentityConfiguration, self).__init__(**kwargs)
+        self.identity_type = None  # type: Optional[str]
+
+
+class AmlToken(IdentityConfiguration):
+    """AML Token identity configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar identity_type: Required. [Required] Specifies the type of identity framework.Constant
+     filled by server. Possible values include: "Managed", "AMLToken", "UserIdentity".
+    :vartype identity_type: str or
+     ~azure.mgmt.machinelearningservices.models.IdentityConfigurationType
+    """
+
+    _validation = {
+        'identity_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'identity_type': {'key': 'identityType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AmlToken, self).__init__(**kwargs)
+        self.identity_type = 'AMLToken'  # type: str
+
+
+class AmlUserFeature(msrest.serialization.Model):
+    """Features enabled for a workspace.
+
+    :ivar id: Specifies the feature ID.
+    :vartype id: str
+    :ivar display_name: Specifies the feature name.
+    :vartype display_name: str
+    :ivar description: Describes the feature for user experience.
+    :vartype description: str
+    """
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword id: Specifies the feature ID.
+        :paramtype id: str
+        :keyword display_name: Specifies the feature name.
+        :paramtype display_name: str
+        :keyword description: Describes the feature for user experience.
+        :paramtype description: str
+        """
+        super(AmlUserFeature, self).__init__(**kwargs)
+        self.id = kwargs.get('id', None)
+        self.display_name = kwargs.get('display_name', None)
+        self.description = kwargs.get('description', None)
+
+
+class ResourceBase(msrest.serialization.Model):
+    """ResourceBase.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        """
+        super(ResourceBase, self).__init__(**kwargs)
+        self.description = kwargs.get('description', None)
+        self.properties = kwargs.get('properties', None)
+        self.tags = kwargs.get('tags', None)
+
+
+class AssetBase(ResourceBase):
+    """AssetBase.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(AssetBase, self).__init__(**kwargs)
+        self.is_anonymous = kwargs.get('is_anonymous', False)
+        self.is_archived = kwargs.get('is_archived', False)
+
+
+class AssetContainer(ResourceBase):
+    """AssetContainer.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(AssetContainer, self).__init__(**kwargs)
+        self.is_archived = kwargs.get('is_archived', False)
+        self.latest_version = None
+        self.next_version = None
+
+
+class AssetJobInput(msrest.serialization.Model):
+    """Asset input type.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        """
+        super(AssetJobInput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs['uri']
+
+
+class AssetJobOutput(msrest.serialization.Model):
+    """Asset output type.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    """
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        """
+        super(AssetJobOutput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs.get('uri', None)
+
+
+class AssetReferenceBase(msrest.serialization.Model):
+    """Base definition for asset references.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: DataPathAssetReference, IdAssetReference, OutputPathAssetReference.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar reference_type: Required. [Required] Specifies the type of asset reference.Constant
+     filled by server. Possible values include: "Id", "DataPath", "OutputPath".
+    :vartype reference_type: str or ~azure.mgmt.machinelearningservices.models.ReferenceType
+    """
+
+    _validation = {
+        'reference_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'reference_type': {'key': 'referenceType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'reference_type': {'DataPath': 'DataPathAssetReference', 'Id': 'IdAssetReference', 'OutputPath': 'OutputPathAssetReference'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AssetReferenceBase, self).__init__(**kwargs)
+        self.reference_type = None  # type: Optional[str]
+
+
+class AssignedUser(msrest.serialization.Model):
+    """A user that can be assigned to a compute instance.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar object_id: Required. User�s AAD Object Id.
+    :vartype object_id: str
+    :ivar tenant_id: Required. User�s AAD Tenant Id.
+    :vartype tenant_id: str
+    """
+
+    _validation = {
+        'object_id': {'required': True},
+        'tenant_id': {'required': True},
+    }
+
+    _attribute_map = {
+        'object_id': {'key': 'objectId', 'type': 'str'},
+        'tenant_id': {'key': 'tenantId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword object_id: Required. User�s AAD Object Id.
+        :paramtype object_id: str
+        :keyword tenant_id: Required. User�s AAD Tenant Id.
+        :paramtype tenant_id: str
+        """
+        super(AssignedUser, self).__init__(**kwargs)
+        self.object_id = kwargs['object_id']
+        self.tenant_id = kwargs['tenant_id']
+
+
+class ForecastHorizon(msrest.serialization.Model):
+    """The desired maximum forecast horizon in units of time-series frequency.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoForecastHorizon, CustomForecastHorizon.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set forecast horizon value selection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.ForecastHorizonMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'mode': {'Auto': 'AutoForecastHorizon', 'Custom': 'CustomForecastHorizon'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ForecastHorizon, self).__init__(**kwargs)
+        self.mode = None  # type: Optional[str]
+
+
+class AutoForecastHorizon(ForecastHorizon):
+    """Forecast horizon determined automatically by system.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set forecast horizon value selection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.ForecastHorizonMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AutoForecastHorizon, self).__init__(**kwargs)
+        self.mode = 'Auto'  # type: str
+
+
+class JobBaseProperties(ResourceBase):
+    """Base definition for a job.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoMLJob, CommandJob, PipelineJob, SweepJob.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "AutoML", "Command", "Sweep", "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Possible values include: "NotStarted", "Starting",
+     "Provisioning", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
+     "Failed", "Canceled", "NotResponding", "Paused", "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    """
+
+    _validation = {
+        'job_type': {'required': True},
+        'status': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'component_id': {'key': 'componentId', 'type': 'str'},
+        'compute_id': {'key': 'computeId', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'experiment_name': {'key': 'experimentName', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'job_type': {'key': 'jobType', 'type': 'str'},
+        'services': {'key': 'services', 'type': '{JobService}'},
+        'status': {'key': 'status', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'job_type': {'AutoML': 'AutoMLJob', 'Command': 'CommandJob', 'Pipeline': 'PipelineJob', 'Sweep': 'SweepJob'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        """
+        super(JobBaseProperties, self).__init__(**kwargs)
+        self.component_id = kwargs.get('component_id', None)
+        self.compute_id = kwargs.get('compute_id', None)
+        self.display_name = kwargs.get('display_name', None)
+        self.experiment_name = kwargs.get('experiment_name', "Default")
+        self.identity = kwargs.get('identity', None)
+        self.is_archived = kwargs.get('is_archived', False)
+        self.job_type = 'JobBaseProperties'  # type: str
+        self.services = kwargs.get('services', None)
+        self.status = None
+
+
+class AutoMLJob(JobBaseProperties):
+    """AutoMLJob class.
+Use this class for executing AutoML tasks like Classification/Regression etc.
+See TaskType enum for all the tasks supported.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "AutoML", "Command", "Sweep", "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Possible values include: "NotStarted", "Starting",
+     "Provisioning", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
+     "Failed", "Canceled", "NotResponding", "Paused", "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar environment_id: The ARM resource ID of the Environment specification for the job.
+     This is optional value to provide, if not provided, AutoML will default this to Production
+     AutoML curated environment version when running the job.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables included in the job.
+    :vartype environment_variables: dict[str, str]
+    :ivar outputs: Mapping of output data bindings used in the job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar resources: Compute Resource configuration for the job.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+    :ivar task_details: Required. [Required] This represents scenario which can be one of
+     Tables/NLP/Image.
+    :vartype task_details: ~azure.mgmt.machinelearningservices.models.AutoMLVertical
+    """
+
+    _validation = {
+        'job_type': {'required': True},
+        'status': {'readonly': True},
+        'task_details': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'component_id': {'key': 'componentId', 'type': 'str'},
+        'compute_id': {'key': 'computeId', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'experiment_name': {'key': 'experimentName', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'job_type': {'key': 'jobType', 'type': 'str'},
+        'services': {'key': 'services', 'type': '{JobService}'},
+        'status': {'key': 'status', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
+        'resources': {'key': 'resources', 'type': 'JobResourceConfiguration'},
+        'task_details': {'key': 'taskDetails', 'type': 'AutoMLVertical'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword environment_id: The ARM resource ID of the Environment specification for the job.
+         This is optional value to provide, if not provided, AutoML will default this to Production
+         AutoML curated environment version when running the job.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables included in the job.
+        :paramtype environment_variables: dict[str, str]
+        :keyword outputs: Mapping of output data bindings used in the job.
+        :paramtype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+        :keyword resources: Compute Resource configuration for the job.
+        :paramtype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+        :keyword task_details: Required. [Required] This represents scenario which can be one of
+         Tables/NLP/Image.
+        :paramtype task_details: ~azure.mgmt.machinelearningservices.models.AutoMLVertical
+        """
+        super(AutoMLJob, self).__init__(**kwargs)
+        self.job_type = 'AutoML'  # type: str
+        self.environment_id = kwargs.get('environment_id', None)
+        self.environment_variables = kwargs.get('environment_variables', None)
+        self.outputs = kwargs.get('outputs', None)
+        self.resources = kwargs.get('resources', None)
+        self.task_details = kwargs['task_details']
+
+
+class AutoMLVertical(msrest.serialization.Model):
+    """AutoML vertical class.
+Base class for AutoML verticals - TableVertical/ImageVertical/NLPVertical.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: Classification, Forecasting, ImageClassification, ImageClassificationMultilabel, ImageInstanceSegmentation, ImageObjectDetection, Regression, TextClassification, TextClassificationMultilabel, TextNer.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+    }
+
+    _subtype_map = {
+        'task_type': {'Classification': 'Classification', 'Forecasting': 'Forecasting', 'ImageClassification': 'ImageClassification', 'ImageClassificationMultilabel': 'ImageClassificationMultilabel', 'ImageInstanceSegmentation': 'ImageInstanceSegmentation', 'ImageObjectDetection': 'ImageObjectDetection', 'Regression': 'Regression', 'TextClassification': 'TextClassification', 'TextClassificationMultilabel': 'TextClassificationMultilabel', 'TextNER': 'TextNer'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        """
+        super(AutoMLVertical, self).__init__(**kwargs)
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.task_type = None  # type: Optional[str]
+        self.training_data = kwargs['training_data']
+
+
+class NCrossValidations(msrest.serialization.Model):
+    """N-Cross validations value.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoNCrossValidations, CustomNCrossValidations.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Mode for determining N-Cross validations.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.NCrossValidationsMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'mode': {'Auto': 'AutoNCrossValidations', 'Custom': 'CustomNCrossValidations'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(NCrossValidations, self).__init__(**kwargs)
+        self.mode = None  # type: Optional[str]
+
+
+class AutoNCrossValidations(NCrossValidations):
+    """N-Cross validations determined automatically.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Mode for determining N-Cross validations.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.NCrossValidationsMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AutoNCrossValidations, self).__init__(**kwargs)
+        self.mode = 'Auto'  # type: str
+
+
+class AutoPauseProperties(msrest.serialization.Model):
+    """Auto pause properties.
+
+    :ivar delay_in_minutes:
+    :vartype delay_in_minutes: int
+    :ivar enabled:
+    :vartype enabled: bool
+    """
+
+    _attribute_map = {
+        'delay_in_minutes': {'key': 'delayInMinutes', 'type': 'int'},
+        'enabled': {'key': 'enabled', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword delay_in_minutes:
+        :paramtype delay_in_minutes: int
+        :keyword enabled:
+        :paramtype enabled: bool
+        """
+        super(AutoPauseProperties, self).__init__(**kwargs)
+        self.delay_in_minutes = kwargs.get('delay_in_minutes', None)
+        self.enabled = kwargs.get('enabled', None)
+
+
+class AutoScaleProperties(msrest.serialization.Model):
+    """Auto scale properties.
+
+    :ivar min_node_count:
+    :vartype min_node_count: int
+    :ivar enabled:
+    :vartype enabled: bool
+    :ivar max_node_count:
+    :vartype max_node_count: int
+    """
+
+    _attribute_map = {
+        'min_node_count': {'key': 'minNodeCount', 'type': 'int'},
+        'enabled': {'key': 'enabled', 'type': 'bool'},
+        'max_node_count': {'key': 'maxNodeCount', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword min_node_count:
+        :paramtype min_node_count: int
+        :keyword enabled:
+        :paramtype enabled: bool
+        :keyword max_node_count:
+        :paramtype max_node_count: int
+        """
+        super(AutoScaleProperties, self).__init__(**kwargs)
+        self.min_node_count = kwargs.get('min_node_count', None)
+        self.enabled = kwargs.get('enabled', None)
+        self.max_node_count = kwargs.get('max_node_count', None)
+
+
+class Seasonality(msrest.serialization.Model):
+    """Forecasting seasonality.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoSeasonality, CustomSeasonality.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Seasonality mode.Constant filled by server. Possible values
+     include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.SeasonalityMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'mode': {'Auto': 'AutoSeasonality', 'Custom': 'CustomSeasonality'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(Seasonality, self).__init__(**kwargs)
+        self.mode = None  # type: Optional[str]
+
+
+class AutoSeasonality(Seasonality):
+    """AutoSeasonality.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Seasonality mode.Constant filled by server. Possible values
+     include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.SeasonalityMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AutoSeasonality, self).__init__(**kwargs)
+        self.mode = 'Auto'  # type: str
+
+
+class TargetLags(msrest.serialization.Model):
+    """The number of past periods to lag from the target column.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoTargetLags, CustomTargetLags.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set target lags mode - Auto/Custom.Constant filled by server.
+     Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetLagsMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'mode': {'Auto': 'AutoTargetLags', 'Custom': 'CustomTargetLags'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(TargetLags, self).__init__(**kwargs)
+        self.mode = None  # type: Optional[str]
+
+
+class AutoTargetLags(TargetLags):
+    """AutoTargetLags.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set target lags mode - Auto/Custom.Constant filled by server.
+     Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetLagsMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AutoTargetLags, self).__init__(**kwargs)
+        self.mode = 'Auto'  # type: str
+
+
+class TargetRollingWindowSize(msrest.serialization.Model):
+    """Forecasting target rolling window size.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoTargetRollingWindowSize, CustomTargetRollingWindowSize.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] TargetRollingWindowSiz detection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetRollingWindowSizeMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'mode': {'Auto': 'AutoTargetRollingWindowSize', 'Custom': 'CustomTargetRollingWindowSize'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(TargetRollingWindowSize, self).__init__(**kwargs)
+        self.mode = None  # type: Optional[str]
+
+
+class AutoTargetRollingWindowSize(TargetRollingWindowSize):
+    """Target lags rolling window determined automatically.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] TargetRollingWindowSiz detection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetRollingWindowSizeMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AutoTargetRollingWindowSize, self).__init__(**kwargs)
+        self.mode = 'Auto'  # type: str
+
+
+class DatastoreProperties(ResourceBase):
+    """Base definition for datastore contents configuration.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AzureBlobDatastore, AzureDataLakeGen1Datastore, AzureDataLakeGen2Datastore, AzureFileDatastore.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: Required. [Required] Account credentials.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: Required. [Required] Storage type backing the datastore.Constant filled
+     by server. Possible values include: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2",
+     "AzureFile".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    """
+
+    _validation = {
+        'credentials': {'required': True},
+        'datastore_type': {'required': True},
+        'is_default': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'credentials': {'key': 'credentials', 'type': 'DatastoreCredentials'},
+        'datastore_type': {'key': 'datastoreType', 'type': 'str'},
+        'is_default': {'key': 'isDefault', 'type': 'bool'},
+    }
+
+    _subtype_map = {
+        'datastore_type': {'AzureBlob': 'AzureBlobDatastore', 'AzureDataLakeGen1': 'AzureDataLakeGen1Datastore', 'AzureDataLakeGen2': 'AzureDataLakeGen2Datastore', 'AzureFile': 'AzureFileDatastore'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: Required. [Required] Account credentials.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        """
+        super(DatastoreProperties, self).__init__(**kwargs)
+        self.credentials = kwargs['credentials']
+        self.datastore_type = 'DatastoreProperties'  # type: str
+        self.is_default = None
+
+
+class AzureBlobDatastore(DatastoreProperties):
+    """Azure Blob datastore configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: Required. [Required] Account credentials.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: Required. [Required] Storage type backing the datastore.Constant filled
+     by server. Possible values include: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2",
+     "AzureFile".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    :ivar account_name: Storage account name.
+    :vartype account_name: str
+    :ivar container_name: Storage account container name.
+    :vartype container_name: str
+    :ivar endpoint: Azure cloud endpoint for the storage account.
+    :vartype endpoint: str
+    :ivar protocol: Protocol used to communicate with the storage account.
+    :vartype protocol: str
+    :ivar service_data_access_auth_identity: Indicates which identity to use to authenticate
+     service data access to customer's storage. Possible values include: "None",
+     "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+    :vartype service_data_access_auth_identity: str or
+     ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+    """
+
+    _validation = {
+        'credentials': {'required': True},
+        'datastore_type': {'required': True},
+        'is_default': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'credentials': {'key': 'credentials', 'type': 'DatastoreCredentials'},
+        'datastore_type': {'key': 'datastoreType', 'type': 'str'},
+        'is_default': {'key': 'isDefault', 'type': 'bool'},
+        'account_name': {'key': 'accountName', 'type': 'str'},
+        'container_name': {'key': 'containerName', 'type': 'str'},
+        'endpoint': {'key': 'endpoint', 'type': 'str'},
+        'protocol': {'key': 'protocol', 'type': 'str'},
+        'service_data_access_auth_identity': {'key': 'serviceDataAccessAuthIdentity', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: Required. [Required] Account credentials.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword account_name: Storage account name.
+        :paramtype account_name: str
+        :keyword container_name: Storage account container name.
+        :paramtype container_name: str
+        :keyword endpoint: Azure cloud endpoint for the storage account.
+        :paramtype endpoint: str
+        :keyword protocol: Protocol used to communicate with the storage account.
+        :paramtype protocol: str
+        :keyword service_data_access_auth_identity: Indicates which identity to use to authenticate
+         service data access to customer's storage. Possible values include: "None",
+         "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+        :paramtype service_data_access_auth_identity: str or
+         ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+        """
+        super(AzureBlobDatastore, self).__init__(**kwargs)
+        self.datastore_type = 'AzureBlob'  # type: str
+        self.account_name = kwargs.get('account_name', None)
+        self.container_name = kwargs.get('container_name', None)
+        self.endpoint = kwargs.get('endpoint', None)
+        self.protocol = kwargs.get('protocol', None)
+        self.service_data_access_auth_identity = kwargs.get('service_data_access_auth_identity', None)
+
+
+class AzureDataLakeGen1Datastore(DatastoreProperties):
+    """Azure Data Lake Gen1 datastore configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: Required. [Required] Account credentials.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: Required. [Required] Storage type backing the datastore.Constant filled
+     by server. Possible values include: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2",
+     "AzureFile".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    :ivar service_data_access_auth_identity: Indicates which identity to use to authenticate
+     service data access to customer's storage. Possible values include: "None",
+     "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+    :vartype service_data_access_auth_identity: str or
+     ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+    :ivar store_name: Required. [Required] Azure Data Lake store name.
+    :vartype store_name: str
+    """
+
+    _validation = {
+        'credentials': {'required': True},
+        'datastore_type': {'required': True},
+        'is_default': {'readonly': True},
+        'store_name': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'credentials': {'key': 'credentials', 'type': 'DatastoreCredentials'},
+        'datastore_type': {'key': 'datastoreType', 'type': 'str'},
+        'is_default': {'key': 'isDefault', 'type': 'bool'},
+        'service_data_access_auth_identity': {'key': 'serviceDataAccessAuthIdentity', 'type': 'str'},
+        'store_name': {'key': 'storeName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: Required. [Required] Account credentials.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword service_data_access_auth_identity: Indicates which identity to use to authenticate
+         service data access to customer's storage. Possible values include: "None",
+         "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+        :paramtype service_data_access_auth_identity: str or
+         ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+        :keyword store_name: Required. [Required] Azure Data Lake store name.
+        :paramtype store_name: str
+        """
+        super(AzureDataLakeGen1Datastore, self).__init__(**kwargs)
+        self.datastore_type = 'AzureDataLakeGen1'  # type: str
+        self.service_data_access_auth_identity = kwargs.get('service_data_access_auth_identity', None)
+        self.store_name = kwargs['store_name']
+
+
+class AzureDataLakeGen2Datastore(DatastoreProperties):
+    """Azure Data Lake Gen2 datastore configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: Required. [Required] Account credentials.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: Required. [Required] Storage type backing the datastore.Constant filled
+     by server. Possible values include: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2",
+     "AzureFile".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    :ivar account_name: Required. [Required] Storage account name.
+    :vartype account_name: str
+    :ivar endpoint: Azure cloud endpoint for the storage account.
+    :vartype endpoint: str
+    :ivar filesystem: Required. [Required] The name of the Data Lake Gen2 filesystem.
+    :vartype filesystem: str
+    :ivar protocol: Protocol used to communicate with the storage account.
+    :vartype protocol: str
+    :ivar service_data_access_auth_identity: Indicates which identity to use to authenticate
+     service data access to customer's storage. Possible values include: "None",
+     "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+    :vartype service_data_access_auth_identity: str or
+     ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+    """
+
+    _validation = {
+        'credentials': {'required': True},
+        'datastore_type': {'required': True},
+        'is_default': {'readonly': True},
+        'account_name': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'filesystem': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'credentials': {'key': 'credentials', 'type': 'DatastoreCredentials'},
+        'datastore_type': {'key': 'datastoreType', 'type': 'str'},
+        'is_default': {'key': 'isDefault', 'type': 'bool'},
+        'account_name': {'key': 'accountName', 'type': 'str'},
+        'endpoint': {'key': 'endpoint', 'type': 'str'},
+        'filesystem': {'key': 'filesystem', 'type': 'str'},
+        'protocol': {'key': 'protocol', 'type': 'str'},
+        'service_data_access_auth_identity': {'key': 'serviceDataAccessAuthIdentity', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: Required. [Required] Account credentials.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword account_name: Required. [Required] Storage account name.
+        :paramtype account_name: str
+        :keyword endpoint: Azure cloud endpoint for the storage account.
+        :paramtype endpoint: str
+        :keyword filesystem: Required. [Required] The name of the Data Lake Gen2 filesystem.
+        :paramtype filesystem: str
+        :keyword protocol: Protocol used to communicate with the storage account.
+        :paramtype protocol: str
+        :keyword service_data_access_auth_identity: Indicates which identity to use to authenticate
+         service data access to customer's storage. Possible values include: "None",
+         "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+        :paramtype service_data_access_auth_identity: str or
+         ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+        """
+        super(AzureDataLakeGen2Datastore, self).__init__(**kwargs)
+        self.datastore_type = 'AzureDataLakeGen2'  # type: str
+        self.account_name = kwargs['account_name']
+        self.endpoint = kwargs.get('endpoint', None)
+        self.filesystem = kwargs['filesystem']
+        self.protocol = kwargs.get('protocol', None)
+        self.service_data_access_auth_identity = kwargs.get('service_data_access_auth_identity', None)
+
+
+class AzureFileDatastore(DatastoreProperties):
+    """Azure File datastore configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: Required. [Required] Account credentials.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: Required. [Required] Storage type backing the datastore.Constant filled
+     by server. Possible values include: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2",
+     "AzureFile".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    :ivar account_name: Required. [Required] Storage account name.
+    :vartype account_name: str
+    :ivar endpoint: Azure cloud endpoint for the storage account.
+    :vartype endpoint: str
+    :ivar file_share_name: Required. [Required] The name of the Azure file share that the datastore
+     points to.
+    :vartype file_share_name: str
+    :ivar protocol: Protocol used to communicate with the storage account.
+    :vartype protocol: str
+    :ivar service_data_access_auth_identity: Indicates which identity to use to authenticate
+     service data access to customer's storage. Possible values include: "None",
+     "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+    :vartype service_data_access_auth_identity: str or
+     ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+    """
+
+    _validation = {
+        'credentials': {'required': True},
+        'datastore_type': {'required': True},
+        'is_default': {'readonly': True},
+        'account_name': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'file_share_name': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'credentials': {'key': 'credentials', 'type': 'DatastoreCredentials'},
+        'datastore_type': {'key': 'datastoreType', 'type': 'str'},
+        'is_default': {'key': 'isDefault', 'type': 'bool'},
+        'account_name': {'key': 'accountName', 'type': 'str'},
+        'endpoint': {'key': 'endpoint', 'type': 'str'},
+        'file_share_name': {'key': 'fileShareName', 'type': 'str'},
+        'protocol': {'key': 'protocol', 'type': 'str'},
+        'service_data_access_auth_identity': {'key': 'serviceDataAccessAuthIdentity', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: Required. [Required] Account credentials.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword account_name: Required. [Required] Storage account name.
+        :paramtype account_name: str
+        :keyword endpoint: Azure cloud endpoint for the storage account.
+        :paramtype endpoint: str
+        :keyword file_share_name: Required. [Required] The name of the Azure file share that the
+         datastore points to.
+        :paramtype file_share_name: str
+        :keyword protocol: Protocol used to communicate with the storage account.
+        :paramtype protocol: str
+        :keyword service_data_access_auth_identity: Indicates which identity to use to authenticate
+         service data access to customer's storage. Possible values include: "None",
+         "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+        :paramtype service_data_access_auth_identity: str or
+         ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+        """
+        super(AzureFileDatastore, self).__init__(**kwargs)
+        self.datastore_type = 'AzureFile'  # type: str
+        self.account_name = kwargs['account_name']
+        self.endpoint = kwargs.get('endpoint', None)
+        self.file_share_name = kwargs['file_share_name']
+        self.protocol = kwargs.get('protocol', None)
+        self.service_data_access_auth_identity = kwargs.get('service_data_access_auth_identity', None)
+
+
+class EarlyTerminationPolicy(msrest.serialization.Model):
+    """Early termination policies enable canceling poor-performing runs before they complete.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: BanditPolicy, MedianStoppingPolicy, TruncationSelectionPolicy.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar delay_evaluation: Number of intervals by which to delay the first evaluation.
+    :vartype delay_evaluation: int
+    :ivar evaluation_interval: Interval (number of runs) between policy evaluations.
+    :vartype evaluation_interval: int
+    :ivar policy_type: Required. [Required] Name of policy configuration.Constant filled by server.
+     Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
+    :vartype policy_type: str or
+     ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicyType
+    """
+
+    _validation = {
+        'policy_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
+        'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
+        'policy_type': {'key': 'policyType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'policy_type': {'Bandit': 'BanditPolicy', 'MedianStopping': 'MedianStoppingPolicy', 'TruncationSelection': 'TruncationSelectionPolicy'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
+        :paramtype delay_evaluation: int
+        :keyword evaluation_interval: Interval (number of runs) between policy evaluations.
+        :paramtype evaluation_interval: int
+        """
+        super(EarlyTerminationPolicy, self).__init__(**kwargs)
+        self.delay_evaluation = kwargs.get('delay_evaluation', 0)
+        self.evaluation_interval = kwargs.get('evaluation_interval', 0)
+        self.policy_type = None  # type: Optional[str]
+
+
+class BanditPolicy(EarlyTerminationPolicy):
+    """Defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar delay_evaluation: Number of intervals by which to delay the first evaluation.
+    :vartype delay_evaluation: int
+    :ivar evaluation_interval: Interval (number of runs) between policy evaluations.
+    :vartype evaluation_interval: int
+    :ivar policy_type: Required. [Required] Name of policy configuration.Constant filled by server.
+     Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
+    :vartype policy_type: str or
+     ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicyType
+    :ivar slack_amount: Absolute distance allowed from the best performing run.
+    :vartype slack_amount: float
+    :ivar slack_factor: Ratio of the allowed distance from the best performing run.
+    :vartype slack_factor: float
+    """
+
+    _validation = {
+        'policy_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
+        'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
+        'policy_type': {'key': 'policyType', 'type': 'str'},
+        'slack_amount': {'key': 'slackAmount', 'type': 'float'},
+        'slack_factor': {'key': 'slackFactor', 'type': 'float'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
+        :paramtype delay_evaluation: int
+        :keyword evaluation_interval: Interval (number of runs) between policy evaluations.
+        :paramtype evaluation_interval: int
+        :keyword slack_amount: Absolute distance allowed from the best performing run.
+        :paramtype slack_amount: float
+        :keyword slack_factor: Ratio of the allowed distance from the best performing run.
+        :paramtype slack_factor: float
+        """
+        super(BanditPolicy, self).__init__(**kwargs)
+        self.policy_type = 'Bandit'  # type: str
+        self.slack_amount = kwargs.get('slack_amount', 0)
+        self.slack_factor = kwargs.get('slack_factor', 0)
+
+
+class Resource(msrest.serialization.Model):
+    """Common fields that are returned in the response for all Azure Resource Manager resources.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(Resource, self).__init__(**kwargs)
+        self.id = None
+        self.name = None
+        self.type = None
+        self.system_data = None
+
+
+class TrackedResource(Resource):
+    """The resource model definition for an Azure Resource Manager tracked top level resource which has 'tags' and a 'location'.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: Required. The geo-location where the resource lives.
+    :vartype location: str
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'location': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'location': {'key': 'location', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: Required. The geo-location where the resource lives.
+        :paramtype location: str
+        """
+        super(TrackedResource, self).__init__(**kwargs)
+        self.tags = kwargs.get('tags', None)
+        self.location = kwargs['location']
+
+
+class BatchDeployment(TrackedResource):
+    """BatchDeployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: Required. The geo-location where the resource lives.
+    :vartype location: str
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.BatchDeploymentProperties
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'location': {'required': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'location': {'key': 'location', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'kind': {'key': 'kind', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': 'BatchDeploymentProperties'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: Required. The geo-location where the resource lives.
+        :paramtype location: str
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.BatchDeploymentProperties
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super(BatchDeployment, self).__init__(**kwargs)
+        self.identity = kwargs.get('identity', None)
+        self.kind = kwargs.get('kind', None)
+        self.properties = kwargs['properties']
+        self.sku = kwargs.get('sku', None)
+
+
+class EndpointDeploymentPropertiesBase(msrest.serialization.Model):
+    """Base definition for endpoint deployment.
+
+    :ivar code_configuration: Code configuration for the endpoint deployment.
+    :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
+     endpoint deployment.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables configuration for the deployment.
+    :vartype environment_variables: dict[str, str]
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    """
+
+    _attribute_map = {
+        'code_configuration': {'key': 'codeConfiguration', 'type': 'CodeConfiguration'},
+        'description': {'key': 'description', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword code_configuration: Code configuration for the endpoint deployment.
+        :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
+         endpoint deployment.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables configuration for the deployment.
+        :paramtype environment_variables: dict[str, str]
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        """
+        super(EndpointDeploymentPropertiesBase, self).__init__(**kwargs)
+        self.code_configuration = kwargs.get('code_configuration', None)
+        self.description = kwargs.get('description', None)
+        self.environment_id = kwargs.get('environment_id', None)
+        self.environment_variables = kwargs.get('environment_variables', None)
+        self.properties = kwargs.get('properties', None)
+
+
+class BatchDeploymentProperties(EndpointDeploymentPropertiesBase):
+    """Batch inference settings per deployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar code_configuration: Code configuration for the endpoint deployment.
+    :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
+     endpoint deployment.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables configuration for the deployment.
+    :vartype environment_variables: dict[str, str]
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar compute: Compute target for batch inference operation.
+    :vartype compute: str
+    :ivar error_threshold: Error threshold, if the error count for the entire input goes above this
+     value,
+     the batch inference will be aborted. Range is [-1, int.MaxValue].
+     For FileDataset, this value is the count of file failures.
+     For TabularDataset, this value is the count of record failures.
+     If set to -1 (the lower bound), all failures during batch inference will be ignored.
+    :vartype error_threshold: int
+    :ivar logging_level: Logging level for batch inference operation. Possible values include:
+     "Info", "Warning", "Debug".
+    :vartype logging_level: str or ~azure.mgmt.machinelearningservices.models.BatchLoggingLevel
+    :ivar max_concurrency_per_instance: Indicates maximum number of parallelism per instance.
+    :vartype max_concurrency_per_instance: int
+    :ivar mini_batch_size: Size of the mini-batch passed to each batch invocation.
+     For FileDataset, this is the number of files per mini-batch.
+     For TabularDataset, this is the size of the records in bytes, per mini-batch.
+    :vartype mini_batch_size: long
+    :ivar model: Reference to the model asset for the endpoint deployment.
+    :vartype model: ~azure.mgmt.machinelearningservices.models.AssetReferenceBase
+    :ivar output_action: Indicates how the output will be organized. Possible values include:
+     "SummaryOnly", "AppendRow".
+    :vartype output_action: str or ~azure.mgmt.machinelearningservices.models.BatchOutputAction
+    :ivar output_file_name: Customized output file name for append_row output action.
+    :vartype output_file_name: str
+    :ivar provisioning_state: Provisioning state for the endpoint deployment. Possible values
+     include: "Creating", "Deleting", "Scaling", "Updating", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.DeploymentProvisioningState
+    :ivar resources: Indicates compute configuration for the job.
+     If not provided, will default to the defaults defined in ResourceConfiguration.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.DeploymentResourceConfiguration
+    :ivar retry_settings: Retry Settings for the batch inference operation.
+     If not provided, will default to the defaults defined in BatchRetrySettings.
+    :vartype retry_settings: ~azure.mgmt.machinelearningservices.models.BatchRetrySettings
+    """
+
+    _validation = {
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code_configuration': {'key': 'codeConfiguration', 'type': 'CodeConfiguration'},
+        'description': {'key': 'description', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'compute': {'key': 'compute', 'type': 'str'},
+        'error_threshold': {'key': 'errorThreshold', 'type': 'int'},
+        'logging_level': {'key': 'loggingLevel', 'type': 'str'},
+        'max_concurrency_per_instance': {'key': 'maxConcurrencyPerInstance', 'type': 'int'},
+        'mini_batch_size': {'key': 'miniBatchSize', 'type': 'long'},
+        'model': {'key': 'model', 'type': 'AssetReferenceBase'},
+        'output_action': {'key': 'outputAction', 'type': 'str'},
+        'output_file_name': {'key': 'outputFileName', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'resources': {'key': 'resources', 'type': 'DeploymentResourceConfiguration'},
+        'retry_settings': {'key': 'retrySettings', 'type': 'BatchRetrySettings'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword code_configuration: Code configuration for the endpoint deployment.
+        :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
+         endpoint deployment.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables configuration for the deployment.
+        :paramtype environment_variables: dict[str, str]
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword compute: Compute target for batch inference operation.
+        :paramtype compute: str
+        :keyword error_threshold: Error threshold, if the error count for the entire input goes above
+         this value,
+         the batch inference will be aborted. Range is [-1, int.MaxValue].
+         For FileDataset, this value is the count of file failures.
+         For TabularDataset, this value is the count of record failures.
+         If set to -1 (the lower bound), all failures during batch inference will be ignored.
+        :paramtype error_threshold: int
+        :keyword logging_level: Logging level for batch inference operation. Possible values include:
+         "Info", "Warning", "Debug".
+        :paramtype logging_level: str or ~azure.mgmt.machinelearningservices.models.BatchLoggingLevel
+        :keyword max_concurrency_per_instance: Indicates maximum number of parallelism per instance.
+        :paramtype max_concurrency_per_instance: int
+        :keyword mini_batch_size: Size of the mini-batch passed to each batch invocation.
+         For FileDataset, this is the number of files per mini-batch.
+         For TabularDataset, this is the size of the records in bytes, per mini-batch.
+        :paramtype mini_batch_size: long
+        :keyword model: Reference to the model asset for the endpoint deployment.
+        :paramtype model: ~azure.mgmt.machinelearningservices.models.AssetReferenceBase
+        :keyword output_action: Indicates how the output will be organized. Possible values include:
+         "SummaryOnly", "AppendRow".
+        :paramtype output_action: str or ~azure.mgmt.machinelearningservices.models.BatchOutputAction
+        :keyword output_file_name: Customized output file name for append_row output action.
+        :paramtype output_file_name: str
+        :keyword resources: Indicates compute configuration for the job.
+         If not provided, will default to the defaults defined in ResourceConfiguration.
+        :paramtype resources:
+         ~azure.mgmt.machinelearningservices.models.DeploymentResourceConfiguration
+        :keyword retry_settings: Retry Settings for the batch inference operation.
+         If not provided, will default to the defaults defined in BatchRetrySettings.
+        :paramtype retry_settings: ~azure.mgmt.machinelearningservices.models.BatchRetrySettings
+        """
+        super(BatchDeploymentProperties, self).__init__(**kwargs)
+        self.compute = kwargs.get('compute', None)
+        self.error_threshold = kwargs.get('error_threshold', -1)
+        self.logging_level = kwargs.get('logging_level', None)
+        self.max_concurrency_per_instance = kwargs.get('max_concurrency_per_instance', 1)
+        self.mini_batch_size = kwargs.get('mini_batch_size', 10)
+        self.model = kwargs.get('model', None)
+        self.output_action = kwargs.get('output_action', None)
+        self.output_file_name = kwargs.get('output_file_name', "predictions.csv")
+        self.provisioning_state = None
+        self.resources = kwargs.get('resources', None)
+        self.retry_settings = kwargs.get('retry_settings', None)
+
+
+class BatchDeploymentTrackedResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of BatchDeployment entities.
+
+    :ivar next_link: The link to the next page of BatchDeployment objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type BatchDeployment.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.BatchDeployment]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[BatchDeployment]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of BatchDeployment objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type BatchDeployment.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.BatchDeployment]
+        """
+        super(BatchDeploymentTrackedResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class BatchEndpoint(TrackedResource):
+    """BatchEndpoint.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: Required. The geo-location where the resource lives.
+    :vartype location: str
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.BatchEndpointProperties
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'location': {'required': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'location': {'key': 'location', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'kind': {'key': 'kind', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': 'BatchEndpointProperties'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: Required. The geo-location where the resource lives.
+        :paramtype location: str
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.BatchEndpointProperties
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super(BatchEndpoint, self).__init__(**kwargs)
+        self.identity = kwargs.get('identity', None)
+        self.kind = kwargs.get('kind', None)
+        self.properties = kwargs['properties']
+        self.sku = kwargs.get('sku', None)
+
+
+class BatchEndpointDefaults(msrest.serialization.Model):
+    """Batch endpoint default values.
+
+    :ivar deployment_name: Name of the deployment that will be default for the endpoint.
+     This deployment will end up getting 100% traffic when the endpoint scoring URL is invoked.
+    :vartype deployment_name: str
+    """
+
+    _attribute_map = {
+        'deployment_name': {'key': 'deploymentName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword deployment_name: Name of the deployment that will be default for the endpoint.
+         This deployment will end up getting 100% traffic when the endpoint scoring URL is invoked.
+        :paramtype deployment_name: str
+        """
+        super(BatchEndpointDefaults, self).__init__(**kwargs)
+        self.deployment_name = kwargs.get('deployment_name', None)
+
+
+class EndpointPropertiesBase(msrest.serialization.Model):
+    """Inference Endpoint base definition.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken' for
+     Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
+     Possible values include: "AMLToken", "Key", "AADToken".
+    :vartype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+    :ivar description: Description of the inference endpoint.
+    :vartype description: str
+    :ivar keys: EndpointAuthKeys to set initially on an Endpoint.
+     This property will always be returned as null. AuthKey values must be retrieved using the
+     ListKeys API.
+    :vartype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar scoring_uri: Endpoint URI.
+    :vartype scoring_uri: str
+    :ivar swagger_uri: Endpoint Swagger URI.
+    :vartype swagger_uri: str
+    """
+
+    _validation = {
+        'auth_mode': {'required': True},
+        'scoring_uri': {'readonly': True},
+        'swagger_uri': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'auth_mode': {'key': 'authMode', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'keys': {'key': 'keys', 'type': 'EndpointAuthKeys'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'scoring_uri': {'key': 'scoringUri', 'type': 'str'},
+        'swagger_uri': {'key': 'swaggerUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken'
+         for Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken'
+         does. Possible values include: "AMLToken", "Key", "AADToken".
+        :paramtype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+        :keyword description: Description of the inference endpoint.
+        :paramtype description: str
+        :keyword keys: EndpointAuthKeys to set initially on an Endpoint.
+         This property will always be returned as null. AuthKey values must be retrieved using the
+         ListKeys API.
+        :paramtype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        """
+        super(EndpointPropertiesBase, self).__init__(**kwargs)
+        self.auth_mode = kwargs['auth_mode']
+        self.description = kwargs.get('description', None)
+        self.keys = kwargs.get('keys', None)
+        self.properties = kwargs.get('properties', None)
+        self.scoring_uri = None
+        self.swagger_uri = None
+
+
+class BatchEndpointProperties(EndpointPropertiesBase):
+    """Batch endpoint configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken' for
+     Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
+     Possible values include: "AMLToken", "Key", "AADToken".
+    :vartype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+    :ivar description: Description of the inference endpoint.
+    :vartype description: str
+    :ivar keys: EndpointAuthKeys to set initially on an Endpoint.
+     This property will always be returned as null. AuthKey values must be retrieved using the
+     ListKeys API.
+    :vartype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar scoring_uri: Endpoint URI.
+    :vartype scoring_uri: str
+    :ivar swagger_uri: Endpoint Swagger URI.
+    :vartype swagger_uri: str
+    :ivar defaults: Default values for Batch Endpoint.
+    :vartype defaults: ~azure.mgmt.machinelearningservices.models.BatchEndpointDefaults
+    :ivar provisioning_state: Provisioning state for the endpoint. Possible values include:
+     "Creating", "Deleting", "Succeeded", "Failed", "Updating", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.EndpointProvisioningState
+    """
+
+    _validation = {
+        'auth_mode': {'required': True},
+        'scoring_uri': {'readonly': True},
+        'swagger_uri': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'auth_mode': {'key': 'authMode', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'keys': {'key': 'keys', 'type': 'EndpointAuthKeys'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'scoring_uri': {'key': 'scoringUri', 'type': 'str'},
+        'swagger_uri': {'key': 'swaggerUri', 'type': 'str'},
+        'defaults': {'key': 'defaults', 'type': 'BatchEndpointDefaults'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken'
+         for Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken'
+         does. Possible values include: "AMLToken", "Key", "AADToken".
+        :paramtype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+        :keyword description: Description of the inference endpoint.
+        :paramtype description: str
+        :keyword keys: EndpointAuthKeys to set initially on an Endpoint.
+         This property will always be returned as null. AuthKey values must be retrieved using the
+         ListKeys API.
+        :paramtype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword defaults: Default values for Batch Endpoint.
+        :paramtype defaults: ~azure.mgmt.machinelearningservices.models.BatchEndpointDefaults
+        """
+        super(BatchEndpointProperties, self).__init__(**kwargs)
+        self.defaults = kwargs.get('defaults', None)
+        self.provisioning_state = None
+
+
+class BatchEndpointTrackedResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of BatchEndpoint entities.
+
+    :ivar next_link: The link to the next page of BatchEndpoint objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type BatchEndpoint.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.BatchEndpoint]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[BatchEndpoint]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of BatchEndpoint objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type BatchEndpoint.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.BatchEndpoint]
+        """
+        super(BatchEndpointTrackedResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class BatchRetrySettings(msrest.serialization.Model):
+    """Retry settings for a batch inference operation.
+
+    :ivar max_retries: Maximum retry count for a mini-batch.
+    :vartype max_retries: int
+    :ivar timeout: Invocation timeout for a mini-batch, in ISO 8601 format.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'max_retries': {'key': 'maxRetries', 'type': 'int'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword max_retries: Maximum retry count for a mini-batch.
+        :paramtype max_retries: int
+        :keyword timeout: Invocation timeout for a mini-batch, in ISO 8601 format.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(BatchRetrySettings, self).__init__(**kwargs)
+        self.max_retries = kwargs.get('max_retries', 3)
+        self.timeout = kwargs.get('timeout', "PT30S")
+
+
+class SamplingAlgorithm(msrest.serialization.Model):
+    """The Sampling Algorithm used to generate hyperparameter values, along with properties to
+configure the algorithm.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: BayesianSamplingAlgorithm, GridSamplingAlgorithm, RandomSamplingAlgorithm.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar sampling_algorithm_type: Required. [Required] The algorithm used for generating
+     hyperparameter values, along with configuration properties.Constant filled by server. Possible
+     values include: "Grid", "Random", "Bayesian".
+    :vartype sampling_algorithm_type: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    """
+
+    _validation = {
+        'sampling_algorithm_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'sampling_algorithm_type': {'key': 'samplingAlgorithmType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'sampling_algorithm_type': {'Bayesian': 'BayesianSamplingAlgorithm', 'Grid': 'GridSamplingAlgorithm', 'Random': 'RandomSamplingAlgorithm'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(SamplingAlgorithm, self).__init__(**kwargs)
+        self.sampling_algorithm_type = None  # type: Optional[str]
+
+
+class BayesianSamplingAlgorithm(SamplingAlgorithm):
+    """Defines a Sampling Algorithm that generates values based on previous values.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar sampling_algorithm_type: Required. [Required] The algorithm used for generating
+     hyperparameter values, along with configuration properties.Constant filled by server. Possible
+     values include: "Grid", "Random", "Bayesian".
+    :vartype sampling_algorithm_type: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    """
+
+    _validation = {
+        'sampling_algorithm_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'sampling_algorithm_type': {'key': 'samplingAlgorithmType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(BayesianSamplingAlgorithm, self).__init__(**kwargs)
+        self.sampling_algorithm_type = 'Bayesian'  # type: str
+
+
+class BuildContext(msrest.serialization.Model):
+    """Configuration settings for Docker build context.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar context_uri: Required. [Required] URI of the Docker build context used to build the
+     image. Supports blob URIs on environment creation and may return blob or Git URIs.
+    
+    
+     .. raw:: html
+    
+        <seealso
+     href="https://docs.docker.com/engine/reference/commandline/build/#extended-description" />.
+    :vartype context_uri: str
+    :ivar dockerfile_path: Path to the Dockerfile in the build context.
+    
+    
+     .. raw:: html
+    
+        <seealso href="https://docs.docker.com/engine/reference/builder/" />.
+    :vartype dockerfile_path: str
+    """
+
+    _validation = {
+        'context_uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'context_uri': {'key': 'contextUri', 'type': 'str'},
+        'dockerfile_path': {'key': 'dockerfilePath', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword context_uri: Required. [Required] URI of the Docker build context used to build the
+         image. Supports blob URIs on environment creation and may return blob or Git URIs.
+        
+        
+         .. raw:: html
+        
+            <seealso
+         href="https://docs.docker.com/engine/reference/commandline/build/#extended-description" />.
+        :paramtype context_uri: str
+        :keyword dockerfile_path: Path to the Dockerfile in the build context.
+        
+        
+         .. raw:: html
+        
+            <seealso href="https://docs.docker.com/engine/reference/builder/" />.
+        :paramtype dockerfile_path: str
+        """
+        super(BuildContext, self).__init__(**kwargs)
+        self.context_uri = kwargs['context_uri']
+        self.dockerfile_path = kwargs.get('dockerfile_path', "Dockerfile")
+
+
+class CertificateDatastoreCredentials(DatastoreCredentials):
+    """Certificate datastore credentials configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    :ivar authority_url: Authority URL used for authentication.
+    :vartype authority_url: str
+    :ivar client_id: Required. [Required] Service principal client ID.
+    :vartype client_id: str
+    :ivar resource_url: Resource the service principal has access to.
+    :vartype resource_url: str
+    :ivar secrets: Required. [Required] Service principal secrets.
+    :vartype secrets: ~azure.mgmt.machinelearningservices.models.CertificateDatastoreSecrets
+    :ivar tenant_id: Required. [Required] ID of the tenant to which the service principal belongs.
+    :vartype tenant_id: str
+    :ivar thumbprint: Required. [Required] Thumbprint of the certificate used for authentication.
+    :vartype thumbprint: str
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+        'client_id': {'required': True},
+        'secrets': {'required': True},
+        'tenant_id': {'required': True},
+        'thumbprint': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+        'authority_url': {'key': 'authorityUrl', 'type': 'str'},
+        'client_id': {'key': 'clientId', 'type': 'str'},
+        'resource_url': {'key': 'resourceUrl', 'type': 'str'},
+        'secrets': {'key': 'secrets', 'type': 'CertificateDatastoreSecrets'},
+        'tenant_id': {'key': 'tenantId', 'type': 'str'},
+        'thumbprint': {'key': 'thumbprint', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword authority_url: Authority URL used for authentication.
+        :paramtype authority_url: str
+        :keyword client_id: Required. [Required] Service principal client ID.
+        :paramtype client_id: str
+        :keyword resource_url: Resource the service principal has access to.
+        :paramtype resource_url: str
+        :keyword secrets: Required. [Required] Service principal secrets.
+        :paramtype secrets: ~azure.mgmt.machinelearningservices.models.CertificateDatastoreSecrets
+        :keyword tenant_id: Required. [Required] ID of the tenant to which the service principal
+         belongs.
+        :paramtype tenant_id: str
+        :keyword thumbprint: Required. [Required] Thumbprint of the certificate used for
+         authentication.
+        :paramtype thumbprint: str
+        """
+        super(CertificateDatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = 'Certificate'  # type: str
+        self.authority_url = kwargs.get('authority_url', None)
+        self.client_id = kwargs['client_id']
+        self.resource_url = kwargs.get('resource_url', None)
+        self.secrets = kwargs['secrets']
+        self.tenant_id = kwargs['tenant_id']
+        self.thumbprint = kwargs['thumbprint']
+
+
+class CertificateDatastoreSecrets(DatastoreSecrets):
+    """Datastore certificate secrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate", "Sas",
+     "ServicePrincipal".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    :ivar certificate: Service principal certificate.
+    :vartype certificate: str
+    """
+
+    _validation = {
+        'secrets_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'secrets_type': {'key': 'secretsType', 'type': 'str'},
+        'certificate': {'key': 'certificate', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword certificate: Service principal certificate.
+        :paramtype certificate: str
+        """
+        super(CertificateDatastoreSecrets, self).__init__(**kwargs)
+        self.secrets_type = 'Certificate'  # type: str
+        self.certificate = kwargs.get('certificate', None)
+
+
+class TableVertical(msrest.serialization.Model):
+    """Abstract class for AutoML tasks that use table dataset as input - such as Classification/Regression/Forecasting.
+
+    :ivar cv_split_column_names: Columns to use for CVSplit data.
+    :vartype cv_split_column_names: list[str]
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+    :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
+     when validation dataset is not provided.
+    :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar test_data: Test data input.
+    :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
+     purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype test_data_size: float
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar weight_column_name: The name of the sample weight column. Automated ML supports a
+     weighted column as an input, causing rows in the data to be weighted up or down.
+    :vartype weight_column_name: str
+    """
+
+    _attribute_map = {
+        'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'TableVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'TableVerticalLimitSettings'},
+        'n_cross_validations': {'key': 'nCrossValidations', 'type': 'NCrossValidations'},
+        'test_data': {'key': 'testData', 'type': 'MLTableJobInput'},
+        'test_data_size': {'key': 'testDataSize', 'type': 'float'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword cv_split_column_names: Columns to use for CVSplit data.
+        :paramtype cv_split_column_names: list[str]
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+        :keyword n_cross_validations: Number of cross validation folds to be applied on training
+         dataset
+         when validation dataset is not provided.
+        :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword test_data: Test data input.
+        :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
+         purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype test_data_size: float
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword weight_column_name: The name of the sample weight column. Automated ML supports a
+         weighted column as an input, causing rows in the data to be weighted up or down.
+        :paramtype weight_column_name: str
+        """
+        super(TableVertical, self).__init__(**kwargs)
+        self.cv_split_column_names = kwargs.get('cv_split_column_names', None)
+        self.featurization_settings = kwargs.get('featurization_settings', None)
+        self.limit_settings = kwargs.get('limit_settings', None)
+        self.n_cross_validations = kwargs.get('n_cross_validations', None)
+        self.test_data = kwargs.get('test_data', None)
+        self.test_data_size = kwargs.get('test_data_size', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.validation_data_size = kwargs.get('validation_data_size', None)
+        self.weight_column_name = kwargs.get('weight_column_name', None)
+
+
+class Classification(AutoMLVertical, TableVertical):
+    """Classification task in AutoML Table vertical.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar cv_split_column_names: Columns to use for CVSplit data.
+    :vartype cv_split_column_names: list[str]
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+    :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
+     when validation dataset is not provided.
+    :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar test_data: Test data input.
+    :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
+     purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype test_data_size: float
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar weight_column_name: The name of the sample weight column. Automated ML supports a
+     weighted column as an input, causing rows in the data to be weighted up or down.
+    :vartype weight_column_name: str
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar positive_label: Positive label for binary metrics calculation.
+    :vartype positive_label: str
+    :ivar primary_metric: Primary metric for the task. Possible values include: "AUCWeighted",
+     "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    :ivar training_settings: Inputs for training phase for an AutoML Job.
+    :vartype training_settings:
+     ~azure.mgmt.machinelearningservices.models.ClassificationTrainingSettings
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'TableVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'TableVerticalLimitSettings'},
+        'n_cross_validations': {'key': 'nCrossValidations', 'type': 'NCrossValidations'},
+        'test_data': {'key': 'testData', 'type': 'MLTableJobInput'},
+        'test_data_size': {'key': 'testDataSize', 'type': 'float'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'positive_label': {'key': 'positiveLabel', 'type': 'str'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+        'training_settings': {'key': 'trainingSettings', 'type': 'ClassificationTrainingSettings'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword cv_split_column_names: Columns to use for CVSplit data.
+        :paramtype cv_split_column_names: list[str]
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+        :keyword n_cross_validations: Number of cross validation folds to be applied on training
+         dataset
+         when validation dataset is not provided.
+        :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword test_data: Test data input.
+        :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
+         purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype test_data_size: float
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword weight_column_name: The name of the sample weight column. Automated ML supports a
+         weighted column as an input, causing rows in the data to be weighted up or down.
+        :paramtype weight_column_name: str
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword positive_label: Positive label for binary metrics calculation.
+        :paramtype positive_label: str
+        :keyword primary_metric: Primary metric for the task. Possible values include: "AUCWeighted",
+         "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", "PrecisionScoreWeighted".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+        :keyword training_settings: Inputs for training phase for an AutoML Job.
+        :paramtype training_settings:
+         ~azure.mgmt.machinelearningservices.models.ClassificationTrainingSettings
+        """
+        super(Classification, self).__init__(**kwargs)
+        self.cv_split_column_names = kwargs.get('cv_split_column_names', None)
+        self.featurization_settings = kwargs.get('featurization_settings', None)
+        self.limit_settings = kwargs.get('limit_settings', None)
+        self.n_cross_validations = kwargs.get('n_cross_validations', None)
+        self.test_data = kwargs.get('test_data', None)
+        self.test_data_size = kwargs.get('test_data_size', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.validation_data_size = kwargs.get('validation_data_size', None)
+        self.weight_column_name = kwargs.get('weight_column_name', None)
+        self.task_type = 'Classification'  # type: str
+        self.positive_label = kwargs.get('positive_label', None)
+        self.primary_metric = kwargs.get('primary_metric', None)
+        self.training_settings = kwargs.get('training_settings', None)
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.training_data = kwargs['training_data']
+
+
+class TrainingSettings(msrest.serialization.Model):
+    """Training related configuration.
+
+    :ivar enable_dnn_training: Enable recommendation of DNN models.
+    :vartype enable_dnn_training: bool
+    :ivar enable_model_explainability: Flag to turn on explainability on best model.
+    :vartype enable_model_explainability: bool
+    :ivar enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+    :vartype enable_onnx_compatible_models: bool
+    :ivar enable_stack_ensemble: Enable stack ensemble run.
+    :vartype enable_stack_ensemble: bool
+    :ivar enable_vote_ensemble: Enable voting ensemble run.
+    :vartype enable_vote_ensemble: bool
+    :ivar ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+     generation, multiple fitted models from the previous child runs are downloaded.
+     Configure this parameter with a higher value than 300 secs, if more time is needed.
+    :vartype ensemble_model_download_timeout: ~datetime.timedelta
+    :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+    :vartype stack_ensemble_settings:
+     ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    """
+
+    _attribute_map = {
+        'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
+        'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
+        'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
+        'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
+        'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
+        'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'duration'},
+        'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'StackEnsembleSettings'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword enable_dnn_training: Enable recommendation of DNN models.
+        :paramtype enable_dnn_training: bool
+        :keyword enable_model_explainability: Flag to turn on explainability on best model.
+        :paramtype enable_model_explainability: bool
+        :keyword enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+        :paramtype enable_onnx_compatible_models: bool
+        :keyword enable_stack_ensemble: Enable stack ensemble run.
+        :paramtype enable_stack_ensemble: bool
+        :keyword enable_vote_ensemble: Enable voting ensemble run.
+        :paramtype enable_vote_ensemble: bool
+        :keyword ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+         generation, multiple fitted models from the previous child runs are downloaded.
+         Configure this parameter with a higher value than 300 secs, if more time is needed.
+        :paramtype ensemble_model_download_timeout: ~datetime.timedelta
+        :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+        :paramtype stack_ensemble_settings:
+         ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        """
+        super(TrainingSettings, self).__init__(**kwargs)
+        self.enable_dnn_training = kwargs.get('enable_dnn_training', False)
+        self.enable_model_explainability = kwargs.get('enable_model_explainability', True)
+        self.enable_onnx_compatible_models = kwargs.get('enable_onnx_compatible_models', False)
+        self.enable_stack_ensemble = kwargs.get('enable_stack_ensemble', True)
+        self.enable_vote_ensemble = kwargs.get('enable_vote_ensemble', True)
+        self.ensemble_model_download_timeout = kwargs.get('ensemble_model_download_timeout', "PT5M")
+        self.stack_ensemble_settings = kwargs.get('stack_ensemble_settings', None)
+
+
+class ClassificationTrainingSettings(TrainingSettings):
+    """Classification Training related configuration.
+
+    :ivar enable_dnn_training: Enable recommendation of DNN models.
+    :vartype enable_dnn_training: bool
+    :ivar enable_model_explainability: Flag to turn on explainability on best model.
+    :vartype enable_model_explainability: bool
+    :ivar enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+    :vartype enable_onnx_compatible_models: bool
+    :ivar enable_stack_ensemble: Enable stack ensemble run.
+    :vartype enable_stack_ensemble: bool
+    :ivar enable_vote_ensemble: Enable voting ensemble run.
+    :vartype enable_vote_ensemble: bool
+    :ivar ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+     generation, multiple fitted models from the previous child runs are downloaded.
+     Configure this parameter with a higher value than 300 secs, if more time is needed.
+    :vartype ensemble_model_download_timeout: ~datetime.timedelta
+    :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+    :vartype stack_ensemble_settings:
+     ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    :ivar allowed_training_algorithms: Allowed models for classification task.
+    :vartype allowed_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationModels]
+    :ivar blocked_training_algorithms: Blocked models for classification task.
+    :vartype blocked_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationModels]
+    """
+
+    _attribute_map = {
+        'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
+        'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
+        'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
+        'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
+        'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
+        'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'duration'},
+        'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'StackEnsembleSettings'},
+        'allowed_training_algorithms': {'key': 'allowedTrainingAlgorithms', 'type': '[str]'},
+        'blocked_training_algorithms': {'key': 'blockedTrainingAlgorithms', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword enable_dnn_training: Enable recommendation of DNN models.
+        :paramtype enable_dnn_training: bool
+        :keyword enable_model_explainability: Flag to turn on explainability on best model.
+        :paramtype enable_model_explainability: bool
+        :keyword enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+        :paramtype enable_onnx_compatible_models: bool
+        :keyword enable_stack_ensemble: Enable stack ensemble run.
+        :paramtype enable_stack_ensemble: bool
+        :keyword enable_vote_ensemble: Enable voting ensemble run.
+        :paramtype enable_vote_ensemble: bool
+        :keyword ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+         generation, multiple fitted models from the previous child runs are downloaded.
+         Configure this parameter with a higher value than 300 secs, if more time is needed.
+        :paramtype ensemble_model_download_timeout: ~datetime.timedelta
+        :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+        :paramtype stack_ensemble_settings:
+         ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        :keyword allowed_training_algorithms: Allowed models for classification task.
+        :paramtype allowed_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationModels]
+        :keyword blocked_training_algorithms: Blocked models for classification task.
+        :paramtype blocked_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationModels]
+        """
+        super(ClassificationTrainingSettings, self).__init__(**kwargs)
+        self.allowed_training_algorithms = kwargs.get('allowed_training_algorithms', None)
+        self.blocked_training_algorithms = kwargs.get('blocked_training_algorithms', None)
+
+
+class ClusterUpdateParameters(msrest.serialization.Model):
+    """AmlCompute update parameters.
+
+    :ivar properties: Properties of ClusterUpdate.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ScaleSettingsInformation
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties.properties', 'type': 'ScaleSettingsInformation'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of ClusterUpdate.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ScaleSettingsInformation
+        """
+        super(ClusterUpdateParameters, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class CodeConfiguration(msrest.serialization.Model):
+    """Configuration for a scoring code asset.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar code_id: ARM resource ID of the code asset.
+    :vartype code_id: str
+    :ivar scoring_script: Required. [Required] The script to execute on startup. eg. "score.py".
+    :vartype scoring_script: str
+    """
+
+    _validation = {
+        'scoring_script': {'required': True, 'min_length': 1, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'code_id': {'key': 'codeId', 'type': 'str'},
+        'scoring_script': {'key': 'scoringScript', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword code_id: ARM resource ID of the code asset.
+        :paramtype code_id: str
+        :keyword scoring_script: Required. [Required] The script to execute on startup. eg. "score.py".
+        :paramtype scoring_script: str
+        """
+        super(CodeConfiguration, self).__init__(**kwargs)
+        self.code_id = kwargs.get('code_id', None)
+        self.scoring_script = kwargs['scoring_script']
+
+
+class CodeContainer(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.CodeContainerProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'CodeContainerProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.CodeContainerProperties
+        """
+        super(CodeContainer, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class CodeContainerProperties(AssetContainer):
+    """Container for code asset versions.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(CodeContainerProperties, self).__init__(**kwargs)
+
+
+class CodeContainerResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of CodeContainer entities.
+
+    :ivar next_link: The link to the next page of CodeContainer objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type CodeContainer.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.CodeContainer]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[CodeContainer]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of CodeContainer objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type CodeContainer.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.CodeContainer]
+        """
+        super(CodeContainerResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class CodeVersion(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.CodeVersionProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'CodeVersionProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.CodeVersionProperties
+        """
+        super(CodeVersion, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class CodeVersionProperties(AssetBase):
+    """Code asset version details.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar code_uri: Uri where code is located.
+    :vartype code_uri: str
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'code_uri': {'key': 'codeUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword code_uri: Uri where code is located.
+        :paramtype code_uri: str
+        """
+        super(CodeVersionProperties, self).__init__(**kwargs)
+        self.code_uri = kwargs.get('code_uri', None)
+
+
+class CodeVersionResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of CodeVersion entities.
+
+    :ivar next_link: The link to the next page of CodeVersion objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type CodeVersion.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.CodeVersion]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[CodeVersion]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of CodeVersion objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type CodeVersion.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.CodeVersion]
+        """
+        super(CodeVersionResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class ColumnTransformer(msrest.serialization.Model):
+    """Column transformer parameters.
+
+    :ivar fields: Fields to apply transformer logic on.
+    :vartype fields: list[str]
+    :ivar parameters: Different properties to be passed to transformer.
+     Input expected is dictionary of key,value pairs in JSON format.
+    :vartype parameters: any
+    """
+
+    _attribute_map = {
+        'fields': {'key': 'fields', 'type': '[str]'},
+        'parameters': {'key': 'parameters', 'type': 'object'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword fields: Fields to apply transformer logic on.
+        :paramtype fields: list[str]
+        :keyword parameters: Different properties to be passed to transformer.
+         Input expected is dictionary of key,value pairs in JSON format.
+        :paramtype parameters: any
+        """
+        super(ColumnTransformer, self).__init__(**kwargs)
+        self.fields = kwargs.get('fields', None)
+        self.parameters = kwargs.get('parameters', None)
+
+
+class CommandJob(JobBaseProperties):
+    """Command job definition.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "AutoML", "Command", "Sweep", "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Possible values include: "NotStarted", "Starting",
+     "Provisioning", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
+     "Failed", "Canceled", "NotResponding", "Paused", "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar code_id: ARM resource ID of the code asset.
+    :vartype code_id: str
+    :ivar command: Required. [Required] The command to execute on startup of the job. eg. "python
+     train.py".
+    :vartype command: str
+    :ivar distribution: Distribution configuration of the job. If set, this should be one of Mpi,
+     Tensorflow, PyTorch, or null.
+    :vartype distribution: ~azure.mgmt.machinelearningservices.models.DistributionConfiguration
+    :ivar environment_id: Required. [Required] The ARM resource ID of the Environment specification
+     for the job.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables included in the job.
+    :vartype environment_variables: dict[str, str]
+    :ivar inputs: Mapping of input data bindings used in the job.
+    :vartype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+    :ivar limits: Command Job limit.
+    :vartype limits: ~azure.mgmt.machinelearningservices.models.CommandJobLimits
+    :ivar outputs: Mapping of output data bindings used in the job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar parameters: Input parameters.
+    :vartype parameters: any
+    :ivar resources: Compute Resource configuration for the job.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+    """
+
+    _validation = {
+        'job_type': {'required': True},
+        'status': {'readonly': True},
+        'command': {'required': True, 'min_length': 1, 'pattern': r'[a-zA-Z0-9_]'},
+        'environment_id': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'parameters': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'component_id': {'key': 'componentId', 'type': 'str'},
+        'compute_id': {'key': 'computeId', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'experiment_name': {'key': 'experimentName', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'job_type': {'key': 'jobType', 'type': 'str'},
+        'services': {'key': 'services', 'type': '{JobService}'},
+        'status': {'key': 'status', 'type': 'str'},
+        'code_id': {'key': 'codeId', 'type': 'str'},
+        'command': {'key': 'command', 'type': 'str'},
+        'distribution': {'key': 'distribution', 'type': 'DistributionConfiguration'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'inputs': {'key': 'inputs', 'type': '{JobInput}'},
+        'limits': {'key': 'limits', 'type': 'CommandJobLimits'},
+        'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
+        'parameters': {'key': 'parameters', 'type': 'object'},
+        'resources': {'key': 'resources', 'type': 'JobResourceConfiguration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword code_id: ARM resource ID of the code asset.
+        :paramtype code_id: str
+        :keyword command: Required. [Required] The command to execute on startup of the job. eg.
+         "python train.py".
+        :paramtype command: str
+        :keyword distribution: Distribution configuration of the job. If set, this should be one of
+         Mpi, Tensorflow, PyTorch, or null.
+        :paramtype distribution: ~azure.mgmt.machinelearningservices.models.DistributionConfiguration
+        :keyword environment_id: Required. [Required] The ARM resource ID of the Environment
+         specification for the job.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables included in the job.
+        :paramtype environment_variables: dict[str, str]
+        :keyword inputs: Mapping of input data bindings used in the job.
+        :paramtype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+        :keyword limits: Command Job limit.
+        :paramtype limits: ~azure.mgmt.machinelearningservices.models.CommandJobLimits
+        :keyword outputs: Mapping of output data bindings used in the job.
+        :paramtype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+        :keyword resources: Compute Resource configuration for the job.
+        :paramtype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+        """
+        super(CommandJob, self).__init__(**kwargs)
+        self.job_type = 'Command'  # type: str
+        self.code_id = kwargs.get('code_id', None)
+        self.command = kwargs['command']
+        self.distribution = kwargs.get('distribution', None)
+        self.environment_id = kwargs['environment_id']
+        self.environment_variables = kwargs.get('environment_variables', None)
+        self.inputs = kwargs.get('inputs', None)
+        self.limits = kwargs.get('limits', None)
+        self.outputs = kwargs.get('outputs', None)
+        self.parameters = None
+        self.resources = kwargs.get('resources', None)
+
+
+class JobLimits(msrest.serialization.Model):
+    """JobLimits.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: CommandJobLimits, SweepJobLimits.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar job_limits_type: Required. [Required] JobLimit type.Constant filled by server. Possible
+     values include: "Command", "Sweep".
+    :vartype job_limits_type: str or ~azure.mgmt.machinelearningservices.models.JobLimitsType
+    :ivar timeout: The max run duration in ISO 8601 format, after which the job will be cancelled.
+     Only supports duration with precision as low as Seconds.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _validation = {
+        'job_limits_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'job_limits_type': {'key': 'jobLimitsType', 'type': 'str'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    _subtype_map = {
+        'job_limits_type': {'Command': 'CommandJobLimits', 'Sweep': 'SweepJobLimits'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
+         cancelled. Only supports duration with precision as low as Seconds.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(JobLimits, self).__init__(**kwargs)
+        self.job_limits_type = None  # type: Optional[str]
+        self.timeout = kwargs.get('timeout', None)
+
+
+class CommandJobLimits(JobLimits):
+    """Command Job limit class.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar job_limits_type: Required. [Required] JobLimit type.Constant filled by server. Possible
+     values include: "Command", "Sweep".
+    :vartype job_limits_type: str or ~azure.mgmt.machinelearningservices.models.JobLimitsType
+    :ivar timeout: The max run duration in ISO 8601 format, after which the job will be cancelled.
+     Only supports duration with precision as low as Seconds.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _validation = {
+        'job_limits_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'job_limits_type': {'key': 'jobLimitsType', 'type': 'str'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
+         cancelled. Only supports duration with precision as low as Seconds.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(CommandJobLimits, self).__init__(**kwargs)
+        self.job_limits_type = 'Command'  # type: str
+
+
+class ComponentContainer(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ComponentContainerProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'ComponentContainerProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComponentContainerProperties
+        """
+        super(ComponentContainer, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class ComponentContainerProperties(AssetContainer):
+    """Component container definition.
+
+
+.. raw:: html
+
+   <see href="https://learn.microsoft.com/azure/machine-learning/reference-yaml-component-command" />.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(ComponentContainerProperties, self).__init__(**kwargs)
+
+
+class ComponentContainerResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of ComponentContainer entities.
+
+    :ivar next_link: The link to the next page of ComponentContainer objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type ComponentContainer.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[ComponentContainer]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of ComponentContainer objects. If null, there are
+         no additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type ComponentContainer.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+        """
+        super(ComponentContainerResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class ComponentVersion(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ComponentVersionProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'ComponentVersionProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComponentVersionProperties
+        """
+        super(ComponentVersion, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class ComponentVersionProperties(AssetBase):
+    """Definition of a component version: defines resources that span component types.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar component_spec: Defines Component definition details.
+    
+    
+     .. raw:: html
+    
+        <see
+     href="https://learn.microsoft.com/azure/machine-learning/reference-yaml-component-command"
+     />.
+    :vartype component_spec: any
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'component_spec': {'key': 'componentSpec', 'type': 'object'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword component_spec: Defines Component definition details.
+        
+        
+         .. raw:: html
+        
+            <see
+         href="https://learn.microsoft.com/azure/machine-learning/reference-yaml-component-command"
+         />.
+        :paramtype component_spec: any
+        """
+        super(ComponentVersionProperties, self).__init__(**kwargs)
+        self.component_spec = kwargs.get('component_spec', None)
+
+
+class ComponentVersionResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of ComponentVersion entities.
+
+    :ivar next_link: The link to the next page of ComponentVersion objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type ComponentVersion.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[ComponentVersion]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of ComponentVersion objects. If null, there are
+         no additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type ComponentVersion.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+        """
+        super(ComponentVersionResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class ComputeInstanceSchema(msrest.serialization.Model):
+    """Properties(top level) of ComputeInstance.
+
+    :ivar properties: Properties of ComputeInstance.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'ComputeInstanceProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of ComputeInstance.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
+        """
+        super(ComputeInstanceSchema, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class ComputeInstance(Compute, ComputeInstanceSchema):
+    """An Azure Machine Learning compute instance.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: Properties of ComputeInstance.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'ComputeInstanceProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of ComputeInstance.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(ComputeInstance, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+        self.compute_type = 'ComputeInstance'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = kwargs.get('description', None)
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = kwargs.get('resource_id', None)
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class ComputeInstanceApplication(msrest.serialization.Model):
+    """Defines an Aml Instance application and its connectivity endpoint URI.
+
+    :ivar display_name: Name of the ComputeInstance application.
+    :vartype display_name: str
+    :ivar endpoint_uri: Application' endpoint URI.
+    :vartype endpoint_uri: str
+    """
+
+    _attribute_map = {
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'endpoint_uri': {'key': 'endpointUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword display_name: Name of the ComputeInstance application.
+        :paramtype display_name: str
+        :keyword endpoint_uri: Application' endpoint URI.
+        :paramtype endpoint_uri: str
+        """
+        super(ComputeInstanceApplication, self).__init__(**kwargs)
+        self.display_name = kwargs.get('display_name', None)
+        self.endpoint_uri = kwargs.get('endpoint_uri', None)
+
+
+class ComputeInstanceConnectivityEndpoints(msrest.serialization.Model):
+    """Defines all connectivity endpoints and properties for an ComputeInstance.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar public_ip_address: Public IP Address of this ComputeInstance.
+    :vartype public_ip_address: str
+    :ivar private_ip_address: Private IP Address of this ComputeInstance (local to the VNET in
+     which the compute instance is deployed).
+    :vartype private_ip_address: str
+    """
+
+    _validation = {
+        'public_ip_address': {'readonly': True},
+        'private_ip_address': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'public_ip_address': {'key': 'publicIpAddress', 'type': 'str'},
+        'private_ip_address': {'key': 'privateIpAddress', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ComputeInstanceConnectivityEndpoints, self).__init__(**kwargs)
+        self.public_ip_address = None
+        self.private_ip_address = None
+
+
+class ComputeInstanceContainer(msrest.serialization.Model):
+    """Defines an Aml Instance container.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar name: Name of the ComputeInstance container.
+    :vartype name: str
+    :ivar autosave: Auto save settings. Possible values include: "None", "Local", "Remote".
+    :vartype autosave: str or ~azure.mgmt.machinelearningservices.models.Autosave
+    :ivar gpu: Information of GPU.
+    :vartype gpu: str
+    :ivar network: network of this container. Possible values include: "Bridge", "Host".
+    :vartype network: str or ~azure.mgmt.machinelearningservices.models.Network
+    :ivar environment: Environment information of this container.
+    :vartype environment: ~azure.mgmt.machinelearningservices.models.ComputeInstanceEnvironmentInfo
+    :ivar services: services of this containers.
+    :vartype services: list[any]
+    """
+
+    _validation = {
+        'services': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'autosave': {'key': 'autosave', 'type': 'str'},
+        'gpu': {'key': 'gpu', 'type': 'str'},
+        'network': {'key': 'network', 'type': 'str'},
+        'environment': {'key': 'environment', 'type': 'ComputeInstanceEnvironmentInfo'},
+        'services': {'key': 'services', 'type': '[object]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword name: Name of the ComputeInstance container.
+        :paramtype name: str
+        :keyword autosave: Auto save settings. Possible values include: "None", "Local", "Remote".
+        :paramtype autosave: str or ~azure.mgmt.machinelearningservices.models.Autosave
+        :keyword gpu: Information of GPU.
+        :paramtype gpu: str
+        :keyword network: network of this container. Possible values include: "Bridge", "Host".
+        :paramtype network: str or ~azure.mgmt.machinelearningservices.models.Network
+        :keyword environment: Environment information of this container.
+        :paramtype environment:
+         ~azure.mgmt.machinelearningservices.models.ComputeInstanceEnvironmentInfo
+        """
+        super(ComputeInstanceContainer, self).__init__(**kwargs)
+        self.name = kwargs.get('name', None)
+        self.autosave = kwargs.get('autosave', None)
+        self.gpu = kwargs.get('gpu', None)
+        self.network = kwargs.get('network', None)
+        self.environment = kwargs.get('environment', None)
+        self.services = None
+
+
+class ComputeInstanceCreatedBy(msrest.serialization.Model):
+    """Describes information on user who created this ComputeInstance.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar user_name: Name of the user.
+    :vartype user_name: str
+    :ivar user_org_id: Uniquely identifies user' Azure Active Directory organization.
+    :vartype user_org_id: str
+    :ivar user_id: Uniquely identifies the user within his/her organization.
+    :vartype user_id: str
+    """
+
+    _validation = {
+        'user_name': {'readonly': True},
+        'user_org_id': {'readonly': True},
+        'user_id': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'user_name': {'key': 'userName', 'type': 'str'},
+        'user_org_id': {'key': 'userOrgId', 'type': 'str'},
+        'user_id': {'key': 'userId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ComputeInstanceCreatedBy, self).__init__(**kwargs)
+        self.user_name = None
+        self.user_org_id = None
+        self.user_id = None
+
+
+class ComputeInstanceDataDisk(msrest.serialization.Model):
+    """Defines an Aml Instance DataDisk.
+
+    :ivar caching: Caching type of Data Disk. Possible values include: "None", "ReadOnly",
+     "ReadWrite".
+    :vartype caching: str or ~azure.mgmt.machinelearningservices.models.Caching
+    :ivar disk_size_gb: The initial disk size in gigabytes.
+    :vartype disk_size_gb: int
+    :ivar lun: The lun is used to uniquely identify each data disk. If attaching multiple disks,
+     each should have a distinct lun.
+    :vartype lun: int
+    :ivar storage_account_type: type of this storage account. Possible values include:
+     "Standard_LRS", "Premium_LRS". Default value: "Standard_LRS".
+    :vartype storage_account_type: str or
+     ~azure.mgmt.machinelearningservices.models.StorageAccountType
+    """
+
+    _attribute_map = {
+        'caching': {'key': 'caching', 'type': 'str'},
+        'disk_size_gb': {'key': 'diskSizeGB', 'type': 'int'},
+        'lun': {'key': 'lun', 'type': 'int'},
+        'storage_account_type': {'key': 'storageAccountType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword caching: Caching type of Data Disk. Possible values include: "None", "ReadOnly",
+         "ReadWrite".
+        :paramtype caching: str or ~azure.mgmt.machinelearningservices.models.Caching
+        :keyword disk_size_gb: The initial disk size in gigabytes.
+        :paramtype disk_size_gb: int
+        :keyword lun: The lun is used to uniquely identify each data disk. If attaching multiple disks,
+         each should have a distinct lun.
+        :paramtype lun: int
+        :keyword storage_account_type: type of this storage account. Possible values include:
+         "Standard_LRS", "Premium_LRS". Default value: "Standard_LRS".
+        :paramtype storage_account_type: str or
+         ~azure.mgmt.machinelearningservices.models.StorageAccountType
+        """
+        super(ComputeInstanceDataDisk, self).__init__(**kwargs)
+        self.caching = kwargs.get('caching', None)
+        self.disk_size_gb = kwargs.get('disk_size_gb', None)
+        self.lun = kwargs.get('lun', None)
+        self.storage_account_type = kwargs.get('storage_account_type', "Standard_LRS")
+
+
+class ComputeInstanceDataMount(msrest.serialization.Model):
+    """Defines an Aml Instance DataMount.
+
+    :ivar source: Source of the ComputeInstance data mount.
+    :vartype source: str
+    :ivar source_type: Data source type. Possible values include: "Dataset", "Datastore", "URI".
+    :vartype source_type: str or ~azure.mgmt.machinelearningservices.models.SourceType
+    :ivar mount_name: name of the ComputeInstance data mount.
+    :vartype mount_name: str
+    :ivar mount_action: Mount Action. Possible values include: "Mount", "Unmount".
+    :vartype mount_action: str or ~azure.mgmt.machinelearningservices.models.MountAction
+    :ivar created_by: who this data mount created by.
+    :vartype created_by: str
+    :ivar mount_path: Path of this data mount.
+    :vartype mount_path: str
+    :ivar mount_state: Mount state. Possible values include: "MountRequested", "Mounted",
+     "MountFailed", "UnmountRequested", "UnmountFailed", "Unmounted".
+    :vartype mount_state: str or ~azure.mgmt.machinelearningservices.models.MountState
+    :ivar mounted_on: The time when the disk mounted.
+    :vartype mounted_on: ~datetime.datetime
+    :ivar error: Error of this data mount.
+    :vartype error: str
+    """
+
+    _attribute_map = {
+        'source': {'key': 'source', 'type': 'str'},
+        'source_type': {'key': 'sourceType', 'type': 'str'},
+        'mount_name': {'key': 'mountName', 'type': 'str'},
+        'mount_action': {'key': 'mountAction', 'type': 'str'},
+        'created_by': {'key': 'createdBy', 'type': 'str'},
+        'mount_path': {'key': 'mountPath', 'type': 'str'},
+        'mount_state': {'key': 'mountState', 'type': 'str'},
+        'mounted_on': {'key': 'mountedOn', 'type': 'iso-8601'},
+        'error': {'key': 'error', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword source: Source of the ComputeInstance data mount.
+        :paramtype source: str
+        :keyword source_type: Data source type. Possible values include: "Dataset", "Datastore", "URI".
+        :paramtype source_type: str or ~azure.mgmt.machinelearningservices.models.SourceType
+        :keyword mount_name: name of the ComputeInstance data mount.
+        :paramtype mount_name: str
+        :keyword mount_action: Mount Action. Possible values include: "Mount", "Unmount".
+        :paramtype mount_action: str or ~azure.mgmt.machinelearningservices.models.MountAction
+        :keyword created_by: who this data mount created by.
+        :paramtype created_by: str
+        :keyword mount_path: Path of this data mount.
+        :paramtype mount_path: str
+        :keyword mount_state: Mount state. Possible values include: "MountRequested", "Mounted",
+         "MountFailed", "UnmountRequested", "UnmountFailed", "Unmounted".
+        :paramtype mount_state: str or ~azure.mgmt.machinelearningservices.models.MountState
+        :keyword mounted_on: The time when the disk mounted.
+        :paramtype mounted_on: ~datetime.datetime
+        :keyword error: Error of this data mount.
+        :paramtype error: str
+        """
+        super(ComputeInstanceDataMount, self).__init__(**kwargs)
+        self.source = kwargs.get('source', None)
+        self.source_type = kwargs.get('source_type', None)
+        self.mount_name = kwargs.get('mount_name', None)
+        self.mount_action = kwargs.get('mount_action', None)
+        self.created_by = kwargs.get('created_by', None)
+        self.mount_path = kwargs.get('mount_path', None)
+        self.mount_state = kwargs.get('mount_state', None)
+        self.mounted_on = kwargs.get('mounted_on', None)
+        self.error = kwargs.get('error', None)
+
+
+class ComputeInstanceEnvironmentInfo(msrest.serialization.Model):
+    """Environment information.
+
+    :ivar name: name of environment.
+    :vartype name: str
+    :ivar version: version of environment.
+    :vartype version: str
+    """
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'version': {'key': 'version', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword name: name of environment.
+        :paramtype name: str
+        :keyword version: version of environment.
+        :paramtype version: str
+        """
+        super(ComputeInstanceEnvironmentInfo, self).__init__(**kwargs)
+        self.name = kwargs.get('name', None)
+        self.version = kwargs.get('version', None)
+
+
+class ComputeInstanceLastOperation(msrest.serialization.Model):
+    """The last operation on ComputeInstance.
+
+    :ivar operation_name: Name of the last operation. Possible values include: "Create", "Start",
+     "Stop", "Restart", "Reimage", "Delete".
+    :vartype operation_name: str or ~azure.mgmt.machinelearningservices.models.OperationName
+    :ivar operation_time: Time of the last operation.
+    :vartype operation_time: ~datetime.datetime
+    :ivar operation_status: Operation status. Possible values include: "InProgress", "Succeeded",
+     "CreateFailed", "StartFailed", "StopFailed", "RestartFailed", "ReimageFailed", "DeleteFailed".
+    :vartype operation_status: str or ~azure.mgmt.machinelearningservices.models.OperationStatus
+    :ivar operation_trigger: Trigger of operation. Possible values include: "User", "Schedule",
+     "IdleShutdown".
+    :vartype operation_trigger: str or ~azure.mgmt.machinelearningservices.models.OperationTrigger
+    """
+
+    _attribute_map = {
+        'operation_name': {'key': 'operationName', 'type': 'str'},
+        'operation_time': {'key': 'operationTime', 'type': 'iso-8601'},
+        'operation_status': {'key': 'operationStatus', 'type': 'str'},
+        'operation_trigger': {'key': 'operationTrigger', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword operation_name: Name of the last operation. Possible values include: "Create",
+         "Start", "Stop", "Restart", "Reimage", "Delete".
+        :paramtype operation_name: str or ~azure.mgmt.machinelearningservices.models.OperationName
+        :keyword operation_time: Time of the last operation.
+        :paramtype operation_time: ~datetime.datetime
+        :keyword operation_status: Operation status. Possible values include: "InProgress",
+         "Succeeded", "CreateFailed", "StartFailed", "StopFailed", "RestartFailed", "ReimageFailed",
+         "DeleteFailed".
+        :paramtype operation_status: str or ~azure.mgmt.machinelearningservices.models.OperationStatus
+        :keyword operation_trigger: Trigger of operation. Possible values include: "User", "Schedule",
+         "IdleShutdown".
+        :paramtype operation_trigger: str or
+         ~azure.mgmt.machinelearningservices.models.OperationTrigger
+        """
+        super(ComputeInstanceLastOperation, self).__init__(**kwargs)
+        self.operation_name = kwargs.get('operation_name', None)
+        self.operation_time = kwargs.get('operation_time', None)
+        self.operation_status = kwargs.get('operation_status', None)
+        self.operation_trigger = kwargs.get('operation_trigger', None)
+
+
+class ComputeInstanceProperties(msrest.serialization.Model):
+    """Compute Instance properties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar vm_size: Virtual Machine Size.
+    :vartype vm_size: str
+    :ivar subnet: Virtual network subnet resource ID the compute nodes belong to.
+    :vartype subnet: ~azure.mgmt.machinelearningservices.models.ResourceId
+    :ivar application_sharing_policy: Policy for sharing applications on this compute instance
+     among users of parent workspace. If Personal, only the creator can access applications on this
+     compute instance. When Shared, any workspace user can access applications on this instance
+     depending on his/her assigned role. Possible values include: "Personal", "Shared". Default
+     value: "Shared".
+    :vartype application_sharing_policy: str or
+     ~azure.mgmt.machinelearningservices.models.ApplicationSharingPolicy
+    :ivar ssh_settings: Specifies policy and settings for SSH access.
+    :vartype ssh_settings: ~azure.mgmt.machinelearningservices.models.ComputeInstanceSshSettings
+    :ivar connectivity_endpoints: Describes all connectivity endpoints available for this
+     ComputeInstance.
+    :vartype connectivity_endpoints:
+     ~azure.mgmt.machinelearningservices.models.ComputeInstanceConnectivityEndpoints
+    :ivar applications: Describes available applications and their endpoints on this
+     ComputeInstance.
+    :vartype applications:
+     list[~azure.mgmt.machinelearningservices.models.ComputeInstanceApplication]
+    :ivar created_by: Describes information on user who created this ComputeInstance.
+    :vartype created_by: ~azure.mgmt.machinelearningservices.models.ComputeInstanceCreatedBy
+    :ivar errors: Collection of errors encountered on this ComputeInstance.
+    :vartype errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar state: The current state of this ComputeInstance. Possible values include: "Creating",
+     "CreateFailed", "Deleting", "Running", "Restarting", "JobRunning", "SettingUp", "SetupFailed",
+     "Starting", "Stopped", "Stopping", "UserSettingUp", "UserSetupFailed", "Unknown", "Unusable".
+    :vartype state: str or ~azure.mgmt.machinelearningservices.models.ComputeInstanceState
+    :ivar compute_instance_authorization_type: The Compute Instance Authorization type. Available
+     values are personal (default). Possible values include: "personal". Default value: "personal".
+    :vartype compute_instance_authorization_type: str or
+     ~azure.mgmt.machinelearningservices.models.ComputeInstanceAuthorizationType
+    :ivar personal_compute_instance_settings: Settings for a personal compute instance.
+    :vartype personal_compute_instance_settings:
+     ~azure.mgmt.machinelearningservices.models.PersonalComputeInstanceSettings
+    :ivar setup_scripts: Details of customized scripts to execute for setting up the cluster.
+    :vartype setup_scripts: ~azure.mgmt.machinelearningservices.models.SetupScripts
+    :ivar last_operation: The last operation on ComputeInstance.
+    :vartype last_operation:
+     ~azure.mgmt.machinelearningservices.models.ComputeInstanceLastOperation
+    :ivar schedules: The list of schedules to be applied on the computes.
+    :vartype schedules: ~azure.mgmt.machinelearningservices.models.ComputeSchedules
+    :ivar enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
+     values are: Possible values are: true - Indicates that the compute nodes will have public IPs
+     provisioned. false - Indicates that the compute nodes will have a private endpoint and no
+     public IPs.
+    :vartype enable_node_public_ip: bool
+    :ivar containers: Describes informations of containers on this ComputeInstance.
+    :vartype containers: list[~azure.mgmt.machinelearningservices.models.ComputeInstanceContainer]
+    :ivar data_disks: Describes informations of dataDisks on this ComputeInstance.
+    :vartype data_disks: list[~azure.mgmt.machinelearningservices.models.ComputeInstanceDataDisk]
+    :ivar data_mounts: Describes informations of dataMounts on this ComputeInstance.
+    :vartype data_mounts: list[~azure.mgmt.machinelearningservices.models.ComputeInstanceDataMount]
+    :ivar versions: ComputeInstance version.
+    :vartype versions: ~azure.mgmt.machinelearningservices.models.ComputeInstanceVersion
+    """
+
+    _validation = {
+        'connectivity_endpoints': {'readonly': True},
+        'applications': {'readonly': True},
+        'created_by': {'readonly': True},
+        'errors': {'readonly': True},
+        'state': {'readonly': True},
+        'last_operation': {'readonly': True},
+        'schedules': {'readonly': True},
+        'containers': {'readonly': True},
+        'data_disks': {'readonly': True},
+        'data_mounts': {'readonly': True},
+        'versions': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'vm_size': {'key': 'vmSize', 'type': 'str'},
+        'subnet': {'key': 'subnet', 'type': 'ResourceId'},
+        'application_sharing_policy': {'key': 'applicationSharingPolicy', 'type': 'str'},
+        'ssh_settings': {'key': 'sshSettings', 'type': 'ComputeInstanceSshSettings'},
+        'connectivity_endpoints': {'key': 'connectivityEndpoints', 'type': 'ComputeInstanceConnectivityEndpoints'},
+        'applications': {'key': 'applications', 'type': '[ComputeInstanceApplication]'},
+        'created_by': {'key': 'createdBy', 'type': 'ComputeInstanceCreatedBy'},
+        'errors': {'key': 'errors', 'type': '[ErrorResponse]'},
+        'state': {'key': 'state', 'type': 'str'},
+        'compute_instance_authorization_type': {'key': 'computeInstanceAuthorizationType', 'type': 'str'},
+        'personal_compute_instance_settings': {'key': 'personalComputeInstanceSettings', 'type': 'PersonalComputeInstanceSettings'},
+        'setup_scripts': {'key': 'setupScripts', 'type': 'SetupScripts'},
+        'last_operation': {'key': 'lastOperation', 'type': 'ComputeInstanceLastOperation'},
+        'schedules': {'key': 'schedules', 'type': 'ComputeSchedules'},
+        'enable_node_public_ip': {'key': 'enableNodePublicIp', 'type': 'bool'},
+        'containers': {'key': 'containers', 'type': '[ComputeInstanceContainer]'},
+        'data_disks': {'key': 'dataDisks', 'type': '[ComputeInstanceDataDisk]'},
+        'data_mounts': {'key': 'dataMounts', 'type': '[ComputeInstanceDataMount]'},
+        'versions': {'key': 'versions', 'type': 'ComputeInstanceVersion'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword vm_size: Virtual Machine Size.
+        :paramtype vm_size: str
+        :keyword subnet: Virtual network subnet resource ID the compute nodes belong to.
+        :paramtype subnet: ~azure.mgmt.machinelearningservices.models.ResourceId
+        :keyword application_sharing_policy: Policy for sharing applications on this compute instance
+         among users of parent workspace. If Personal, only the creator can access applications on this
+         compute instance. When Shared, any workspace user can access applications on this instance
+         depending on his/her assigned role. Possible values include: "Personal", "Shared". Default
+         value: "Shared".
+        :paramtype application_sharing_policy: str or
+         ~azure.mgmt.machinelearningservices.models.ApplicationSharingPolicy
+        :keyword ssh_settings: Specifies policy and settings for SSH access.
+        :paramtype ssh_settings: ~azure.mgmt.machinelearningservices.models.ComputeInstanceSshSettings
+        :keyword compute_instance_authorization_type: The Compute Instance Authorization type.
+         Available values are personal (default). Possible values include: "personal". Default value:
+         "personal".
+        :paramtype compute_instance_authorization_type: str or
+         ~azure.mgmt.machinelearningservices.models.ComputeInstanceAuthorizationType
+        :keyword personal_compute_instance_settings: Settings for a personal compute instance.
+        :paramtype personal_compute_instance_settings:
+         ~azure.mgmt.machinelearningservices.models.PersonalComputeInstanceSettings
+        :keyword setup_scripts: Details of customized scripts to execute for setting up the cluster.
+        :paramtype setup_scripts: ~azure.mgmt.machinelearningservices.models.SetupScripts
+        :keyword enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
+         values are: Possible values are: true - Indicates that the compute nodes will have public IPs
+         provisioned. false - Indicates that the compute nodes will have a private endpoint and no
+         public IPs.
+        :paramtype enable_node_public_ip: bool
+        """
+        super(ComputeInstanceProperties, self).__init__(**kwargs)
+        self.vm_size = kwargs.get('vm_size', None)
+        self.subnet = kwargs.get('subnet', None)
+        self.application_sharing_policy = kwargs.get('application_sharing_policy', "Shared")
+        self.ssh_settings = kwargs.get('ssh_settings', None)
+        self.connectivity_endpoints = None
+        self.applications = None
+        self.created_by = None
+        self.errors = None
+        self.state = None
+        self.compute_instance_authorization_type = kwargs.get('compute_instance_authorization_type', "personal")
+        self.personal_compute_instance_settings = kwargs.get('personal_compute_instance_settings', None)
+        self.setup_scripts = kwargs.get('setup_scripts', None)
+        self.last_operation = None
+        self.schedules = None
+        self.enable_node_public_ip = kwargs.get('enable_node_public_ip', None)
+        self.containers = None
+        self.data_disks = None
+        self.data_mounts = None
+        self.versions = None
+
+
+class ComputeInstanceSshSettings(msrest.serialization.Model):
+    """Specifies policy and settings for SSH access.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar ssh_public_access: State of the public SSH port. Possible values are: Disabled -
+     Indicates that the public ssh port is closed on this instance. Enabled - Indicates that the
+     public ssh port is open and accessible according to the VNet/subnet policy if applicable.
+     Possible values include: "Enabled", "Disabled". Default value: "Disabled".
+    :vartype ssh_public_access: str or ~azure.mgmt.machinelearningservices.models.SshPublicAccess
+    :ivar admin_user_name: Describes the admin user name.
+    :vartype admin_user_name: str
+    :ivar ssh_port: Describes the port for connecting through SSH.
+    :vartype ssh_port: int
+    :ivar admin_public_key: Specifies the SSH rsa public key file as a string. Use "ssh-keygen -t
+     rsa -b 2048" to generate your SSH key pairs.
+    :vartype admin_public_key: str
+    """
+
+    _validation = {
+        'admin_user_name': {'readonly': True},
+        'ssh_port': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'ssh_public_access': {'key': 'sshPublicAccess', 'type': 'str'},
+        'admin_user_name': {'key': 'adminUserName', 'type': 'str'},
+        'ssh_port': {'key': 'sshPort', 'type': 'int'},
+        'admin_public_key': {'key': 'adminPublicKey', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword ssh_public_access: State of the public SSH port. Possible values are: Disabled -
+         Indicates that the public ssh port is closed on this instance. Enabled - Indicates that the
+         public ssh port is open and accessible according to the VNet/subnet policy if applicable.
+         Possible values include: "Enabled", "Disabled". Default value: "Disabled".
+        :paramtype ssh_public_access: str or ~azure.mgmt.machinelearningservices.models.SshPublicAccess
+        :keyword admin_public_key: Specifies the SSH rsa public key file as a string. Use "ssh-keygen
+         -t rsa -b 2048" to generate your SSH key pairs.
+        :paramtype admin_public_key: str
+        """
+        super(ComputeInstanceSshSettings, self).__init__(**kwargs)
+        self.ssh_public_access = kwargs.get('ssh_public_access', "Disabled")
+        self.admin_user_name = None
+        self.ssh_port = None
+        self.admin_public_key = kwargs.get('admin_public_key', None)
+
+
+class ComputeInstanceVersion(msrest.serialization.Model):
+    """Version of computeInstance.
+
+    :ivar runtime: Runtime of compute instance.
+    :vartype runtime: str
+    """
+
+    _attribute_map = {
+        'runtime': {'key': 'runtime', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword runtime: Runtime of compute instance.
+        :paramtype runtime: str
+        """
+        super(ComputeInstanceVersion, self).__init__(**kwargs)
+        self.runtime = kwargs.get('runtime', None)
+
+
+class ComputeResourceSchema(msrest.serialization.Model):
+    """ComputeResourceSchema.
+
+    :ivar properties: Compute properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.Compute
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'Compute'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Compute properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.Compute
+        """
+        super(ComputeResourceSchema, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class ComputeResource(Resource, ComputeResourceSchema):
+    """Machine Learning compute object wrapped into ARM resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar properties: Compute properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.Compute
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar identity: The identity of the resource.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar location: Specifies the location of the resource.
+    :vartype location: str
+    :ivar tags: A set of tags. Contains resource tags defined as key/value pairs.
+    :vartype tags: dict[str, str]
+    :ivar sku: The sku of the workspace.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'Compute'},
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'location': {'key': 'location', 'type': 'str'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Compute properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.Compute
+        :keyword identity: The identity of the resource.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword location: Specifies the location of the resource.
+        :paramtype location: str
+        :keyword tags: A set of tags. Contains resource tags defined as key/value pairs.
+        :paramtype tags: dict[str, str]
+        :keyword sku: The sku of the workspace.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super(ComputeResource, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+        self.identity = kwargs.get('identity', None)
+        self.location = kwargs.get('location', None)
+        self.tags = kwargs.get('tags', None)
+        self.sku = kwargs.get('sku', None)
+        self.id = None
+        self.name = None
+        self.type = None
+        self.system_data = None
+
+
+class ComputeSchedules(msrest.serialization.Model):
+    """The list of schedules to be applied on the computes.
+
+    :ivar compute_start_stop: The list of compute start stop schedules to be applied.
+    :vartype compute_start_stop:
+     list[~azure.mgmt.machinelearningservices.models.ComputeStartStopSchedule]
+    """
+
+    _attribute_map = {
+        'compute_start_stop': {'key': 'computeStartStop', 'type': '[ComputeStartStopSchedule]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword compute_start_stop: The list of compute start stop schedules to be applied.
+        :paramtype compute_start_stop:
+         list[~azure.mgmt.machinelearningservices.models.ComputeStartStopSchedule]
+        """
+        super(ComputeSchedules, self).__init__(**kwargs)
+        self.compute_start_stop = kwargs.get('compute_start_stop', None)
+
+
+class ComputeStartStopSchedule(msrest.serialization.Model):
+    """Compute start stop schedule properties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: A system assigned id for the schedule.
+    :vartype id: str
+    :ivar provisioning_status: The current deployment state of schedule. Possible values include:
+     "Completed", "Provisioning", "Failed".
+    :vartype provisioning_status: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningStatus
+    :ivar status: Is the schedule enabled or disabled?. Possible values include: "Enabled",
+     "Disabled".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.ScheduleStatus
+    :ivar action: [Required] The compute power action. Possible values include: "Start", "Stop".
+    :vartype action: str or ~azure.mgmt.machinelearningservices.models.ComputePowerAction
+    :ivar trigger_type: [Required] The schedule trigger type. Possible values include:
+     "Recurrence", "Cron".
+    :vartype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
+    :ivar recurrence: Required if triggerType is Recurrence.
+    :vartype recurrence: ~azure.mgmt.machinelearningservices.models.RecurrenceTrigger
+    :ivar cron: Required if triggerType is Cron.
+    :vartype cron: ~azure.mgmt.machinelearningservices.models.CronTrigger
+    :ivar schedule: [Deprecated] Not used any more.
+    :vartype schedule: ~azure.mgmt.machinelearningservices.models.ScheduleBase
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'provisioning_status': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'provisioning_status': {'key': 'provisioningStatus', 'type': 'str'},
+        'status': {'key': 'status', 'type': 'str'},
+        'action': {'key': 'action', 'type': 'str'},
+        'trigger_type': {'key': 'triggerType', 'type': 'str'},
+        'recurrence': {'key': 'recurrence', 'type': 'RecurrenceTrigger'},
+        'cron': {'key': 'cron', 'type': 'CronTrigger'},
+        'schedule': {'key': 'schedule', 'type': 'ScheduleBase'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword status: Is the schedule enabled or disabled?. Possible values include: "Enabled",
+         "Disabled".
+        :paramtype status: str or ~azure.mgmt.machinelearningservices.models.ScheduleStatus
+        :keyword action: [Required] The compute power action. Possible values include: "Start", "Stop".
+        :paramtype action: str or ~azure.mgmt.machinelearningservices.models.ComputePowerAction
+        :keyword trigger_type: [Required] The schedule trigger type. Possible values include:
+         "Recurrence", "Cron".
+        :paramtype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
+        :keyword recurrence: Required if triggerType is Recurrence.
+        :paramtype recurrence: ~azure.mgmt.machinelearningservices.models.RecurrenceTrigger
+        :keyword cron: Required if triggerType is Cron.
+        :paramtype cron: ~azure.mgmt.machinelearningservices.models.CronTrigger
+        :keyword schedule: [Deprecated] Not used any more.
+        :paramtype schedule: ~azure.mgmt.machinelearningservices.models.ScheduleBase
+        """
+        super(ComputeStartStopSchedule, self).__init__(**kwargs)
+        self.id = None
+        self.provisioning_status = None
+        self.status = kwargs.get('status', None)
+        self.action = kwargs.get('action', None)
+        self.trigger_type = kwargs.get('trigger_type', None)
+        self.recurrence = kwargs.get('recurrence', None)
+        self.cron = kwargs.get('cron', None)
+        self.schedule = kwargs.get('schedule', None)
+
+
+class ContainerResourceRequirements(msrest.serialization.Model):
+    """Resource requirements for each container instance within an online deployment.
+
+    :ivar container_resource_limits: Container resource limit info:.
+    :vartype container_resource_limits:
+     ~azure.mgmt.machinelearningservices.models.ContainerResourceSettings
+    :ivar container_resource_requests: Container resource request info:.
+    :vartype container_resource_requests:
+     ~azure.mgmt.machinelearningservices.models.ContainerResourceSettings
+    """
+
+    _attribute_map = {
+        'container_resource_limits': {'key': 'containerResourceLimits', 'type': 'ContainerResourceSettings'},
+        'container_resource_requests': {'key': 'containerResourceRequests', 'type': 'ContainerResourceSettings'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword container_resource_limits: Container resource limit info:.
+        :paramtype container_resource_limits:
+         ~azure.mgmt.machinelearningservices.models.ContainerResourceSettings
+        :keyword container_resource_requests: Container resource request info:.
+        :paramtype container_resource_requests:
+         ~azure.mgmt.machinelearningservices.models.ContainerResourceSettings
+        """
+        super(ContainerResourceRequirements, self).__init__(**kwargs)
+        self.container_resource_limits = kwargs.get('container_resource_limits', None)
+        self.container_resource_requests = kwargs.get('container_resource_requests', None)
+
+
+class ContainerResourceSettings(msrest.serialization.Model):
+    """ContainerResourceSettings.
+
+    :ivar cpu: Number of vCPUs request/limit for container. More info:
+     https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+    :vartype cpu: str
+    :ivar gpu: Number of Nvidia GPU cards request/limit for container. More info:
+     https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+    :vartype gpu: str
+    :ivar memory: Memory size request/limit for container. More info:
+     https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+    :vartype memory: str
+    """
+
+    _attribute_map = {
+        'cpu': {'key': 'cpu', 'type': 'str'},
+        'gpu': {'key': 'gpu', 'type': 'str'},
+        'memory': {'key': 'memory', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword cpu: Number of vCPUs request/limit for container. More info:
+         https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+        :paramtype cpu: str
+        :keyword gpu: Number of Nvidia GPU cards request/limit for container. More info:
+         https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+        :paramtype gpu: str
+        :keyword memory: Memory size request/limit for container. More info:
+         https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+        :paramtype memory: str
+        """
+        super(ContainerResourceSettings, self).__init__(**kwargs)
+        self.cpu = kwargs.get('cpu', None)
+        self.gpu = kwargs.get('gpu', None)
+        self.memory = kwargs.get('memory', None)
+
+
+class CosmosDbSettings(msrest.serialization.Model):
+    """CosmosDbSettings.
+
+    :ivar collections_throughput: The throughput of the collections in cosmosdb database.
+    :vartype collections_throughput: int
+    """
+
+    _attribute_map = {
+        'collections_throughput': {'key': 'collectionsThroughput', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword collections_throughput: The throughput of the collections in cosmosdb database.
+        :paramtype collections_throughput: int
+        """
+        super(CosmosDbSettings, self).__init__(**kwargs)
+        self.collections_throughput = kwargs.get('collections_throughput', None)
+
+
+class TriggerBase(msrest.serialization.Model):
+    """TriggerBase.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: CronTrigger, RecurrenceTrigger.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+     https://en.wikipedia.org/wiki/ISO_8601.
+     Recommented format would be "2022-06-01T00:00:01"
+     If not present, the schedule will run indefinitely.
+    :vartype end_time: str
+    :ivar start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+     offset.
+    :vartype start_time: str
+    :ivar time_zone: Specifies time zone in which the schedule runs.
+     TimeZone should follow Windows time zone format. Refer:
+     https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+    :vartype time_zone: str
+    :ivar trigger_type: Required. [Required].Constant filled by server. Possible values include:
+     "Recurrence", "Cron".
+    :vartype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
+    """
+
+    _validation = {
+        'trigger_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'end_time': {'key': 'endTime', 'type': 'str'},
+        'start_time': {'key': 'startTime', 'type': 'str'},
+        'time_zone': {'key': 'timeZone', 'type': 'str'},
+        'trigger_type': {'key': 'triggerType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'trigger_type': {'Cron': 'CronTrigger', 'Recurrence': 'RecurrenceTrigger'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+         https://en.wikipedia.org/wiki/ISO_8601.
+         Recommented format would be "2022-06-01T00:00:01"
+         If not present, the schedule will run indefinitely.
+        :paramtype end_time: str
+        :keyword start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+         offset.
+        :paramtype start_time: str
+        :keyword time_zone: Specifies time zone in which the schedule runs.
+         TimeZone should follow Windows time zone format. Refer:
+         https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+        :paramtype time_zone: str
+        """
+        super(TriggerBase, self).__init__(**kwargs)
+        self.end_time = kwargs.get('end_time', None)
+        self.start_time = kwargs.get('start_time', None)
+        self.time_zone = kwargs.get('time_zone', "UTC")
+        self.trigger_type = None  # type: Optional[str]
+
+
+class CronTrigger(TriggerBase):
+    """CronTrigger.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+     https://en.wikipedia.org/wiki/ISO_8601.
+     Recommented format would be "2022-06-01T00:00:01"
+     If not present, the schedule will run indefinitely.
+    :vartype end_time: str
+    :ivar start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+     offset.
+    :vartype start_time: str
+    :ivar time_zone: Specifies time zone in which the schedule runs.
+     TimeZone should follow Windows time zone format. Refer:
+     https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+    :vartype time_zone: str
+    :ivar trigger_type: Required. [Required].Constant filled by server. Possible values include:
+     "Recurrence", "Cron".
+    :vartype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
+    :ivar expression: Required. [Required] Specifies cron expression of schedule.
+     The expression should follow NCronTab format.
+    :vartype expression: str
+    """
+
+    _validation = {
+        'trigger_type': {'required': True},
+        'expression': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'end_time': {'key': 'endTime', 'type': 'str'},
+        'start_time': {'key': 'startTime', 'type': 'str'},
+        'time_zone': {'key': 'timeZone', 'type': 'str'},
+        'trigger_type': {'key': 'triggerType', 'type': 'str'},
+        'expression': {'key': 'expression', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+         https://en.wikipedia.org/wiki/ISO_8601.
+         Recommented format would be "2022-06-01T00:00:01"
+         If not present, the schedule will run indefinitely.
+        :paramtype end_time: str
+        :keyword start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+         offset.
+        :paramtype start_time: str
+        :keyword time_zone: Specifies time zone in which the schedule runs.
+         TimeZone should follow Windows time zone format. Refer:
+         https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+        :paramtype time_zone: str
+        :keyword expression: Required. [Required] Specifies cron expression of schedule.
+         The expression should follow NCronTab format.
+        :paramtype expression: str
+        """
+        super(CronTrigger, self).__init__(**kwargs)
+        self.trigger_type = 'Cron'  # type: str
+        self.expression = kwargs['expression']
+
+
+class CustomForecastHorizon(ForecastHorizon):
+    """The desired maximum forecast horizon in units of time-series frequency.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set forecast horizon value selection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.ForecastHorizonMode
+    :ivar value: Required. [Required] Forecast horizon value.
+    :vartype value: int
+    """
+
+    _validation = {
+        'mode': {'required': True},
+        'value': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: Required. [Required] Forecast horizon value.
+        :paramtype value: int
+        """
+        super(CustomForecastHorizon, self).__init__(**kwargs)
+        self.mode = 'Custom'  # type: str
+        self.value = kwargs['value']
+
+
+class JobInput(msrest.serialization.Model):
+    """Command job definition.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: CustomModelJobInput, LiteralJobInput, MLFlowModelJobInput, MLTableJobInput, TritonModelJobInput, UriFileJobInput, UriFolderJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'job_input_type': {'custom_model': 'CustomModelJobInput', 'literal': 'LiteralJobInput', 'mlflow_model': 'MLFlowModelJobInput', 'mltable': 'MLTableJobInput', 'triton_model': 'TritonModelJobInput', 'uri_file': 'UriFileJobInput', 'uri_folder': 'UriFolderJobInput'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(JobInput, self).__init__(**kwargs)
+        self.description = kwargs.get('description', None)
+        self.job_input_type = None  # type: Optional[str]
+
+
+class CustomModelJobInput(JobInput, AssetJobInput):
+    """CustomModelJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(CustomModelJobInput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs['uri']
+        self.job_input_type = 'custom_model'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class JobOutput(msrest.serialization.Model):
+    """Job output definition container information on where to find job output/logs.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: CustomModelJobOutput, MLFlowModelJobOutput, MLTableJobOutput, TritonModelJobOutput, UriFileJobOutput, UriFolderJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'job_output_type': {'custom_model': 'CustomModelJobOutput', 'mlflow_model': 'MLFlowModelJobOutput', 'mltable': 'MLTableJobOutput', 'triton_model': 'TritonModelJobOutput', 'uri_file': 'UriFileJobOutput', 'uri_folder': 'UriFolderJobOutput'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(JobOutput, self).__init__(**kwargs)
+        self.description = kwargs.get('description', None)
+        self.job_output_type = None  # type: Optional[str]
+
+
+class CustomModelJobOutput(JobOutput, AssetJobOutput):
+    """CustomModelJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(CustomModelJobOutput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs.get('uri', None)
+        self.job_output_type = 'custom_model'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class CustomNCrossValidations(NCrossValidations):
+    """N-Cross validations are specified by user.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Mode for determining N-Cross validations.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.NCrossValidationsMode
+    :ivar value: Required. [Required] N-Cross validations value.
+    :vartype value: int
+    """
+
+    _validation = {
+        'mode': {'required': True},
+        'value': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: Required. [Required] N-Cross validations value.
+        :paramtype value: int
+        """
+        super(CustomNCrossValidations, self).__init__(**kwargs)
+        self.mode = 'Custom'  # type: str
+        self.value = kwargs['value']
+
+
+class CustomSeasonality(Seasonality):
+    """CustomSeasonality.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Seasonality mode.Constant filled by server. Possible values
+     include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.SeasonalityMode
+    :ivar value: Required. [Required] Seasonality value.
+    :vartype value: int
+    """
+
+    _validation = {
+        'mode': {'required': True},
+        'value': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: Required. [Required] Seasonality value.
+        :paramtype value: int
+        """
+        super(CustomSeasonality, self).__init__(**kwargs)
+        self.mode = 'Custom'  # type: str
+        self.value = kwargs['value']
+
+
+class CustomTargetLags(TargetLags):
+    """CustomTargetLags.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set target lags mode - Auto/Custom.Constant filled by server.
+     Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetLagsMode
+    :ivar values: Required. [Required] Set target lags values.
+    :vartype values: list[int]
+    """
+
+    _validation = {
+        'mode': {'required': True},
+        'values': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'values': {'key': 'values', 'type': '[int]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword values: Required. [Required] Set target lags values.
+        :paramtype values: list[int]
+        """
+        super(CustomTargetLags, self).__init__(**kwargs)
+        self.mode = 'Custom'  # type: str
+        self.values = kwargs['values']
+
+
+class CustomTargetRollingWindowSize(TargetRollingWindowSize):
+    """CustomTargetRollingWindowSize.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] TargetRollingWindowSiz detection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetRollingWindowSizeMode
+    :ivar value: Required. [Required] TargetRollingWindowSize value.
+    :vartype value: int
+    """
+
+    _validation = {
+        'mode': {'required': True},
+        'value': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: Required. [Required] TargetRollingWindowSize value.
+        :paramtype value: int
+        """
+        super(CustomTargetRollingWindowSize, self).__init__(**kwargs)
+        self.mode = 'Custom'  # type: str
+        self.value = kwargs['value']
+
+
+class DatabricksSchema(msrest.serialization.Model):
+    """DatabricksSchema.
+
+    :ivar properties: Properties of Databricks.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'DatabricksProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of Databricks.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
+        """
+        super(DatabricksSchema, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class Databricks(Compute, DatabricksSchema):
+    """A DataFactory compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: Properties of Databricks.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'DatabricksProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of Databricks.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(Databricks, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+        self.compute_type = 'Databricks'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = kwargs.get('description', None)
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = kwargs.get('resource_id', None)
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class DatabricksComputeSecretsProperties(msrest.serialization.Model):
+    """Properties of Databricks Compute Secrets.
+
+    :ivar databricks_access_token: access token for databricks account.
+    :vartype databricks_access_token: str
+    """
+
+    _attribute_map = {
+        'databricks_access_token': {'key': 'databricksAccessToken', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword databricks_access_token: access token for databricks account.
+        :paramtype databricks_access_token: str
+        """
+        super(DatabricksComputeSecretsProperties, self).__init__(**kwargs)
+        self.databricks_access_token = kwargs.get('databricks_access_token', None)
+
+
+class DatabricksComputeSecrets(ComputeSecrets, DatabricksComputeSecretsProperties):
+    """Secrets related to a Machine Learning compute based on Databricks.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar databricks_access_token: access token for databricks account.
+    :vartype databricks_access_token: str
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'databricks_access_token': {'key': 'databricksAccessToken', 'type': 'str'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword databricks_access_token: access token for databricks account.
+        :paramtype databricks_access_token: str
+        """
+        super(DatabricksComputeSecrets, self).__init__(**kwargs)
+        self.databricks_access_token = kwargs.get('databricks_access_token', None)
+        self.compute_type = 'Databricks'  # type: str
+
+
+class DatabricksProperties(msrest.serialization.Model):
+    """Properties of Databricks.
+
+    :ivar databricks_access_token: Databricks access token.
+    :vartype databricks_access_token: str
+    :ivar workspace_url: Workspace Url.
+    :vartype workspace_url: str
+    """
+
+    _attribute_map = {
+        'databricks_access_token': {'key': 'databricksAccessToken', 'type': 'str'},
+        'workspace_url': {'key': 'workspaceUrl', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword databricks_access_token: Databricks access token.
+        :paramtype databricks_access_token: str
+        :keyword workspace_url: Workspace Url.
+        :paramtype workspace_url: str
+        """
+        super(DatabricksProperties, self).__init__(**kwargs)
+        self.databricks_access_token = kwargs.get('databricks_access_token', None)
+        self.workspace_url = kwargs.get('workspace_url', None)
+
+
+class DataContainer(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.DataContainerProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'DataContainerProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.DataContainerProperties
+        """
+        super(DataContainer, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class DataContainerProperties(AssetContainer):
+    """Container for data asset versions.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    :ivar data_type: Required. [Required] Specifies the type of data. Possible values include:
+     "uri_file", "uri_folder", "mltable".
+    :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+        'data_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+        'data_type': {'key': 'dataType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword data_type: Required. [Required] Specifies the type of data. Possible values include:
+         "uri_file", "uri_folder", "mltable".
+        :paramtype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+        """
+        super(DataContainerProperties, self).__init__(**kwargs)
+        self.data_type = kwargs['data_type']
+
+
+class DataContainerResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of DataContainer entities.
+
+    :ivar next_link: The link to the next page of DataContainer objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type DataContainer.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.DataContainer]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[DataContainer]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of DataContainer objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type DataContainer.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.DataContainer]
+        """
+        super(DataContainerResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class DataFactory(Compute):
+    """A DataFactory compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(DataFactory, self).__init__(**kwargs)
+        self.compute_type = 'DataFactory'  # type: str
+
+
+class DataLakeAnalyticsSchema(msrest.serialization.Model):
+    """DataLakeAnalyticsSchema.
+
+    :ivar properties:
+    :vartype properties:
+     ~azure.mgmt.machinelearningservices.models.DataLakeAnalyticsSchemaProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'DataLakeAnalyticsSchemaProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties:
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.DataLakeAnalyticsSchemaProperties
+        """
+        super(DataLakeAnalyticsSchema, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class DataLakeAnalytics(Compute, DataLakeAnalyticsSchema):
+    """A DataLakeAnalytics compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties:
+    :vartype properties:
+     ~azure.mgmt.machinelearningservices.models.DataLakeAnalyticsSchemaProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'DataLakeAnalyticsSchemaProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties:
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.DataLakeAnalyticsSchemaProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(DataLakeAnalytics, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+        self.compute_type = 'DataLakeAnalytics'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = kwargs.get('description', None)
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = kwargs.get('resource_id', None)
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class DataLakeAnalyticsSchemaProperties(msrest.serialization.Model):
+    """DataLakeAnalyticsSchemaProperties.
+
+    :ivar data_lake_store_account_name: DataLake Store Account Name.
+    :vartype data_lake_store_account_name: str
+    """
+
+    _attribute_map = {
+        'data_lake_store_account_name': {'key': 'dataLakeStoreAccountName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword data_lake_store_account_name: DataLake Store Account Name.
+        :paramtype data_lake_store_account_name: str
+        """
+        super(DataLakeAnalyticsSchemaProperties, self).__init__(**kwargs)
+        self.data_lake_store_account_name = kwargs.get('data_lake_store_account_name', None)
+
+
+class DataPathAssetReference(AssetReferenceBase):
+    """Reference to an asset via its path in a datastore.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar reference_type: Required. [Required] Specifies the type of asset reference.Constant
+     filled by server. Possible values include: "Id", "DataPath", "OutputPath".
+    :vartype reference_type: str or ~azure.mgmt.machinelearningservices.models.ReferenceType
+    :ivar datastore_id: ARM resource ID of the datastore where the asset is located.
+    :vartype datastore_id: str
+    :ivar path: The path of the file/directory in the datastore.
+    :vartype path: str
+    """
+
+    _validation = {
+        'reference_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'reference_type': {'key': 'referenceType', 'type': 'str'},
+        'datastore_id': {'key': 'datastoreId', 'type': 'str'},
+        'path': {'key': 'path', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword datastore_id: ARM resource ID of the datastore where the asset is located.
+        :paramtype datastore_id: str
+        :keyword path: The path of the file/directory in the datastore.
+        :paramtype path: str
+        """
+        super(DataPathAssetReference, self).__init__(**kwargs)
+        self.reference_type = 'DataPath'  # type: str
+        self.datastore_id = kwargs.get('datastore_id', None)
+        self.path = kwargs.get('path', None)
+
+
+class Datastore(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.DatastoreProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'DatastoreProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatastoreProperties
+        """
+        super(Datastore, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class DatastoreResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of Datastore entities.
+
+    :ivar next_link: The link to the next page of Datastore objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type Datastore.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Datastore]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[Datastore]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of Datastore objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type Datastore.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.Datastore]
+        """
+        super(DatastoreResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class DataVersionBase(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.DataVersionBaseProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'DataVersionBaseProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.DataVersionBaseProperties
+        """
+        super(DataVersionBase, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class DataVersionBaseProperties(AssetBase):
+    """Data version base definition.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: MLTableData, UriFileDataVersion, UriFolderDataVersion.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar data_type: Required. [Required] Specifies the type of data.Constant filled by server.
+     Possible values include: "uri_file", "uri_folder", "mltable".
+    :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+    :ivar data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+    :vartype data_uri: str
+    """
+
+    _validation = {
+        'data_type': {'required': True},
+        'data_uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'data_type': {'key': 'dataType', 'type': 'str'},
+        'data_uri': {'key': 'dataUri', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'data_type': {'mltable': 'MLTableData', 'uri_file': 'UriFileDataVersion', 'uri_folder': 'UriFolderDataVersion'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+        :paramtype data_uri: str
+        """
+        super(DataVersionBaseProperties, self).__init__(**kwargs)
+        self.data_type = 'DataVersionBaseProperties'  # type: str
+        self.data_uri = kwargs['data_uri']
+
+
+class DataVersionBaseResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of DataVersionBase entities.
+
+    :ivar next_link: The link to the next page of DataVersionBase objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type DataVersionBase.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.DataVersionBase]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[DataVersionBase]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of DataVersionBase objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type DataVersionBase.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.DataVersionBase]
+        """
+        super(DataVersionBaseResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class OnlineScaleSettings(msrest.serialization.Model):
+    """Online deployment scaling configuration.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: DefaultScaleSettings, TargetUtilizationScaleSettings.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar scale_type: Required. [Required] Type of deployment scaling algorithm.Constant filled by
+     server. Possible values include: "Default", "TargetUtilization".
+    :vartype scale_type: str or ~azure.mgmt.machinelearningservices.models.ScaleType
+    """
+
+    _validation = {
+        'scale_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'scale_type': {'key': 'scaleType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'scale_type': {'Default': 'DefaultScaleSettings', 'TargetUtilization': 'TargetUtilizationScaleSettings'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(OnlineScaleSettings, self).__init__(**kwargs)
+        self.scale_type = None  # type: Optional[str]
+
+
+class DefaultScaleSettings(OnlineScaleSettings):
+    """DefaultScaleSettings.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar scale_type: Required. [Required] Type of deployment scaling algorithm.Constant filled by
+     server. Possible values include: "Default", "TargetUtilization".
+    :vartype scale_type: str or ~azure.mgmt.machinelearningservices.models.ScaleType
+    """
+
+    _validation = {
+        'scale_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'scale_type': {'key': 'scaleType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(DefaultScaleSettings, self).__init__(**kwargs)
+        self.scale_type = 'Default'  # type: str
+
+
+class DeploymentLogs(msrest.serialization.Model):
+    """DeploymentLogs.
+
+    :ivar content: The retrieved online deployment logs.
+    :vartype content: str
+    """
+
+    _attribute_map = {
+        'content': {'key': 'content', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword content: The retrieved online deployment logs.
+        :paramtype content: str
+        """
+        super(DeploymentLogs, self).__init__(**kwargs)
+        self.content = kwargs.get('content', None)
+
+
+class DeploymentLogsRequest(msrest.serialization.Model):
+    """DeploymentLogsRequest.
+
+    :ivar container_type: The type of container to retrieve logs from. Possible values include:
+     "StorageInitializer", "InferenceServer".
+    :vartype container_type: str or ~azure.mgmt.machinelearningservices.models.ContainerType
+    :ivar tail: The maximum number of lines to tail.
+    :vartype tail: int
+    """
+
+    _attribute_map = {
+        'container_type': {'key': 'containerType', 'type': 'str'},
+        'tail': {'key': 'tail', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword container_type: The type of container to retrieve logs from. Possible values include:
+         "StorageInitializer", "InferenceServer".
+        :paramtype container_type: str or ~azure.mgmt.machinelearningservices.models.ContainerType
+        :keyword tail: The maximum number of lines to tail.
+        :paramtype tail: int
+        """
+        super(DeploymentLogsRequest, self).__init__(**kwargs)
+        self.container_type = kwargs.get('container_type', None)
+        self.tail = kwargs.get('tail', None)
+
+
+class ResourceConfiguration(msrest.serialization.Model):
+    """ResourceConfiguration.
+
+    :ivar instance_count: Optional number of instances or nodes used by the compute target.
+    :vartype instance_count: int
+    :ivar instance_type: Optional type of VM used as supported by the compute target.
+    :vartype instance_type: str
+    :ivar properties: Additional properties bag.
+    :vartype properties: dict[str, any]
+    """
+
+    _attribute_map = {
+        'instance_count': {'key': 'instanceCount', 'type': 'int'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{object}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword instance_count: Optional number of instances or nodes used by the compute target.
+        :paramtype instance_count: int
+        :keyword instance_type: Optional type of VM used as supported by the compute target.
+        :paramtype instance_type: str
+        :keyword properties: Additional properties bag.
+        :paramtype properties: dict[str, any]
+        """
+        super(ResourceConfiguration, self).__init__(**kwargs)
+        self.instance_count = kwargs.get('instance_count', 1)
+        self.instance_type = kwargs.get('instance_type', None)
+        self.properties = kwargs.get('properties', None)
+
+
+class DeploymentResourceConfiguration(ResourceConfiguration):
+    """DeploymentResourceConfiguration.
+
+    :ivar instance_count: Optional number of instances or nodes used by the compute target.
+    :vartype instance_count: int
+    :ivar instance_type: Optional type of VM used as supported by the compute target.
+    :vartype instance_type: str
+    :ivar properties: Additional properties bag.
+    :vartype properties: dict[str, any]
+    """
+
+    _attribute_map = {
+        'instance_count': {'key': 'instanceCount', 'type': 'int'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{object}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword instance_count: Optional number of instances or nodes used by the compute target.
+        :paramtype instance_count: int
+        :keyword instance_type: Optional type of VM used as supported by the compute target.
+        :paramtype instance_type: str
+        :keyword properties: Additional properties bag.
+        :paramtype properties: dict[str, any]
+        """
+        super(DeploymentResourceConfiguration, self).__init__(**kwargs)
+
+
+class DiagnoseRequestProperties(msrest.serialization.Model):
+    """DiagnoseRequestProperties.
+
+    :ivar udr: Setting for diagnosing user defined routing.
+    :vartype udr: dict[str, any]
+    :ivar nsg: Setting for diagnosing network security group.
+    :vartype nsg: dict[str, any]
+    :ivar resource_lock: Setting for diagnosing resource lock.
+    :vartype resource_lock: dict[str, any]
+    :ivar dns_resolution: Setting for diagnosing dns resolution.
+    :vartype dns_resolution: dict[str, any]
+    :ivar storage_account: Setting for diagnosing dependent storage account.
+    :vartype storage_account: dict[str, any]
+    :ivar key_vault: Setting for diagnosing dependent key vault.
+    :vartype key_vault: dict[str, any]
+    :ivar container_registry: Setting for diagnosing dependent container registry.
+    :vartype container_registry: dict[str, any]
+    :ivar application_insights: Setting for diagnosing dependent application insights.
+    :vartype application_insights: dict[str, any]
+    :ivar others: Setting for diagnosing unclassified category of problems.
+    :vartype others: dict[str, any]
+    """
+
+    _attribute_map = {
+        'udr': {'key': 'udr', 'type': '{object}'},
+        'nsg': {'key': 'nsg', 'type': '{object}'},
+        'resource_lock': {'key': 'resourceLock', 'type': '{object}'},
+        'dns_resolution': {'key': 'dnsResolution', 'type': '{object}'},
+        'storage_account': {'key': 'storageAccount', 'type': '{object}'},
+        'key_vault': {'key': 'keyVault', 'type': '{object}'},
+        'container_registry': {'key': 'containerRegistry', 'type': '{object}'},
+        'application_insights': {'key': 'applicationInsights', 'type': '{object}'},
+        'others': {'key': 'others', 'type': '{object}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword udr: Setting for diagnosing user defined routing.
+        :paramtype udr: dict[str, any]
+        :keyword nsg: Setting for diagnosing network security group.
+        :paramtype nsg: dict[str, any]
+        :keyword resource_lock: Setting for diagnosing resource lock.
+        :paramtype resource_lock: dict[str, any]
+        :keyword dns_resolution: Setting for diagnosing dns resolution.
+        :paramtype dns_resolution: dict[str, any]
+        :keyword storage_account: Setting for diagnosing dependent storage account.
+        :paramtype storage_account: dict[str, any]
+        :keyword key_vault: Setting for diagnosing dependent key vault.
+        :paramtype key_vault: dict[str, any]
+        :keyword container_registry: Setting for diagnosing dependent container registry.
+        :paramtype container_registry: dict[str, any]
+        :keyword application_insights: Setting for diagnosing dependent application insights.
+        :paramtype application_insights: dict[str, any]
+        :keyword others: Setting for diagnosing unclassified category of problems.
+        :paramtype others: dict[str, any]
+        """
+        super(DiagnoseRequestProperties, self).__init__(**kwargs)
+        self.udr = kwargs.get('udr', None)
+        self.nsg = kwargs.get('nsg', None)
+        self.resource_lock = kwargs.get('resource_lock', None)
+        self.dns_resolution = kwargs.get('dns_resolution', None)
+        self.storage_account = kwargs.get('storage_account', None)
+        self.key_vault = kwargs.get('key_vault', None)
+        self.container_registry = kwargs.get('container_registry', None)
+        self.application_insights = kwargs.get('application_insights', None)
+        self.others = kwargs.get('others', None)
+
+
+class DiagnoseResponseResult(msrest.serialization.Model):
+    """DiagnoseResponseResult.
+
+    :ivar value:
+    :vartype value: ~azure.mgmt.machinelearningservices.models.DiagnoseResponseResultValue
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': 'DiagnoseResponseResultValue'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value:
+        :paramtype value: ~azure.mgmt.machinelearningservices.models.DiagnoseResponseResultValue
+        """
+        super(DiagnoseResponseResult, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+
+
+class DiagnoseResponseResultValue(msrest.serialization.Model):
+    """DiagnoseResponseResultValue.
+
+    :ivar user_defined_route_results:
+    :vartype user_defined_route_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar network_security_rule_results:
+    :vartype network_security_rule_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar resource_lock_results:
+    :vartype resource_lock_results: list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar dns_resolution_results:
+    :vartype dns_resolution_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar storage_account_results:
+    :vartype storage_account_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar key_vault_results:
+    :vartype key_vault_results: list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar container_registry_results:
+    :vartype container_registry_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar application_insights_results:
+    :vartype application_insights_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar other_results:
+    :vartype other_results: list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    """
+
+    _attribute_map = {
+        'user_defined_route_results': {'key': 'userDefinedRouteResults', 'type': '[DiagnoseResult]'},
+        'network_security_rule_results': {'key': 'networkSecurityRuleResults', 'type': '[DiagnoseResult]'},
+        'resource_lock_results': {'key': 'resourceLockResults', 'type': '[DiagnoseResult]'},
+        'dns_resolution_results': {'key': 'dnsResolutionResults', 'type': '[DiagnoseResult]'},
+        'storage_account_results': {'key': 'storageAccountResults', 'type': '[DiagnoseResult]'},
+        'key_vault_results': {'key': 'keyVaultResults', 'type': '[DiagnoseResult]'},
+        'container_registry_results': {'key': 'containerRegistryResults', 'type': '[DiagnoseResult]'},
+        'application_insights_results': {'key': 'applicationInsightsResults', 'type': '[DiagnoseResult]'},
+        'other_results': {'key': 'otherResults', 'type': '[DiagnoseResult]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword user_defined_route_results:
+        :paramtype user_defined_route_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword network_security_rule_results:
+        :paramtype network_security_rule_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword resource_lock_results:
+        :paramtype resource_lock_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword dns_resolution_results:
+        :paramtype dns_resolution_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword storage_account_results:
+        :paramtype storage_account_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword key_vault_results:
+        :paramtype key_vault_results: list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword container_registry_results:
+        :paramtype container_registry_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword application_insights_results:
+        :paramtype application_insights_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword other_results:
+        :paramtype other_results: list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        """
+        super(DiagnoseResponseResultValue, self).__init__(**kwargs)
+        self.user_defined_route_results = kwargs.get('user_defined_route_results', None)
+        self.network_security_rule_results = kwargs.get('network_security_rule_results', None)
+        self.resource_lock_results = kwargs.get('resource_lock_results', None)
+        self.dns_resolution_results = kwargs.get('dns_resolution_results', None)
+        self.storage_account_results = kwargs.get('storage_account_results', None)
+        self.key_vault_results = kwargs.get('key_vault_results', None)
+        self.container_registry_results = kwargs.get('container_registry_results', None)
+        self.application_insights_results = kwargs.get('application_insights_results', None)
+        self.other_results = kwargs.get('other_results', None)
+
+
+class DiagnoseResult(msrest.serialization.Model):
+    """Result of Diagnose.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar code: Code for workspace setup error.
+    :vartype code: str
+    :ivar level: Level of workspace setup error. Possible values include: "Warning", "Error",
+     "Information".
+    :vartype level: str or ~azure.mgmt.machinelearningservices.models.DiagnoseResultLevel
+    :ivar message: Message of workspace setup error.
+    :vartype message: str
+    """
+
+    _validation = {
+        'code': {'readonly': True},
+        'level': {'readonly': True},
+        'message': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code': {'key': 'code', 'type': 'str'},
+        'level': {'key': 'level', 'type': 'str'},
+        'message': {'key': 'message', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(DiagnoseResult, self).__init__(**kwargs)
+        self.code = None
+        self.level = None
+        self.message = None
+
+
+class DiagnoseWorkspaceParameters(msrest.serialization.Model):
+    """Parameters to diagnose a workspace.
+
+    :ivar value: Value of Parameters.
+    :vartype value: ~azure.mgmt.machinelearningservices.models.DiagnoseRequestProperties
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': 'DiagnoseRequestProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: Value of Parameters.
+        :paramtype value: ~azure.mgmt.machinelearningservices.models.DiagnoseRequestProperties
+        """
+        super(DiagnoseWorkspaceParameters, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+
+
+class DistributionConfiguration(msrest.serialization.Model):
+    """Base definition for job distribution configuration.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: Mpi, PyTorch, TensorFlow.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar distribution_type: Required. [Required] Specifies the type of distribution
+     framework.Constant filled by server. Possible values include: "PyTorch", "TensorFlow", "Mpi".
+    :vartype distribution_type: str or ~azure.mgmt.machinelearningservices.models.DistributionType
+    """
+
+    _validation = {
+        'distribution_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'distribution_type': {'key': 'distributionType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'distribution_type': {'Mpi': 'Mpi', 'PyTorch': 'PyTorch', 'TensorFlow': 'TensorFlow'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(DistributionConfiguration, self).__init__(**kwargs)
+        self.distribution_type = None  # type: Optional[str]
+
+
+class EncryptionKeyVaultProperties(msrest.serialization.Model):
+    """EncryptionKeyVaultProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar key_vault_arm_id: Required. The ArmId of the keyVault where the customer owned encryption
+     key is present.
+    :vartype key_vault_arm_id: str
+    :ivar key_identifier: Required. Key vault uri to access the encryption key.
+    :vartype key_identifier: str
+    :ivar identity_client_id: For future use - The client id of the identity which will be used to
+     access key vault.
+    :vartype identity_client_id: str
+    """
+
+    _validation = {
+        'key_vault_arm_id': {'required': True},
+        'key_identifier': {'required': True},
+    }
+
+    _attribute_map = {
+        'key_vault_arm_id': {'key': 'keyVaultArmId', 'type': 'str'},
+        'key_identifier': {'key': 'keyIdentifier', 'type': 'str'},
+        'identity_client_id': {'key': 'identityClientId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword key_vault_arm_id: Required. The ArmId of the keyVault where the customer owned
+         encryption key is present.
+        :paramtype key_vault_arm_id: str
+        :keyword key_identifier: Required. Key vault uri to access the encryption key.
+        :paramtype key_identifier: str
+        :keyword identity_client_id: For future use - The client id of the identity which will be used
+         to access key vault.
+        :paramtype identity_client_id: str
+        """
+        super(EncryptionKeyVaultProperties, self).__init__(**kwargs)
+        self.key_vault_arm_id = kwargs['key_vault_arm_id']
+        self.key_identifier = kwargs['key_identifier']
+        self.identity_client_id = kwargs.get('identity_client_id', None)
+
+
+class EncryptionProperty(msrest.serialization.Model):
+    """EncryptionProperty.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar status: Required. Indicates whether or not the encryption is enabled for the workspace.
+     Possible values include: "Enabled", "Disabled".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.EncryptionStatus
+    :ivar identity: The identity that will be used to access the key vault for encryption at rest.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityForCmk
+    :ivar key_vault_properties: Required. Customer Key vault properties.
+    :vartype key_vault_properties:
+     ~azure.mgmt.machinelearningservices.models.EncryptionKeyVaultProperties
+    """
+
+    _validation = {
+        'status': {'required': True},
+        'key_vault_properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'status': {'key': 'status', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityForCmk'},
+        'key_vault_properties': {'key': 'keyVaultProperties', 'type': 'EncryptionKeyVaultProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword status: Required. Indicates whether or not the encryption is enabled for the
+         workspace. Possible values include: "Enabled", "Disabled".
+        :paramtype status: str or ~azure.mgmt.machinelearningservices.models.EncryptionStatus
+        :keyword identity: The identity that will be used to access the key vault for encryption at
+         rest.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityForCmk
+        :keyword key_vault_properties: Required. Customer Key vault properties.
+        :paramtype key_vault_properties:
+         ~azure.mgmt.machinelearningservices.models.EncryptionKeyVaultProperties
+        """
+        super(EncryptionProperty, self).__init__(**kwargs)
+        self.status = kwargs['status']
+        self.identity = kwargs.get('identity', None)
+        self.key_vault_properties = kwargs['key_vault_properties']
+
+
+class EndpointAuthKeys(msrest.serialization.Model):
+    """Keys for endpoint authentication.
+
+    :ivar primary_key: The primary key.
+    :vartype primary_key: str
+    :ivar secondary_key: The secondary key.
+    :vartype secondary_key: str
+    """
+
+    _attribute_map = {
+        'primary_key': {'key': 'primaryKey', 'type': 'str'},
+        'secondary_key': {'key': 'secondaryKey', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword primary_key: The primary key.
+        :paramtype primary_key: str
+        :keyword secondary_key: The secondary key.
+        :paramtype secondary_key: str
+        """
+        super(EndpointAuthKeys, self).__init__(**kwargs)
+        self.primary_key = kwargs.get('primary_key', None)
+        self.secondary_key = kwargs.get('secondary_key', None)
+
+
+class EndpointAuthToken(msrest.serialization.Model):
+    """Service Token.
+
+    :ivar access_token: Access token for endpoint authentication.
+    :vartype access_token: str
+    :ivar expiry_time_utc: Access token expiry time (UTC).
+    :vartype expiry_time_utc: long
+    :ivar refresh_after_time_utc: Refresh access token after time (UTC).
+    :vartype refresh_after_time_utc: long
+    :ivar token_type: Access token type.
+    :vartype token_type: str
+    """
+
+    _attribute_map = {
+        'access_token': {'key': 'accessToken', 'type': 'str'},
+        'expiry_time_utc': {'key': 'expiryTimeUtc', 'type': 'long'},
+        'refresh_after_time_utc': {'key': 'refreshAfterTimeUtc', 'type': 'long'},
+        'token_type': {'key': 'tokenType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword access_token: Access token for endpoint authentication.
+        :paramtype access_token: str
+        :keyword expiry_time_utc: Access token expiry time (UTC).
+        :paramtype expiry_time_utc: long
+        :keyword refresh_after_time_utc: Refresh access token after time (UTC).
+        :paramtype refresh_after_time_utc: long
+        :keyword token_type: Access token type.
+        :paramtype token_type: str
+        """
+        super(EndpointAuthToken, self).__init__(**kwargs)
+        self.access_token = kwargs.get('access_token', None)
+        self.expiry_time_utc = kwargs.get('expiry_time_utc', 0)
+        self.refresh_after_time_utc = kwargs.get('refresh_after_time_utc', 0)
+        self.token_type = kwargs.get('token_type', None)
+
+
+class ScheduleActionBase(msrest.serialization.Model):
+    """ScheduleActionBase.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: JobScheduleAction, EndpointScheduleAction.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar action_type: Required. [Required] Specifies the action type of the schedule.Constant
+     filled by server. Possible values include: "CreateJob", "InvokeBatchEndpoint".
+    :vartype action_type: str or ~azure.mgmt.machinelearningservices.models.ScheduleActionType
+    """
+
+    _validation = {
+        'action_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'action_type': {'key': 'actionType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'action_type': {'CreateJob': 'JobScheduleAction', 'InvokeBatchEndpoint': 'EndpointScheduleAction'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ScheduleActionBase, self).__init__(**kwargs)
+        self.action_type = None  # type: Optional[str]
+
+
+class EndpointScheduleAction(ScheduleActionBase):
+    """EndpointScheduleAction.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar action_type: Required. [Required] Specifies the action type of the schedule.Constant
+     filled by server. Possible values include: "CreateJob", "InvokeBatchEndpoint".
+    :vartype action_type: str or ~azure.mgmt.machinelearningservices.models.ScheduleActionType
+    :ivar endpoint_invocation_definition: Required. [Required] Defines Schedule action definition
+     details.
+    
+    
+     .. raw:: html
+    
+        <see href="TBD" />.
+    :vartype endpoint_invocation_definition: any
+    """
+
+    _validation = {
+        'action_type': {'required': True},
+        'endpoint_invocation_definition': {'required': True},
+    }
+
+    _attribute_map = {
+        'action_type': {'key': 'actionType', 'type': 'str'},
+        'endpoint_invocation_definition': {'key': 'endpointInvocationDefinition', 'type': 'object'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword endpoint_invocation_definition: Required. [Required] Defines Schedule action
+         definition details.
+        
+        
+         .. raw:: html
+        
+            <see href="TBD" />.
+        :paramtype endpoint_invocation_definition: any
+        """
+        super(EndpointScheduleAction, self).__init__(**kwargs)
+        self.action_type = 'InvokeBatchEndpoint'  # type: str
+        self.endpoint_invocation_definition = kwargs['endpoint_invocation_definition']
+
+
+class EnvironmentContainer(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.EnvironmentContainerProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'EnvironmentContainerProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.EnvironmentContainerProperties
+        """
+        super(EnvironmentContainer, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class EnvironmentContainerProperties(AssetContainer):
+    """Container for environment specification versions.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(EnvironmentContainerProperties, self).__init__(**kwargs)
+
+
+class EnvironmentContainerResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of EnvironmentContainer entities.
+
+    :ivar next_link: The link to the next page of EnvironmentContainer objects. If null, there are
+     no additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type EnvironmentContainer.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[EnvironmentContainer]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of EnvironmentContainer objects. If null, there
+         are no additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type EnvironmentContainer.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+        """
+        super(EnvironmentContainerResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class EnvironmentVersion(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.EnvironmentVersionProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'EnvironmentVersionProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.EnvironmentVersionProperties
+        """
+        super(EnvironmentVersion, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class EnvironmentVersionProperties(AssetBase):
+    """Environment version details.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar auto_rebuild: Defines if image needs to be rebuilt based on base image changes. Possible
+     values include: "Disabled", "OnBaseImageUpdate".
+    :vartype auto_rebuild: str or ~azure.mgmt.machinelearningservices.models.AutoRebuildSetting
+    :ivar build: Configuration settings for Docker build context.
+    :vartype build: ~azure.mgmt.machinelearningservices.models.BuildContext
+    :ivar conda_file: Standard configuration file used by Conda that lets you install any kind of
+     package, including Python, R, and C/C++ packages.
+    
+    
+     .. raw:: html
+    
+        <see
+     href="https://repo2docker.readthedocs.io/en/latest/config_files.html#environment-yml-install-a-conda-environment"
+     />.
+    :vartype conda_file: str
+    :ivar environment_type: Environment type is either user managed or curated by the Azure ML
+     service
+    
+    
+     .. raw:: html
+    
+        <see
+     href="https://learn.microsoft.com/azure/machine-learning/resource-curated-environments"
+     />. Possible values include: "Curated", "UserCreated".
+    :vartype environment_type: str or ~azure.mgmt.machinelearningservices.models.EnvironmentType
+    :ivar image: Name of the image that will be used for the environment.
+    
+    
+     .. raw:: html
+    
+        <seealso
+     href="https://learn.microsoft.com/azure/machine-learning/how-to-deploy-custom-docker-image#use-a-custom-base-image"
+     />.
+    :vartype image: str
+    :ivar inference_config: Defines configuration specific to inference.
+    :vartype inference_config:
+     ~azure.mgmt.machinelearningservices.models.InferenceContainerProperties
+    :ivar os_type: The OS type of the environment. Possible values include: "Linux", "Windows".
+    :vartype os_type: str or ~azure.mgmt.machinelearningservices.models.OperatingSystemType
+    """
+
+    _validation = {
+        'environment_type': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'auto_rebuild': {'key': 'autoRebuild', 'type': 'str'},
+        'build': {'key': 'build', 'type': 'BuildContext'},
+        'conda_file': {'key': 'condaFile', 'type': 'str'},
+        'environment_type': {'key': 'environmentType', 'type': 'str'},
+        'image': {'key': 'image', 'type': 'str'},
+        'inference_config': {'key': 'inferenceConfig', 'type': 'InferenceContainerProperties'},
+        'os_type': {'key': 'osType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword auto_rebuild: Defines if image needs to be rebuilt based on base image changes.
+         Possible values include: "Disabled", "OnBaseImageUpdate".
+        :paramtype auto_rebuild: str or ~azure.mgmt.machinelearningservices.models.AutoRebuildSetting
+        :keyword build: Configuration settings for Docker build context.
+        :paramtype build: ~azure.mgmt.machinelearningservices.models.BuildContext
+        :keyword conda_file: Standard configuration file used by Conda that lets you install any kind
+         of package, including Python, R, and C/C++ packages.
+        
+        
+         .. raw:: html
+        
+            <see
+         href="https://repo2docker.readthedocs.io/en/latest/config_files.html#environment-yml-install-a-conda-environment"
+         />.
+        :paramtype conda_file: str
+        :keyword image: Name of the image that will be used for the environment.
+        
+        
+         .. raw:: html
+        
+            <seealso
+         href="https://learn.microsoft.com/azure/machine-learning/how-to-deploy-custom-docker-image#use-a-custom-base-image"
+         />.
+        :paramtype image: str
+        :keyword inference_config: Defines configuration specific to inference.
+        :paramtype inference_config:
+         ~azure.mgmt.machinelearningservices.models.InferenceContainerProperties
+        :keyword os_type: The OS type of the environment. Possible values include: "Linux", "Windows".
+        :paramtype os_type: str or ~azure.mgmt.machinelearningservices.models.OperatingSystemType
+        """
+        super(EnvironmentVersionProperties, self).__init__(**kwargs)
+        self.auto_rebuild = kwargs.get('auto_rebuild', None)
+        self.build = kwargs.get('build', None)
+        self.conda_file = kwargs.get('conda_file', None)
+        self.environment_type = None
+        self.image = kwargs.get('image', None)
+        self.inference_config = kwargs.get('inference_config', None)
+        self.os_type = kwargs.get('os_type', None)
+
+
+class EnvironmentVersionResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of EnvironmentVersion entities.
+
+    :ivar next_link: The link to the next page of EnvironmentVersion objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type EnvironmentVersion.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[EnvironmentVersion]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of EnvironmentVersion objects. If null, there are
+         no additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type EnvironmentVersion.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+        """
+        super(EnvironmentVersionResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class ErrorAdditionalInfo(msrest.serialization.Model):
+    """The resource management error additional info.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar type: The additional info type.
+    :vartype type: str
+    :ivar info: The additional info.
+    :vartype info: any
+    """
+
+    _validation = {
+        'type': {'readonly': True},
+        'info': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'type': {'key': 'type', 'type': 'str'},
+        'info': {'key': 'info', 'type': 'object'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ErrorAdditionalInfo, self).__init__(**kwargs)
+        self.type = None
+        self.info = None
+
+
+class ErrorDetail(msrest.serialization.Model):
+    """The error detail.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar code: The error code.
+    :vartype code: str
+    :ivar message: The error message.
+    :vartype message: str
+    :ivar target: The error target.
+    :vartype target: str
+    :ivar details: The error details.
+    :vartype details: list[~azure.mgmt.machinelearningservices.models.ErrorDetail]
+    :ivar additional_info: The error additional info.
+    :vartype additional_info: list[~azure.mgmt.machinelearningservices.models.ErrorAdditionalInfo]
+    """
+
+    _validation = {
+        'code': {'readonly': True},
+        'message': {'readonly': True},
+        'target': {'readonly': True},
+        'details': {'readonly': True},
+        'additional_info': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code': {'key': 'code', 'type': 'str'},
+        'message': {'key': 'message', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'details': {'key': 'details', 'type': '[ErrorDetail]'},
+        'additional_info': {'key': 'additionalInfo', 'type': '[ErrorAdditionalInfo]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ErrorDetail, self).__init__(**kwargs)
+        self.code = None
+        self.message = None
+        self.target = None
+        self.details = None
+        self.additional_info = None
+
+
+class ErrorResponse(msrest.serialization.Model):
+    """Common error response for all Azure Resource Manager APIs to return error details for failed operations. (This also follows the OData error response format.).
+
+    :ivar error: The error object.
+    :vartype error: ~azure.mgmt.machinelearningservices.models.ErrorDetail
+    """
+
+    _attribute_map = {
+        'error': {'key': 'error', 'type': 'ErrorDetail'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword error: The error object.
+        :paramtype error: ~azure.mgmt.machinelearningservices.models.ErrorDetail
+        """
+        super(ErrorResponse, self).__init__(**kwargs)
+        self.error = kwargs.get('error', None)
+
+
+class EstimatedVMPrice(msrest.serialization.Model):
+    """The estimated price info for using a VM of a particular OS type, tier, etc.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar retail_price: Required. The price charged for using the VM.
+    :vartype retail_price: float
+    :ivar os_type: Required. Operating system type used by the VM. Possible values include:
+     "Linux", "Windows".
+    :vartype os_type: str or ~azure.mgmt.machinelearningservices.models.VMPriceOSType
+    :ivar vm_tier: Required. The type of the VM. Possible values include: "Standard",
+     "LowPriority", "Spot".
+    :vartype vm_tier: str or ~azure.mgmt.machinelearningservices.models.VMTier
+    """
+
+    _validation = {
+        'retail_price': {'required': True},
+        'os_type': {'required': True},
+        'vm_tier': {'required': True},
+    }
+
+    _attribute_map = {
+        'retail_price': {'key': 'retailPrice', 'type': 'float'},
+        'os_type': {'key': 'osType', 'type': 'str'},
+        'vm_tier': {'key': 'vmTier', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword retail_price: Required. The price charged for using the VM.
+        :paramtype retail_price: float
+        :keyword os_type: Required. Operating system type used by the VM. Possible values include:
+         "Linux", "Windows".
+        :paramtype os_type: str or ~azure.mgmt.machinelearningservices.models.VMPriceOSType
+        :keyword vm_tier: Required. The type of the VM. Possible values include: "Standard",
+         "LowPriority", "Spot".
+        :paramtype vm_tier: str or ~azure.mgmt.machinelearningservices.models.VMTier
+        """
+        super(EstimatedVMPrice, self).__init__(**kwargs)
+        self.retail_price = kwargs['retail_price']
+        self.os_type = kwargs['os_type']
+        self.vm_tier = kwargs['vm_tier']
+
+
+class EstimatedVMPrices(msrest.serialization.Model):
+    """The estimated price info for using a VM.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar billing_currency: Required. Three lettered code specifying the currency of the VM price.
+     Example: USD. Possible values include: "USD".
+    :vartype billing_currency: str or ~azure.mgmt.machinelearningservices.models.BillingCurrency
+    :ivar unit_of_measure: Required. The unit of time measurement for the specified VM price.
+     Example: OneHour. Possible values include: "OneHour".
+    :vartype unit_of_measure: str or ~azure.mgmt.machinelearningservices.models.UnitOfMeasure
+    :ivar values: Required. The list of estimated prices for using a VM of a particular OS type,
+     tier, etc.
+    :vartype values: list[~azure.mgmt.machinelearningservices.models.EstimatedVMPrice]
+    """
+
+    _validation = {
+        'billing_currency': {'required': True},
+        'unit_of_measure': {'required': True},
+        'values': {'required': True},
+    }
+
+    _attribute_map = {
+        'billing_currency': {'key': 'billingCurrency', 'type': 'str'},
+        'unit_of_measure': {'key': 'unitOfMeasure', 'type': 'str'},
+        'values': {'key': 'values', 'type': '[EstimatedVMPrice]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword billing_currency: Required. Three lettered code specifying the currency of the VM
+         price. Example: USD. Possible values include: "USD".
+        :paramtype billing_currency: str or ~azure.mgmt.machinelearningservices.models.BillingCurrency
+        :keyword unit_of_measure: Required. The unit of time measurement for the specified VM price.
+         Example: OneHour. Possible values include: "OneHour".
+        :paramtype unit_of_measure: str or ~azure.mgmt.machinelearningservices.models.UnitOfMeasure
+        :keyword values: Required. The list of estimated prices for using a VM of a particular OS type,
+         tier, etc.
+        :paramtype values: list[~azure.mgmt.machinelearningservices.models.EstimatedVMPrice]
+        """
+        super(EstimatedVMPrices, self).__init__(**kwargs)
+        self.billing_currency = kwargs['billing_currency']
+        self.unit_of_measure = kwargs['unit_of_measure']
+        self.values = kwargs['values']
+
+
+class ExternalFQDNResponse(msrest.serialization.Model):
+    """ExternalFQDNResponse.
+
+    :ivar value:
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.FQDNEndpoints]
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[FQDNEndpoints]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value:
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.FQDNEndpoints]
+        """
+        super(ExternalFQDNResponse, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+
+
+class FeaturizationSettings(msrest.serialization.Model):
+    """Featurization Configuration.
+
+    :ivar dataset_language: Dataset language, useful for the text data.
+    :vartype dataset_language: str
+    """
+
+    _attribute_map = {
+        'dataset_language': {'key': 'datasetLanguage', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword dataset_language: Dataset language, useful for the text data.
+        :paramtype dataset_language: str
+        """
+        super(FeaturizationSettings, self).__init__(**kwargs)
+        self.dataset_language = kwargs.get('dataset_language', None)
+
+
+class FlavorData(msrest.serialization.Model):
+    """FlavorData.
+
+    :ivar data: Model flavor-specific data.
+    :vartype data: dict[str, str]
+    """
+
+    _attribute_map = {
+        'data': {'key': 'data', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword data: Model flavor-specific data.
+        :paramtype data: dict[str, str]
+        """
+        super(FlavorData, self).__init__(**kwargs)
+        self.data = kwargs.get('data', None)
+
+
+class Forecasting(AutoMLVertical, TableVertical):
+    """Forecasting task in AutoML Table vertical.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar cv_split_column_names: Columns to use for CVSplit data.
+    :vartype cv_split_column_names: list[str]
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+    :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
+     when validation dataset is not provided.
+    :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar test_data: Test data input.
+    :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
+     purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype test_data_size: float
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar weight_column_name: The name of the sample weight column. Automated ML supports a
+     weighted column as an input, causing rows in the data to be weighted up or down.
+    :vartype weight_column_name: str
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar forecasting_settings: Forecasting task specific inputs.
+    :vartype forecasting_settings: ~azure.mgmt.machinelearningservices.models.ForecastingSettings
+    :ivar primary_metric: Primary metric for forecasting task. Possible values include:
+     "SpearmanCorrelation", "NormalizedRootMeanSquaredError", "R2Score",
+     "NormalizedMeanAbsoluteError".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ForecastingPrimaryMetrics
+    :ivar training_settings: Inputs for training phase for an AutoML Job.
+    :vartype training_settings:
+     ~azure.mgmt.machinelearningservices.models.ForecastingTrainingSettings
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'TableVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'TableVerticalLimitSettings'},
+        'n_cross_validations': {'key': 'nCrossValidations', 'type': 'NCrossValidations'},
+        'test_data': {'key': 'testData', 'type': 'MLTableJobInput'},
+        'test_data_size': {'key': 'testDataSize', 'type': 'float'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'forecasting_settings': {'key': 'forecastingSettings', 'type': 'ForecastingSettings'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+        'training_settings': {'key': 'trainingSettings', 'type': 'ForecastingTrainingSettings'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword cv_split_column_names: Columns to use for CVSplit data.
+        :paramtype cv_split_column_names: list[str]
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+        :keyword n_cross_validations: Number of cross validation folds to be applied on training
+         dataset
+         when validation dataset is not provided.
+        :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword test_data: Test data input.
+        :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
+         purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype test_data_size: float
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword weight_column_name: The name of the sample weight column. Automated ML supports a
+         weighted column as an input, causing rows in the data to be weighted up or down.
+        :paramtype weight_column_name: str
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword forecasting_settings: Forecasting task specific inputs.
+        :paramtype forecasting_settings: ~azure.mgmt.machinelearningservices.models.ForecastingSettings
+        :keyword primary_metric: Primary metric for forecasting task. Possible values include:
+         "SpearmanCorrelation", "NormalizedRootMeanSquaredError", "R2Score",
+         "NormalizedMeanAbsoluteError".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ForecastingPrimaryMetrics
+        :keyword training_settings: Inputs for training phase for an AutoML Job.
+        :paramtype training_settings:
+         ~azure.mgmt.machinelearningservices.models.ForecastingTrainingSettings
+        """
+        super(Forecasting, self).__init__(**kwargs)
+        self.cv_split_column_names = kwargs.get('cv_split_column_names', None)
+        self.featurization_settings = kwargs.get('featurization_settings', None)
+        self.limit_settings = kwargs.get('limit_settings', None)
+        self.n_cross_validations = kwargs.get('n_cross_validations', None)
+        self.test_data = kwargs.get('test_data', None)
+        self.test_data_size = kwargs.get('test_data_size', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.validation_data_size = kwargs.get('validation_data_size', None)
+        self.weight_column_name = kwargs.get('weight_column_name', None)
+        self.task_type = 'Forecasting'  # type: str
+        self.forecasting_settings = kwargs.get('forecasting_settings', None)
+        self.primary_metric = kwargs.get('primary_metric', None)
+        self.training_settings = kwargs.get('training_settings', None)
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.training_data = kwargs['training_data']
+
+
+class ForecastingSettings(msrest.serialization.Model):
+    """Forecasting specific parameters.
+
+    :ivar country_or_region_for_holidays: Country or region for holidays for forecasting tasks.
+     These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
+    :vartype country_or_region_for_holidays: str
+    :ivar cv_step_size: Number of periods between the origin time of one CV fold and the next fold.
+     For
+     example, if ``CVStepSize`` = 3 for daily data, the origin time for each fold will be
+     three days apart.
+    :vartype cv_step_size: int
+    :ivar feature_lags: Flag for generating lags for the numeric features with 'auto' or null.
+     Possible values include: "None", "Auto".
+    :vartype feature_lags: str or ~azure.mgmt.machinelearningservices.models.FeatureLags
+    :ivar forecast_horizon: The desired maximum forecast horizon in units of time-series frequency.
+    :vartype forecast_horizon: ~azure.mgmt.machinelearningservices.models.ForecastHorizon
+    :ivar frequency: When forecasting, this parameter represents the period with which the forecast
+     is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency
+     by default.
+    :vartype frequency: str
+    :ivar seasonality: Set time series seasonality as an integer multiple of the series frequency.
+     If seasonality is set to 'auto', it will be inferred.
+    :vartype seasonality: ~azure.mgmt.machinelearningservices.models.Seasonality
+    :ivar short_series_handling_config: The parameter defining how if AutoML should handle short
+     time series. Possible values include: "None", "Auto", "Pad", "Drop".
+    :vartype short_series_handling_config: str or
+     ~azure.mgmt.machinelearningservices.models.ShortSeriesHandlingConfiguration
+    :ivar target_aggregate_function: The function to be used to aggregate the time series target
+     column to conform to a user specified frequency.
+     If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the
+     error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
+     Possible values include: "None", "Sum", "Max", "Min", "Mean".
+    :vartype target_aggregate_function: str or
+     ~azure.mgmt.machinelearningservices.models.TargetAggregationFunction
+    :ivar target_lags: The number of past periods to lag from the target column.
+    :vartype target_lags: ~azure.mgmt.machinelearningservices.models.TargetLags
+    :ivar target_rolling_window_size: The number of past periods used to create a rolling window
+     average of the target column.
+    :vartype target_rolling_window_size:
+     ~azure.mgmt.machinelearningservices.models.TargetRollingWindowSize
+    :ivar time_column_name: The name of the time column. This parameter is required when
+     forecasting to specify the datetime column in the input data used for building the time series
+     and inferring its frequency.
+    :vartype time_column_name: str
+    :ivar time_series_id_column_names: The names of columns used to group a timeseries. It can be
+     used to create multiple series.
+     If grain is not defined, the data set is assumed to be one time-series. This parameter is used
+     with task type forecasting.
+    :vartype time_series_id_column_names: list[str]
+    :ivar use_stl: Configure STL Decomposition of the time-series target column. Possible values
+     include: "None", "Season", "SeasonTrend".
+    :vartype use_stl: str or ~azure.mgmt.machinelearningservices.models.UseStl
+    """
+
+    _attribute_map = {
+        'country_or_region_for_holidays': {'key': 'countryOrRegionForHolidays', 'type': 'str'},
+        'cv_step_size': {'key': 'cvStepSize', 'type': 'int'},
+        'feature_lags': {'key': 'featureLags', 'type': 'str'},
+        'forecast_horizon': {'key': 'forecastHorizon', 'type': 'ForecastHorizon'},
+        'frequency': {'key': 'frequency', 'type': 'str'},
+        'seasonality': {'key': 'seasonality', 'type': 'Seasonality'},
+        'short_series_handling_config': {'key': 'shortSeriesHandlingConfig', 'type': 'str'},
+        'target_aggregate_function': {'key': 'targetAggregateFunction', 'type': 'str'},
+        'target_lags': {'key': 'targetLags', 'type': 'TargetLags'},
+        'target_rolling_window_size': {'key': 'targetRollingWindowSize', 'type': 'TargetRollingWindowSize'},
+        'time_column_name': {'key': 'timeColumnName', 'type': 'str'},
+        'time_series_id_column_names': {'key': 'timeSeriesIdColumnNames', 'type': '[str]'},
+        'use_stl': {'key': 'useStl', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword country_or_region_for_holidays: Country or region for holidays for forecasting tasks.
+         These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
+        :paramtype country_or_region_for_holidays: str
+        :keyword cv_step_size: Number of periods between the origin time of one CV fold and the next
+         fold. For
+         example, if ``CVStepSize`` = 3 for daily data, the origin time for each fold will be
+         three days apart.
+        :paramtype cv_step_size: int
+        :keyword feature_lags: Flag for generating lags for the numeric features with 'auto' or null.
+         Possible values include: "None", "Auto".
+        :paramtype feature_lags: str or ~azure.mgmt.machinelearningservices.models.FeatureLags
+        :keyword forecast_horizon: The desired maximum forecast horizon in units of time-series
+         frequency.
+        :paramtype forecast_horizon: ~azure.mgmt.machinelearningservices.models.ForecastHorizon
+        :keyword frequency: When forecasting, this parameter represents the period with which the
+         forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset
+         frequency by default.
+        :paramtype frequency: str
+        :keyword seasonality: Set time series seasonality as an integer multiple of the series
+         frequency.
+         If seasonality is set to 'auto', it will be inferred.
+        :paramtype seasonality: ~azure.mgmt.machinelearningservices.models.Seasonality
+        :keyword short_series_handling_config: The parameter defining how if AutoML should handle short
+         time series. Possible values include: "None", "Auto", "Pad", "Drop".
+        :paramtype short_series_handling_config: str or
+         ~azure.mgmt.machinelearningservices.models.ShortSeriesHandlingConfiguration
+        :keyword target_aggregate_function: The function to be used to aggregate the time series target
+         column to conform to a user specified frequency.
+         If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the
+         error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
+         Possible values include: "None", "Sum", "Max", "Min", "Mean".
+        :paramtype target_aggregate_function: str or
+         ~azure.mgmt.machinelearningservices.models.TargetAggregationFunction
+        :keyword target_lags: The number of past periods to lag from the target column.
+        :paramtype target_lags: ~azure.mgmt.machinelearningservices.models.TargetLags
+        :keyword target_rolling_window_size: The number of past periods used to create a rolling window
+         average of the target column.
+        :paramtype target_rolling_window_size:
+         ~azure.mgmt.machinelearningservices.models.TargetRollingWindowSize
+        :keyword time_column_name: The name of the time column. This parameter is required when
+         forecasting to specify the datetime column in the input data used for building the time series
+         and inferring its frequency.
+        :paramtype time_column_name: str
+        :keyword time_series_id_column_names: The names of columns used to group a timeseries. It can
+         be used to create multiple series.
+         If grain is not defined, the data set is assumed to be one time-series. This parameter is used
+         with task type forecasting.
+        :paramtype time_series_id_column_names: list[str]
+        :keyword use_stl: Configure STL Decomposition of the time-series target column. Possible values
+         include: "None", "Season", "SeasonTrend".
+        :paramtype use_stl: str or ~azure.mgmt.machinelearningservices.models.UseStl
+        """
+        super(ForecastingSettings, self).__init__(**kwargs)
+        self.country_or_region_for_holidays = kwargs.get('country_or_region_for_holidays', None)
+        self.cv_step_size = kwargs.get('cv_step_size', None)
+        self.feature_lags = kwargs.get('feature_lags', None)
+        self.forecast_horizon = kwargs.get('forecast_horizon', None)
+        self.frequency = kwargs.get('frequency', None)
+        self.seasonality = kwargs.get('seasonality', None)
+        self.short_series_handling_config = kwargs.get('short_series_handling_config', None)
+        self.target_aggregate_function = kwargs.get('target_aggregate_function', None)
+        self.target_lags = kwargs.get('target_lags', None)
+        self.target_rolling_window_size = kwargs.get('target_rolling_window_size', None)
+        self.time_column_name = kwargs.get('time_column_name', None)
+        self.time_series_id_column_names = kwargs.get('time_series_id_column_names', None)
+        self.use_stl = kwargs.get('use_stl', None)
+
+
+class ForecastingTrainingSettings(TrainingSettings):
+    """Forecasting Training related configuration.
+
+    :ivar enable_dnn_training: Enable recommendation of DNN models.
+    :vartype enable_dnn_training: bool
+    :ivar enable_model_explainability: Flag to turn on explainability on best model.
+    :vartype enable_model_explainability: bool
+    :ivar enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+    :vartype enable_onnx_compatible_models: bool
+    :ivar enable_stack_ensemble: Enable stack ensemble run.
+    :vartype enable_stack_ensemble: bool
+    :ivar enable_vote_ensemble: Enable voting ensemble run.
+    :vartype enable_vote_ensemble: bool
+    :ivar ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+     generation, multiple fitted models from the previous child runs are downloaded.
+     Configure this parameter with a higher value than 300 secs, if more time is needed.
+    :vartype ensemble_model_download_timeout: ~datetime.timedelta
+    :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+    :vartype stack_ensemble_settings:
+     ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    :ivar allowed_training_algorithms: Allowed models for forecasting task.
+    :vartype allowed_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.ForecastingModels]
+    :ivar blocked_training_algorithms: Blocked models for forecasting task.
+    :vartype blocked_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.ForecastingModels]
+    """
+
+    _attribute_map = {
+        'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
+        'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
+        'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
+        'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
+        'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
+        'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'duration'},
+        'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'StackEnsembleSettings'},
+        'allowed_training_algorithms': {'key': 'allowedTrainingAlgorithms', 'type': '[str]'},
+        'blocked_training_algorithms': {'key': 'blockedTrainingAlgorithms', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword enable_dnn_training: Enable recommendation of DNN models.
+        :paramtype enable_dnn_training: bool
+        :keyword enable_model_explainability: Flag to turn on explainability on best model.
+        :paramtype enable_model_explainability: bool
+        :keyword enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+        :paramtype enable_onnx_compatible_models: bool
+        :keyword enable_stack_ensemble: Enable stack ensemble run.
+        :paramtype enable_stack_ensemble: bool
+        :keyword enable_vote_ensemble: Enable voting ensemble run.
+        :paramtype enable_vote_ensemble: bool
+        :keyword ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+         generation, multiple fitted models from the previous child runs are downloaded.
+         Configure this parameter with a higher value than 300 secs, if more time is needed.
+        :paramtype ensemble_model_download_timeout: ~datetime.timedelta
+        :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+        :paramtype stack_ensemble_settings:
+         ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        :keyword allowed_training_algorithms: Allowed models for forecasting task.
+        :paramtype allowed_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.ForecastingModels]
+        :keyword blocked_training_algorithms: Blocked models for forecasting task.
+        :paramtype blocked_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.ForecastingModels]
+        """
+        super(ForecastingTrainingSettings, self).__init__(**kwargs)
+        self.allowed_training_algorithms = kwargs.get('allowed_training_algorithms', None)
+        self.blocked_training_algorithms = kwargs.get('blocked_training_algorithms', None)
+
+
+class FQDNEndpoint(msrest.serialization.Model):
+    """FQDNEndpoint.
+
+    :ivar domain_name:
+    :vartype domain_name: str
+    :ivar endpoint_details:
+    :vartype endpoint_details: list[~azure.mgmt.machinelearningservices.models.FQDNEndpointDetail]
+    """
+
+    _attribute_map = {
+        'domain_name': {'key': 'domainName', 'type': 'str'},
+        'endpoint_details': {'key': 'endpointDetails', 'type': '[FQDNEndpointDetail]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword domain_name:
+        :paramtype domain_name: str
+        :keyword endpoint_details:
+        :paramtype endpoint_details:
+         list[~azure.mgmt.machinelearningservices.models.FQDNEndpointDetail]
+        """
+        super(FQDNEndpoint, self).__init__(**kwargs)
+        self.domain_name = kwargs.get('domain_name', None)
+        self.endpoint_details = kwargs.get('endpoint_details', None)
+
+
+class FQDNEndpointDetail(msrest.serialization.Model):
+    """FQDNEndpointDetail.
+
+    :ivar port:
+    :vartype port: int
+    """
+
+    _attribute_map = {
+        'port': {'key': 'port', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword port:
+        :paramtype port: int
+        """
+        super(FQDNEndpointDetail, self).__init__(**kwargs)
+        self.port = kwargs.get('port', None)
+
+
+class FQDNEndpoints(msrest.serialization.Model):
+    """FQDNEndpoints.
+
+    :ivar properties:
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.FQDNEndpointsProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'FQDNEndpointsProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties:
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.FQDNEndpointsProperties
+        """
+        super(FQDNEndpoints, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class FQDNEndpointsProperties(msrest.serialization.Model):
+    """FQDNEndpointsProperties.
+
+    :ivar category:
+    :vartype category: str
+    :ivar endpoints:
+    :vartype endpoints: list[~azure.mgmt.machinelearningservices.models.FQDNEndpoint]
+    """
+
+    _attribute_map = {
+        'category': {'key': 'category', 'type': 'str'},
+        'endpoints': {'key': 'endpoints', 'type': '[FQDNEndpoint]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword category:
+        :paramtype category: str
+        :keyword endpoints:
+        :paramtype endpoints: list[~azure.mgmt.machinelearningservices.models.FQDNEndpoint]
+        """
+        super(FQDNEndpointsProperties, self).__init__(**kwargs)
+        self.category = kwargs.get('category', None)
+        self.endpoints = kwargs.get('endpoints', None)
+
+
+class GridSamplingAlgorithm(SamplingAlgorithm):
+    """Defines a Sampling Algorithm that exhaustively generates every value combination in the space.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar sampling_algorithm_type: Required. [Required] The algorithm used for generating
+     hyperparameter values, along with configuration properties.Constant filled by server. Possible
+     values include: "Grid", "Random", "Bayesian".
+    :vartype sampling_algorithm_type: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    """
+
+    _validation = {
+        'sampling_algorithm_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'sampling_algorithm_type': {'key': 'samplingAlgorithmType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(GridSamplingAlgorithm, self).__init__(**kwargs)
+        self.sampling_algorithm_type = 'Grid'  # type: str
+
+
+class HDInsightSchema(msrest.serialization.Model):
+    """HDInsightSchema.
+
+    :ivar properties: HDInsight compute properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'HDInsightProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: HDInsight compute properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+        """
+        super(HDInsightSchema, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class HDInsight(Compute, HDInsightSchema):
+    """A HDInsight compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: HDInsight compute properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'HDInsightProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: HDInsight compute properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(HDInsight, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+        self.compute_type = 'HDInsight'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = kwargs.get('description', None)
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = kwargs.get('resource_id', None)
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class HDInsightProperties(msrest.serialization.Model):
+    """HDInsight compute properties.
+
+    :ivar ssh_port: Port open for ssh connections on the master node of the cluster.
+    :vartype ssh_port: int
+    :ivar address: Public IP address of the master node of the cluster.
+    :vartype address: str
+    :ivar administrator_account: Admin credentials for master node of the cluster.
+    :vartype administrator_account:
+     ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+    """
+
+    _attribute_map = {
+        'ssh_port': {'key': 'sshPort', 'type': 'int'},
+        'address': {'key': 'address', 'type': 'str'},
+        'administrator_account': {'key': 'administratorAccount', 'type': 'VirtualMachineSshCredentials'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword ssh_port: Port open for ssh connections on the master node of the cluster.
+        :paramtype ssh_port: int
+        :keyword address: Public IP address of the master node of the cluster.
+        :paramtype address: str
+        :keyword administrator_account: Admin credentials for master node of the cluster.
+        :paramtype administrator_account:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+        """
+        super(HDInsightProperties, self).__init__(**kwargs)
+        self.ssh_port = kwargs.get('ssh_port', None)
+        self.address = kwargs.get('address', None)
+        self.administrator_account = kwargs.get('administrator_account', None)
+
+
+class IdAssetReference(AssetReferenceBase):
+    """Reference to an asset via its ARM resource ID.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar reference_type: Required. [Required] Specifies the type of asset reference.Constant
+     filled by server. Possible values include: "Id", "DataPath", "OutputPath".
+    :vartype reference_type: str or ~azure.mgmt.machinelearningservices.models.ReferenceType
+    :ivar asset_id: Required. [Required] ARM resource ID of the asset.
+    :vartype asset_id: str
+    """
+
+    _validation = {
+        'reference_type': {'required': True},
+        'asset_id': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'reference_type': {'key': 'referenceType', 'type': 'str'},
+        'asset_id': {'key': 'assetId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword asset_id: Required. [Required] ARM resource ID of the asset.
+        :paramtype asset_id: str
+        """
+        super(IdAssetReference, self).__init__(**kwargs)
+        self.reference_type = 'Id'  # type: str
+        self.asset_id = kwargs['asset_id']
+
+
+class IdentityForCmk(msrest.serialization.Model):
+    """Identity that will be used to access key vault for encryption at rest.
+
+    :ivar user_assigned_identity: The ArmId of the user assigned identity that will be used to
+     access the customer managed key vault.
+    :vartype user_assigned_identity: str
+    """
+
+    _attribute_map = {
+        'user_assigned_identity': {'key': 'userAssignedIdentity', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword user_assigned_identity: The ArmId of the user assigned identity that will be used to
+         access the customer managed key vault.
+        :paramtype user_assigned_identity: str
+        """
+        super(IdentityForCmk, self).__init__(**kwargs)
+        self.user_assigned_identity = kwargs.get('user_assigned_identity', None)
+
+
+class ImageVertical(msrest.serialization.Model):
+    """Abstract class for AutoML tasks that train image (computer vision) models -
+such as Image Classification / Image Classification Multilabel / Image Object Detection / Image Instance Segmentation.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        """
+        super(ImageVertical, self).__init__(**kwargs)
+        self.limit_settings = kwargs['limit_settings']
+        self.sweep_settings = kwargs.get('sweep_settings', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.validation_data_size = kwargs.get('validation_data_size', None)
+
+
+class ImageClassificationBase(ImageVertical):
+    """ImageClassificationBase.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsClassification'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsClassification]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+        """
+        super(ImageClassificationBase, self).__init__(**kwargs)
+        self.model_settings = kwargs.get('model_settings', None)
+        self.search_space = kwargs.get('search_space', None)
+
+
+class ImageClassification(AutoMLVertical, ImageClassificationBase):
+    """Image Classification. Multi-class image classification is used when an image is classified with only a single label
+from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog' or a 'duck'.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric to optimize for this task. Possible values include:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsClassification'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsClassification]'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric to optimize for this task. Possible values include:
+         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+         "PrecisionScoreWeighted".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+        """
+        super(ImageClassification, self).__init__(**kwargs)
+        self.limit_settings = kwargs['limit_settings']
+        self.sweep_settings = kwargs.get('sweep_settings', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.validation_data_size = kwargs.get('validation_data_size', None)
+        self.model_settings = kwargs.get('model_settings', None)
+        self.search_space = kwargs.get('search_space', None)
+        self.task_type = 'ImageClassification'  # type: str
+        self.primary_metric = kwargs.get('primary_metric', None)
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.training_data = kwargs['training_data']
+
+
+class ImageClassificationMultilabel(AutoMLVertical, ImageClassificationBase):
+    """Image Classification Multilabel. Multi-label image classification is used when an image could have one or more labels
+from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric to optimize for this task. Possible values include:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+     "PrecisionScoreWeighted", "IOU".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsClassification'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsClassification]'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric to optimize for this task. Possible values include:
+         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+         "PrecisionScoreWeighted", "IOU".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
+        """
+        super(ImageClassificationMultilabel, self).__init__(**kwargs)
+        self.limit_settings = kwargs['limit_settings']
+        self.sweep_settings = kwargs.get('sweep_settings', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.validation_data_size = kwargs.get('validation_data_size', None)
+        self.model_settings = kwargs.get('model_settings', None)
+        self.search_space = kwargs.get('search_space', None)
+        self.task_type = 'ImageClassificationMultilabel'  # type: str
+        self.primary_metric = kwargs.get('primary_metric', None)
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.training_data = kwargs['training_data']
+
+
+class ImageObjectDetectionBase(ImageVertical):
+    """ImageObjectDetectionBase.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsObjectDetection'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsObjectDetection]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+        """
+        super(ImageObjectDetectionBase, self).__init__(**kwargs)
+        self.model_settings = kwargs.get('model_settings', None)
+        self.search_space = kwargs.get('search_space', None)
+
+
+class ImageInstanceSegmentation(AutoMLVertical, ImageObjectDetectionBase):
+    """Image Instance Segmentation. Instance segmentation is used to identify objects in an image at the pixel level,
+drawing a polygon around each object in the image.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric to optimize for this task. Possible values include:
+     "MeanAveragePrecision".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.InstanceSegmentationPrimaryMetrics
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsObjectDetection'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsObjectDetection]'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric to optimize for this task. Possible values include:
+         "MeanAveragePrecision".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.InstanceSegmentationPrimaryMetrics
+        """
+        super(ImageInstanceSegmentation, self).__init__(**kwargs)
+        self.limit_settings = kwargs['limit_settings']
+        self.sweep_settings = kwargs.get('sweep_settings', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.validation_data_size = kwargs.get('validation_data_size', None)
+        self.model_settings = kwargs.get('model_settings', None)
+        self.search_space = kwargs.get('search_space', None)
+        self.task_type = 'ImageInstanceSegmentation'  # type: str
+        self.primary_metric = kwargs.get('primary_metric', None)
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.training_data = kwargs['training_data']
+
+
+class ImageLimitSettings(msrest.serialization.Model):
+    """Limit settings for the AutoML job.
+
+    :ivar max_concurrent_trials: Maximum number of concurrent AutoML iterations.
+    :vartype max_concurrent_trials: int
+    :ivar max_trials: Maximum number of AutoML iterations.
+    :vartype max_trials: int
+    :ivar timeout: AutoML job timeout.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
+        'max_trials': {'key': 'maxTrials', 'type': 'int'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword max_concurrent_trials: Maximum number of concurrent AutoML iterations.
+        :paramtype max_concurrent_trials: int
+        :keyword max_trials: Maximum number of AutoML iterations.
+        :paramtype max_trials: int
+        :keyword timeout: AutoML job timeout.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(ImageLimitSettings, self).__init__(**kwargs)
+        self.max_concurrent_trials = kwargs.get('max_concurrent_trials', 1)
+        self.max_trials = kwargs.get('max_trials', 1)
+        self.timeout = kwargs.get('timeout', "P7D")
+
+
+class ImageModelDistributionSettings(msrest.serialization.Model):
+    """Distribution expressions to sweep over values of model settings.
+
+:code:`<example>
+Some examples are:
+<code>
+ModelName = "choice('seresnext', 'resnest50')";
+LearningRate = "uniform(0.001, 0.01)";
+LayersToFreeze = "choice(0, 2)";
+</code></example>`
+All distributions can be specified as distribution_name(min, max) or choice(val1, val2, ..., valn)
+where distribution name can be: uniform, quniform, loguniform, etc
+For more details on how to compose distribution expressions please check the documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-tune-hyperparameters
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
+    """
+
+    _attribute_map = {
+        'ams_gradient': {'key': 'amsGradient', 'type': 'str'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'str'},
+        'beta2': {'key': 'beta2', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'str'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'str'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'str'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'str'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'str'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'str'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'str'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'str'},
+        'learning_rate': {'key': 'learningRate', 'type': 'str'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'str'},
+        'nesterov': {'key': 'nesterov', 'type': 'str'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'str'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'str'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'str'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'str'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'str'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'str'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'str'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'str'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'str'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: str
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: str
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: str
+        :keyword distributed: Whether to use distributer training.
+        :paramtype distributed: str
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: str
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: str
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: str
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: str
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: str
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: str
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: str
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: str
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'.
+        :paramtype learning_rate_scheduler: str
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: str
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: str
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: str
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: str
+        :keyword optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+        :paramtype optimizer: str
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: str
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: str
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: str
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: str
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: str
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: str
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: str
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: str
+        """
+        super(ImageModelDistributionSettings, self).__init__(**kwargs)
+        self.ams_gradient = kwargs.get('ams_gradient', None)
+        self.augmentations = kwargs.get('augmentations', None)
+        self.beta1 = kwargs.get('beta1', None)
+        self.beta2 = kwargs.get('beta2', None)
+        self.distributed = kwargs.get('distributed', None)
+        self.early_stopping = kwargs.get('early_stopping', None)
+        self.early_stopping_delay = kwargs.get('early_stopping_delay', None)
+        self.early_stopping_patience = kwargs.get('early_stopping_patience', None)
+        self.enable_onnx_normalization = kwargs.get('enable_onnx_normalization', None)
+        self.evaluation_frequency = kwargs.get('evaluation_frequency', None)
+        self.gradient_accumulation_step = kwargs.get('gradient_accumulation_step', None)
+        self.layers_to_freeze = kwargs.get('layers_to_freeze', None)
+        self.learning_rate = kwargs.get('learning_rate', None)
+        self.learning_rate_scheduler = kwargs.get('learning_rate_scheduler', None)
+        self.model_name = kwargs.get('model_name', None)
+        self.momentum = kwargs.get('momentum', None)
+        self.nesterov = kwargs.get('nesterov', None)
+        self.number_of_epochs = kwargs.get('number_of_epochs', None)
+        self.number_of_workers = kwargs.get('number_of_workers', None)
+        self.optimizer = kwargs.get('optimizer', None)
+        self.random_seed = kwargs.get('random_seed', None)
+        self.step_lr_gamma = kwargs.get('step_lr_gamma', None)
+        self.step_lr_step_size = kwargs.get('step_lr_step_size', None)
+        self.training_batch_size = kwargs.get('training_batch_size', None)
+        self.validation_batch_size = kwargs.get('validation_batch_size', None)
+        self.warmup_cosine_lr_cycles = kwargs.get('warmup_cosine_lr_cycles', None)
+        self.warmup_cosine_lr_warmup_epochs = kwargs.get('warmup_cosine_lr_warmup_epochs', None)
+        self.weight_decay = kwargs.get('weight_decay', None)
+
+
+class ImageModelDistributionSettingsClassification(ImageModelDistributionSettings):
+    """Distribution expressions to sweep over values of model settings.
+
+:code:`<example>
+Some examples are:
+<code>
+ModelName = "choice('seresnext', 'resnest50')";
+LearningRate = "uniform(0.001, 0.01)";
+LayersToFreeze = "choice(0, 2)";
+</code></example>`
+For more details on how to compose distribution expressions please check the documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-tune-hyperparameters
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
+    :ivar training_crop_size: Image crop size that is input to the neural network for the training
+     dataset. Must be a positive integer.
+    :vartype training_crop_size: str
+    :ivar validation_crop_size: Image crop size that is input to the neural network for the
+     validation dataset. Must be a positive integer.
+    :vartype validation_crop_size: str
+    :ivar validation_resize_size: Image size to which to resize before cropping for validation
+     dataset. Must be a positive integer.
+    :vartype validation_resize_size: str
+    :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+     1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+     0 or 1 or 2.
+    :vartype weighted_loss: str
+    """
+
+    _attribute_map = {
+        'ams_gradient': {'key': 'amsGradient', 'type': 'str'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'str'},
+        'beta2': {'key': 'beta2', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'str'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'str'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'str'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'str'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'str'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'str'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'str'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'str'},
+        'learning_rate': {'key': 'learningRate', 'type': 'str'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'str'},
+        'nesterov': {'key': 'nesterov', 'type': 'str'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'str'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'str'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'str'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'str'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'str'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'str'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'str'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'str'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'str'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'str'},
+        'training_crop_size': {'key': 'trainingCropSize', 'type': 'str'},
+        'validation_crop_size': {'key': 'validationCropSize', 'type': 'str'},
+        'validation_resize_size': {'key': 'validationResizeSize', 'type': 'str'},
+        'weighted_loss': {'key': 'weightedLoss', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: str
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: str
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: str
+        :keyword distributed: Whether to use distributer training.
+        :paramtype distributed: str
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: str
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: str
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: str
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: str
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: str
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: str
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: str
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: str
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'.
+        :paramtype learning_rate_scheduler: str
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: str
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: str
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: str
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: str
+        :keyword optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+        :paramtype optimizer: str
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: str
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: str
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: str
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: str
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: str
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: str
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: str
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: str
+        :keyword training_crop_size: Image crop size that is input to the neural network for the
+         training dataset. Must be a positive integer.
+        :paramtype training_crop_size: str
+        :keyword validation_crop_size: Image crop size that is input to the neural network for the
+         validation dataset. Must be a positive integer.
+        :paramtype validation_crop_size: str
+        :keyword validation_resize_size: Image size to which to resize before cropping for validation
+         dataset. Must be a positive integer.
+        :paramtype validation_resize_size: str
+        :keyword weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+         1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+         0 or 1 or 2.
+        :paramtype weighted_loss: str
+        """
+        super(ImageModelDistributionSettingsClassification, self).__init__(**kwargs)
+        self.training_crop_size = kwargs.get('training_crop_size', None)
+        self.validation_crop_size = kwargs.get('validation_crop_size', None)
+        self.validation_resize_size = kwargs.get('validation_resize_size', None)
+        self.weighted_loss = kwargs.get('weighted_loss', None)
+
+
+class ImageModelDistributionSettingsObjectDetection(ImageModelDistributionSettings):
+    """Distribution expressions to sweep over values of model settings.
+
+:code:`<example>
+Some examples are:
+<code>
+ModelName = "choice('seresnext', 'resnest50')";
+LearningRate = "uniform(0.001, 0.01)";
+LayersToFreeze = "choice(0, 2)";
+</code></example>`
+For more details on how to compose distribution expressions please check the documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-tune-hyperparameters
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
+    :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
+     be a positive integer.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype box_detections_per_image: str
+    :ivar box_score_threshold: During inference, only return proposals with a classification score
+     greater than
+     BoxScoreThreshold. Must be a float in the range[0, 1].
+    :vartype box_score_threshold: str
+    :ivar image_size: Image size for train and validation. Must be a positive integer.
+     Note: The training run may get into CUDA OOM if the size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype image_size: str
+    :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype max_size: str
+    :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype min_size: str
+    :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+     Note: training run may get into CUDA OOM if the model size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype model_size: str
+    :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+     Note: training run may get into CUDA OOM if no sufficient GPU memory.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype multi_scale: str
+    :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
+     float in the range [0, 1].
+    :vartype nms_iou_threshold: str
+    :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
+     be
+     None to enable small object detection logic. A string containing two integers in mxn format.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_grid_size: str
+    :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
+     in the range [0, 1).
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_overlap_ratio: str
+    :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+     predictions from tiles and image.
+     Used in validation/ inference. Must be float in the range [0, 1].
+     Note: This settings is not supported for the 'yolov5' algorithm.
+     NMS: Non-maximum suppression.
+    :vartype tile_predictions_nms_threshold: str
+    :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
+     float in the range [0, 1].
+    :vartype validation_iou_threshold: str
+    :ivar validation_metric_type: Metric computation method to use for validation metrics. Must be
+     'none', 'coco', 'voc', or 'coco_voc'.
+    :vartype validation_metric_type: str
+    """
+
+    _attribute_map = {
+        'ams_gradient': {'key': 'amsGradient', 'type': 'str'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'str'},
+        'beta2': {'key': 'beta2', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'str'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'str'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'str'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'str'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'str'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'str'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'str'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'str'},
+        'learning_rate': {'key': 'learningRate', 'type': 'str'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'str'},
+        'nesterov': {'key': 'nesterov', 'type': 'str'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'str'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'str'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'str'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'str'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'str'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'str'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'str'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'str'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'str'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'str'},
+        'box_detections_per_image': {'key': 'boxDetectionsPerImage', 'type': 'str'},
+        'box_score_threshold': {'key': 'boxScoreThreshold', 'type': 'str'},
+        'image_size': {'key': 'imageSize', 'type': 'str'},
+        'max_size': {'key': 'maxSize', 'type': 'str'},
+        'min_size': {'key': 'minSize', 'type': 'str'},
+        'model_size': {'key': 'modelSize', 'type': 'str'},
+        'multi_scale': {'key': 'multiScale', 'type': 'str'},
+        'nms_iou_threshold': {'key': 'nmsIouThreshold', 'type': 'str'},
+        'tile_grid_size': {'key': 'tileGridSize', 'type': 'str'},
+        'tile_overlap_ratio': {'key': 'tileOverlapRatio', 'type': 'str'},
+        'tile_predictions_nms_threshold': {'key': 'tilePredictionsNmsThreshold', 'type': 'str'},
+        'validation_iou_threshold': {'key': 'validationIouThreshold', 'type': 'str'},
+        'validation_metric_type': {'key': 'validationMetricType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: str
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: str
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: str
+        :keyword distributed: Whether to use distributer training.
+        :paramtype distributed: str
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: str
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: str
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: str
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: str
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: str
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: str
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: str
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: str
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'.
+        :paramtype learning_rate_scheduler: str
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: str
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: str
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: str
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: str
+        :keyword optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+        :paramtype optimizer: str
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: str
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: str
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: str
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: str
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: str
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: str
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: str
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: str
+        :keyword box_detections_per_image: Maximum number of detections per image, for all classes.
+         Must be a positive integer.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype box_detections_per_image: str
+        :keyword box_score_threshold: During inference, only return proposals with a classification
+         score greater than
+         BoxScoreThreshold. Must be a float in the range[0, 1].
+        :paramtype box_score_threshold: str
+        :keyword image_size: Image size for train and validation. Must be a positive integer.
+         Note: The training run may get into CUDA OOM if the size is too big.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :paramtype image_size: str
+        :keyword max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype max_size: str
+        :keyword min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype min_size: str
+        :keyword model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+         Note: training run may get into CUDA OOM if the model size is too big.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :paramtype model_size: str
+        :keyword multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+         Note: training run may get into CUDA OOM if no sufficient GPU memory.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :paramtype multi_scale: str
+        :keyword nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
+         float in the range [0, 1].
+        :paramtype nms_iou_threshold: str
+        :keyword tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must
+         not be
+         None to enable small object detection logic. A string containing two integers in mxn format.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype tile_grid_size: str
+        :keyword tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be
+         float in the range [0, 1).
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype tile_overlap_ratio: str
+        :keyword tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+         predictions from tiles and image.
+         Used in validation/ inference. Must be float in the range [0, 1].
+         Note: This settings is not supported for the 'yolov5' algorithm.
+         NMS: Non-maximum suppression.
+        :paramtype tile_predictions_nms_threshold: str
+        :keyword validation_iou_threshold: IOU threshold to use when computing validation metric. Must
+         be float in the range [0, 1].
+        :paramtype validation_iou_threshold: str
+        :keyword validation_metric_type: Metric computation method to use for validation metrics. Must
+         be 'none', 'coco', 'voc', or 'coco_voc'.
+        :paramtype validation_metric_type: str
+        """
+        super(ImageModelDistributionSettingsObjectDetection, self).__init__(**kwargs)
+        self.box_detections_per_image = kwargs.get('box_detections_per_image', None)
+        self.box_score_threshold = kwargs.get('box_score_threshold', None)
+        self.image_size = kwargs.get('image_size', None)
+        self.max_size = kwargs.get('max_size', None)
+        self.min_size = kwargs.get('min_size', None)
+        self.model_size = kwargs.get('model_size', None)
+        self.multi_scale = kwargs.get('multi_scale', None)
+        self.nms_iou_threshold = kwargs.get('nms_iou_threshold', None)
+        self.tile_grid_size = kwargs.get('tile_grid_size', None)
+        self.tile_overlap_ratio = kwargs.get('tile_overlap_ratio', None)
+        self.tile_predictions_nms_threshold = kwargs.get('tile_predictions_nms_threshold', None)
+        self.validation_iou_threshold = kwargs.get('validation_iou_threshold', None)
+        self.validation_metric_type = kwargs.get('validation_metric_type', None)
+
+
+class ImageModelSettings(msrest.serialization.Model):
+    """Settings used for training the model.
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Possible values include: "None", "WarmupCosine", "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
+    """
+
+    _attribute_map = {
+        'advanced_settings': {'key': 'advancedSettings', 'type': 'str'},
+        'ams_gradient': {'key': 'amsGradient', 'type': 'bool'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'float'},
+        'beta2': {'key': 'beta2', 'type': 'float'},
+        'checkpoint_frequency': {'key': 'checkpointFrequency', 'type': 'int'},
+        'checkpoint_model': {'key': 'checkpointModel', 'type': 'MLFlowModelJobInput'},
+        'checkpoint_run_id': {'key': 'checkpointRunId', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'bool'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'bool'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'int'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'int'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'bool'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'int'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'int'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'int'},
+        'learning_rate': {'key': 'learningRate', 'type': 'float'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'float'},
+        'nesterov': {'key': 'nesterov', 'type': 'bool'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'int'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'int'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'int'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'float'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'int'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'int'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'int'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'float'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'int'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'float'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword advanced_settings: Settings for advanced scenarios.
+        :paramtype advanced_settings: str
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: bool
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: float
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: float
+        :keyword checkpoint_frequency: Frequency to store model checkpoints. Must be a positive
+         integer.
+        :paramtype checkpoint_frequency: int
+        :keyword checkpoint_model: The pretrained checkpoint model for incremental training.
+        :paramtype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+        :keyword checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+         incremental training.
+        :paramtype checkpoint_run_id: str
+        :keyword distributed: Whether to use distributed training.
+        :paramtype distributed: bool
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: bool
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: int
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: int
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: bool
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: int
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: int
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: int
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: float
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'. Possible values include: "None", "WarmupCosine", "Step".
+        :paramtype learning_rate_scheduler: str or
+         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: float
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: bool
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: int
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: int
+        :keyword optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+        :paramtype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: int
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: float
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: int
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: int
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: int
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: float
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: int
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: float
+        """
+        super(ImageModelSettings, self).__init__(**kwargs)
+        self.advanced_settings = kwargs.get('advanced_settings', None)
+        self.ams_gradient = kwargs.get('ams_gradient', None)
+        self.augmentations = kwargs.get('augmentations', None)
+        self.beta1 = kwargs.get('beta1', None)
+        self.beta2 = kwargs.get('beta2', None)
+        self.checkpoint_frequency = kwargs.get('checkpoint_frequency', None)
+        self.checkpoint_model = kwargs.get('checkpoint_model', None)
+        self.checkpoint_run_id = kwargs.get('checkpoint_run_id', None)
+        self.distributed = kwargs.get('distributed', None)
+        self.early_stopping = kwargs.get('early_stopping', None)
+        self.early_stopping_delay = kwargs.get('early_stopping_delay', None)
+        self.early_stopping_patience = kwargs.get('early_stopping_patience', None)
+        self.enable_onnx_normalization = kwargs.get('enable_onnx_normalization', None)
+        self.evaluation_frequency = kwargs.get('evaluation_frequency', None)
+        self.gradient_accumulation_step = kwargs.get('gradient_accumulation_step', None)
+        self.layers_to_freeze = kwargs.get('layers_to_freeze', None)
+        self.learning_rate = kwargs.get('learning_rate', None)
+        self.learning_rate_scheduler = kwargs.get('learning_rate_scheduler', None)
+        self.model_name = kwargs.get('model_name', None)
+        self.momentum = kwargs.get('momentum', None)
+        self.nesterov = kwargs.get('nesterov', None)
+        self.number_of_epochs = kwargs.get('number_of_epochs', None)
+        self.number_of_workers = kwargs.get('number_of_workers', None)
+        self.optimizer = kwargs.get('optimizer', None)
+        self.random_seed = kwargs.get('random_seed', None)
+        self.step_lr_gamma = kwargs.get('step_lr_gamma', None)
+        self.step_lr_step_size = kwargs.get('step_lr_step_size', None)
+        self.training_batch_size = kwargs.get('training_batch_size', None)
+        self.validation_batch_size = kwargs.get('validation_batch_size', None)
+        self.warmup_cosine_lr_cycles = kwargs.get('warmup_cosine_lr_cycles', None)
+        self.warmup_cosine_lr_warmup_epochs = kwargs.get('warmup_cosine_lr_warmup_epochs', None)
+        self.weight_decay = kwargs.get('weight_decay', None)
+
+
+class ImageModelSettingsClassification(ImageModelSettings):
+    """Settings used for training the model.
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Possible values include: "None", "WarmupCosine", "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
+    :ivar training_crop_size: Image crop size that is input to the neural network for the training
+     dataset. Must be a positive integer.
+    :vartype training_crop_size: int
+    :ivar validation_crop_size: Image crop size that is input to the neural network for the
+     validation dataset. Must be a positive integer.
+    :vartype validation_crop_size: int
+    :ivar validation_resize_size: Image size to which to resize before cropping for validation
+     dataset. Must be a positive integer.
+    :vartype validation_resize_size: int
+    :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+     1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+     0 or 1 or 2.
+    :vartype weighted_loss: int
+    """
+
+    _attribute_map = {
+        'advanced_settings': {'key': 'advancedSettings', 'type': 'str'},
+        'ams_gradient': {'key': 'amsGradient', 'type': 'bool'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'float'},
+        'beta2': {'key': 'beta2', 'type': 'float'},
+        'checkpoint_frequency': {'key': 'checkpointFrequency', 'type': 'int'},
+        'checkpoint_model': {'key': 'checkpointModel', 'type': 'MLFlowModelJobInput'},
+        'checkpoint_run_id': {'key': 'checkpointRunId', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'bool'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'bool'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'int'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'int'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'bool'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'int'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'int'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'int'},
+        'learning_rate': {'key': 'learningRate', 'type': 'float'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'float'},
+        'nesterov': {'key': 'nesterov', 'type': 'bool'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'int'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'int'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'int'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'float'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'int'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'int'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'int'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'float'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'int'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'float'},
+        'training_crop_size': {'key': 'trainingCropSize', 'type': 'int'},
+        'validation_crop_size': {'key': 'validationCropSize', 'type': 'int'},
+        'validation_resize_size': {'key': 'validationResizeSize', 'type': 'int'},
+        'weighted_loss': {'key': 'weightedLoss', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword advanced_settings: Settings for advanced scenarios.
+        :paramtype advanced_settings: str
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: bool
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: float
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: float
+        :keyword checkpoint_frequency: Frequency to store model checkpoints. Must be a positive
+         integer.
+        :paramtype checkpoint_frequency: int
+        :keyword checkpoint_model: The pretrained checkpoint model for incremental training.
+        :paramtype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+        :keyword checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+         incremental training.
+        :paramtype checkpoint_run_id: str
+        :keyword distributed: Whether to use distributed training.
+        :paramtype distributed: bool
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: bool
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: int
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: int
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: bool
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: int
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: int
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: int
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: float
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'. Possible values include: "None", "WarmupCosine", "Step".
+        :paramtype learning_rate_scheduler: str or
+         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: float
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: bool
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: int
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: int
+        :keyword optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+        :paramtype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: int
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: float
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: int
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: int
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: int
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: float
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: int
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: float
+        :keyword training_crop_size: Image crop size that is input to the neural network for the
+         training dataset. Must be a positive integer.
+        :paramtype training_crop_size: int
+        :keyword validation_crop_size: Image crop size that is input to the neural network for the
+         validation dataset. Must be a positive integer.
+        :paramtype validation_crop_size: int
+        :keyword validation_resize_size: Image size to which to resize before cropping for validation
+         dataset. Must be a positive integer.
+        :paramtype validation_resize_size: int
+        :keyword weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+         1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+         0 or 1 or 2.
+        :paramtype weighted_loss: int
+        """
+        super(ImageModelSettingsClassification, self).__init__(**kwargs)
+        self.training_crop_size = kwargs.get('training_crop_size', None)
+        self.validation_crop_size = kwargs.get('validation_crop_size', None)
+        self.validation_resize_size = kwargs.get('validation_resize_size', None)
+        self.weighted_loss = kwargs.get('weighted_loss', None)
+
+
+class ImageModelSettingsObjectDetection(ImageModelSettings):
+    """Settings used for training the model.
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Possible values include: "None", "WarmupCosine", "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
+    :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
+     be a positive integer.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype box_detections_per_image: int
+    :ivar box_score_threshold: During inference, only return proposals with a classification score
+     greater than
+     BoxScoreThreshold. Must be a float in the range[0, 1].
+    :vartype box_score_threshold: float
+    :ivar image_size: Image size for train and validation. Must be a positive integer.
+     Note: The training run may get into CUDA OOM if the size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype image_size: int
+    :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype max_size: int
+    :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype min_size: int
+    :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+     Note: training run may get into CUDA OOM if the model size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm. Possible values include:
+     "None", "Small", "Medium", "Large", "ExtraLarge".
+    :vartype model_size: str or ~azure.mgmt.machinelearningservices.models.ModelSize
+    :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+     Note: training run may get into CUDA OOM if no sufficient GPU memory.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype multi_scale: bool
+    :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be a
+     float in the range [0, 1].
+    :vartype nms_iou_threshold: float
+    :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
+     be
+     None to enable small object detection logic. A string containing two integers in mxn format.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_grid_size: str
+    :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
+     in the range [0, 1).
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_overlap_ratio: float
+    :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+     predictions from tiles and image.
+     Used in validation/ inference. Must be float in the range [0, 1].
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_predictions_nms_threshold: float
+    :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
+     float in the range [0, 1].
+    :vartype validation_iou_threshold: float
+    :ivar validation_metric_type: Metric computation method to use for validation metrics. Possible
+     values include: "None", "Coco", "Voc", "CocoVoc".
+    :vartype validation_metric_type: str or
+     ~azure.mgmt.machinelearningservices.models.ValidationMetricType
+    """
+
+    _attribute_map = {
+        'advanced_settings': {'key': 'advancedSettings', 'type': 'str'},
+        'ams_gradient': {'key': 'amsGradient', 'type': 'bool'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'float'},
+        'beta2': {'key': 'beta2', 'type': 'float'},
+        'checkpoint_frequency': {'key': 'checkpointFrequency', 'type': 'int'},
+        'checkpoint_model': {'key': 'checkpointModel', 'type': 'MLFlowModelJobInput'},
+        'checkpoint_run_id': {'key': 'checkpointRunId', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'bool'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'bool'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'int'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'int'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'bool'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'int'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'int'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'int'},
+        'learning_rate': {'key': 'learningRate', 'type': 'float'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'float'},
+        'nesterov': {'key': 'nesterov', 'type': 'bool'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'int'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'int'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'int'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'float'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'int'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'int'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'int'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'float'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'int'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'float'},
+        'box_detections_per_image': {'key': 'boxDetectionsPerImage', 'type': 'int'},
+        'box_score_threshold': {'key': 'boxScoreThreshold', 'type': 'float'},
+        'image_size': {'key': 'imageSize', 'type': 'int'},
+        'max_size': {'key': 'maxSize', 'type': 'int'},
+        'min_size': {'key': 'minSize', 'type': 'int'},
+        'model_size': {'key': 'modelSize', 'type': 'str'},
+        'multi_scale': {'key': 'multiScale', 'type': 'bool'},
+        'nms_iou_threshold': {'key': 'nmsIouThreshold', 'type': 'float'},
+        'tile_grid_size': {'key': 'tileGridSize', 'type': 'str'},
+        'tile_overlap_ratio': {'key': 'tileOverlapRatio', 'type': 'float'},
+        'tile_predictions_nms_threshold': {'key': 'tilePredictionsNmsThreshold', 'type': 'float'},
+        'validation_iou_threshold': {'key': 'validationIouThreshold', 'type': 'float'},
+        'validation_metric_type': {'key': 'validationMetricType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword advanced_settings: Settings for advanced scenarios.
+        :paramtype advanced_settings: str
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: bool
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: float
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: float
+        :keyword checkpoint_frequency: Frequency to store model checkpoints. Must be a positive
+         integer.
+        :paramtype checkpoint_frequency: int
+        :keyword checkpoint_model: The pretrained checkpoint model for incremental training.
+        :paramtype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+        :keyword checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+         incremental training.
+        :paramtype checkpoint_run_id: str
+        :keyword distributed: Whether to use distributed training.
+        :paramtype distributed: bool
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: bool
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: int
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: int
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: bool
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: int
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: int
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: int
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: float
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'. Possible values include: "None", "WarmupCosine", "Step".
+        :paramtype learning_rate_scheduler: str or
+         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: float
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: bool
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: int
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: int
+        :keyword optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+        :paramtype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: int
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: float
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: int
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: int
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: int
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: float
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: int
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: float
+        :keyword box_detections_per_image: Maximum number of detections per image, for all classes.
+         Must be a positive integer.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype box_detections_per_image: int
+        :keyword box_score_threshold: During inference, only return proposals with a classification
+         score greater than
+         BoxScoreThreshold. Must be a float in the range[0, 1].
+        :paramtype box_score_threshold: float
+        :keyword image_size: Image size for train and validation. Must be a positive integer.
+         Note: The training run may get into CUDA OOM if the size is too big.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :paramtype image_size: int
+        :keyword max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype max_size: int
+        :keyword min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype min_size: int
+        :keyword model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+         Note: training run may get into CUDA OOM if the model size is too big.
+         Note: This settings is only supported for the 'yolov5' algorithm. Possible values include:
+         "None", "Small", "Medium", "Large", "ExtraLarge".
+        :paramtype model_size: str or ~azure.mgmt.machinelearningservices.models.ModelSize
+        :keyword multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+         Note: training run may get into CUDA OOM if no sufficient GPU memory.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :paramtype multi_scale: bool
+        :keyword nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
+         a float in the range [0, 1].
+        :paramtype nms_iou_threshold: float
+        :keyword tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must
+         not be
+         None to enable small object detection logic. A string containing two integers in mxn format.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype tile_grid_size: str
+        :keyword tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be
+         float in the range [0, 1).
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype tile_overlap_ratio: float
+        :keyword tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+         predictions from tiles and image.
+         Used in validation/ inference. Must be float in the range [0, 1].
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype tile_predictions_nms_threshold: float
+        :keyword validation_iou_threshold: IOU threshold to use when computing validation metric. Must
+         be float in the range [0, 1].
+        :paramtype validation_iou_threshold: float
+        :keyword validation_metric_type: Metric computation method to use for validation metrics.
+         Possible values include: "None", "Coco", "Voc", "CocoVoc".
+        :paramtype validation_metric_type: str or
+         ~azure.mgmt.machinelearningservices.models.ValidationMetricType
+        """
+        super(ImageModelSettingsObjectDetection, self).__init__(**kwargs)
+        self.box_detections_per_image = kwargs.get('box_detections_per_image', None)
+        self.box_score_threshold = kwargs.get('box_score_threshold', None)
+        self.image_size = kwargs.get('image_size', None)
+        self.max_size = kwargs.get('max_size', None)
+        self.min_size = kwargs.get('min_size', None)
+        self.model_size = kwargs.get('model_size', None)
+        self.multi_scale = kwargs.get('multi_scale', None)
+        self.nms_iou_threshold = kwargs.get('nms_iou_threshold', None)
+        self.tile_grid_size = kwargs.get('tile_grid_size', None)
+        self.tile_overlap_ratio = kwargs.get('tile_overlap_ratio', None)
+        self.tile_predictions_nms_threshold = kwargs.get('tile_predictions_nms_threshold', None)
+        self.validation_iou_threshold = kwargs.get('validation_iou_threshold', None)
+        self.validation_metric_type = kwargs.get('validation_metric_type', None)
+
+
+class ImageObjectDetection(AutoMLVertical, ImageObjectDetectionBase):
+    """Image Object Detection. Object detection is used to identify objects in an image and locate each object with a
+bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric to optimize for this task. Possible values include:
+     "MeanAveragePrecision".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ObjectDetectionPrimaryMetrics
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsObjectDetection'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsObjectDetection]'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric to optimize for this task. Possible values include:
+         "MeanAveragePrecision".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ObjectDetectionPrimaryMetrics
+        """
+        super(ImageObjectDetection, self).__init__(**kwargs)
+        self.limit_settings = kwargs['limit_settings']
+        self.sweep_settings = kwargs.get('sweep_settings', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.validation_data_size = kwargs.get('validation_data_size', None)
+        self.model_settings = kwargs.get('model_settings', None)
+        self.search_space = kwargs.get('search_space', None)
+        self.task_type = 'ImageObjectDetection'  # type: str
+        self.primary_metric = kwargs.get('primary_metric', None)
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.training_data = kwargs['training_data']
+
+
+class ImageSweepSettings(msrest.serialization.Model):
+    """Model sweeping and hyperparameter sweeping related settings.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar early_termination: Type of early termination policy.
+    :vartype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+    :ivar sampling_algorithm: Required. [Required] Type of the hyperparameter sampling algorithms.
+     Possible values include: "Grid", "Random", "Bayesian".
+    :vartype sampling_algorithm: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    """
+
+    _validation = {
+        'sampling_algorithm': {'required': True},
+    }
+
+    _attribute_map = {
+        'early_termination': {'key': 'earlyTermination', 'type': 'EarlyTerminationPolicy'},
+        'sampling_algorithm': {'key': 'samplingAlgorithm', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword early_termination: Type of early termination policy.
+        :paramtype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+        :keyword sampling_algorithm: Required. [Required] Type of the hyperparameter sampling
+         algorithms. Possible values include: "Grid", "Random", "Bayesian".
+        :paramtype sampling_algorithm: str or
+         ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+        """
+        super(ImageSweepSettings, self).__init__(**kwargs)
+        self.early_termination = kwargs.get('early_termination', None)
+        self.sampling_algorithm = kwargs['sampling_algorithm']
+
+
+class InferenceContainerProperties(msrest.serialization.Model):
+    """InferenceContainerProperties.
+
+    :ivar liveness_route: The route to check the liveness of the inference server container.
+    :vartype liveness_route: ~azure.mgmt.machinelearningservices.models.Route
+    :ivar readiness_route: The route to check the readiness of the inference server container.
+    :vartype readiness_route: ~azure.mgmt.machinelearningservices.models.Route
+    :ivar scoring_route: The port to send the scoring requests to, within the inference server
+     container.
+    :vartype scoring_route: ~azure.mgmt.machinelearningservices.models.Route
+    """
+
+    _attribute_map = {
+        'liveness_route': {'key': 'livenessRoute', 'type': 'Route'},
+        'readiness_route': {'key': 'readinessRoute', 'type': 'Route'},
+        'scoring_route': {'key': 'scoringRoute', 'type': 'Route'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword liveness_route: The route to check the liveness of the inference server container.
+        :paramtype liveness_route: ~azure.mgmt.machinelearningservices.models.Route
+        :keyword readiness_route: The route to check the readiness of the inference server container.
+        :paramtype readiness_route: ~azure.mgmt.machinelearningservices.models.Route
+        :keyword scoring_route: The port to send the scoring requests to, within the inference server
+         container.
+        :paramtype scoring_route: ~azure.mgmt.machinelearningservices.models.Route
+        """
+        super(InferenceContainerProperties, self).__init__(**kwargs)
+        self.liveness_route = kwargs.get('liveness_route', None)
+        self.readiness_route = kwargs.get('readiness_route', None)
+        self.scoring_route = kwargs.get('scoring_route', None)
+
+
+class InstanceTypeSchema(msrest.serialization.Model):
+    """Instance type schema.
+
+    :ivar node_selector: Node Selector.
+    :vartype node_selector: dict[str, str]
+    :ivar resources: Resource requests/limits for this instance type.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.InstanceTypeSchemaResources
+    """
+
+    _attribute_map = {
+        'node_selector': {'key': 'nodeSelector', 'type': '{str}'},
+        'resources': {'key': 'resources', 'type': 'InstanceTypeSchemaResources'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword node_selector: Node Selector.
+        :paramtype node_selector: dict[str, str]
+        :keyword resources: Resource requests/limits for this instance type.
+        :paramtype resources: ~azure.mgmt.machinelearningservices.models.InstanceTypeSchemaResources
+        """
+        super(InstanceTypeSchema, self).__init__(**kwargs)
+        self.node_selector = kwargs.get('node_selector', None)
+        self.resources = kwargs.get('resources', None)
+
+
+class InstanceTypeSchemaResources(msrest.serialization.Model):
+    """Resource requests/limits for this instance type.
+
+    :ivar requests: Resource requests for this instance type.
+    :vartype requests: dict[str, str]
+    :ivar limits: Resource limits for this instance type.
+    :vartype limits: dict[str, str]
+    """
+
+    _attribute_map = {
+        'requests': {'key': 'requests', 'type': '{str}'},
+        'limits': {'key': 'limits', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword requests: Resource requests for this instance type.
+        :paramtype requests: dict[str, str]
+        :keyword limits: Resource limits for this instance type.
+        :paramtype limits: dict[str, str]
+        """
+        super(InstanceTypeSchemaResources, self).__init__(**kwargs)
+        self.requests = kwargs.get('requests', None)
+        self.limits = kwargs.get('limits', None)
+
+
+class JobBase(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'JobBaseProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
+        """
+        super(JobBase, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class JobBaseResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of JobBase entities.
+
+    :ivar next_link: The link to the next page of JobBase objects. If null, there are no additional
+     pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type JobBase.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.JobBase]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[JobBase]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of JobBase objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type JobBase.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.JobBase]
+        """
+        super(JobBaseResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class JobResourceConfiguration(ResourceConfiguration):
+    """JobResourceConfiguration.
+
+    :ivar instance_count: Optional number of instances or nodes used by the compute target.
+    :vartype instance_count: int
+    :ivar instance_type: Optional type of VM used as supported by the compute target.
+    :vartype instance_type: str
+    :ivar properties: Additional properties bag.
+    :vartype properties: dict[str, any]
+    :ivar docker_args: Extra arguments to pass to the Docker run command. This would override any
+     parameters that have already been set by the system, or in this section. This parameter is only
+     supported for Azure ML compute types.
+    :vartype docker_args: str
+    :ivar shm_size: Size of the docker container's shared memory block. This should be in the
+     format of (number)(unit) where number as to be greater than 0 and the unit can be one of
+     b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
+    :vartype shm_size: str
+    """
+
+    _validation = {
+        'shm_size': {'pattern': r'\d+[bBkKmMgG]'},
+    }
+
+    _attribute_map = {
+        'instance_count': {'key': 'instanceCount', 'type': 'int'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{object}'},
+        'docker_args': {'key': 'dockerArgs', 'type': 'str'},
+        'shm_size': {'key': 'shmSize', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword instance_count: Optional number of instances or nodes used by the compute target.
+        :paramtype instance_count: int
+        :keyword instance_type: Optional type of VM used as supported by the compute target.
+        :paramtype instance_type: str
+        :keyword properties: Additional properties bag.
+        :paramtype properties: dict[str, any]
+        :keyword docker_args: Extra arguments to pass to the Docker run command. This would override
+         any parameters that have already been set by the system, or in this section. This parameter is
+         only supported for Azure ML compute types.
+        :paramtype docker_args: str
+        :keyword shm_size: Size of the docker container's shared memory block. This should be in the
+         format of (number)(unit) where number as to be greater than 0 and the unit can be one of
+         b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
+        :paramtype shm_size: str
+        """
+        super(JobResourceConfiguration, self).__init__(**kwargs)
+        self.docker_args = kwargs.get('docker_args', None)
+        self.shm_size = kwargs.get('shm_size', "2g")
+
+
+class JobScheduleAction(ScheduleActionBase):
+    """JobScheduleAction.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar action_type: Required. [Required] Specifies the action type of the schedule.Constant
+     filled by server. Possible values include: "CreateJob", "InvokeBatchEndpoint".
+    :vartype action_type: str or ~azure.mgmt.machinelearningservices.models.ScheduleActionType
+    :ivar job_definition: Required. [Required] Defines Schedule action definition details.
+    :vartype job_definition: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
+    """
+
+    _validation = {
+        'action_type': {'required': True},
+        'job_definition': {'required': True},
+    }
+
+    _attribute_map = {
+        'action_type': {'key': 'actionType', 'type': 'str'},
+        'job_definition': {'key': 'jobDefinition', 'type': 'JobBaseProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword job_definition: Required. [Required] Defines Schedule action definition details.
+        :paramtype job_definition: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
+        """
+        super(JobScheduleAction, self).__init__(**kwargs)
+        self.action_type = 'CreateJob'  # type: str
+        self.job_definition = kwargs['job_definition']
+
+
+class JobService(msrest.serialization.Model):
+    """Job endpoint definition.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar endpoint: Url for endpoint.
+    :vartype endpoint: str
+    :ivar error_message: Any error in the service.
+    :vartype error_message: str
+    :ivar job_service_type: Endpoint type.
+    :vartype job_service_type: str
+    :ivar port: Port for endpoint.
+    :vartype port: int
+    :ivar properties: Additional properties to set on the endpoint.
+    :vartype properties: dict[str, str]
+    :ivar status: Status of endpoint.
+    :vartype status: str
+    """
+
+    _validation = {
+        'error_message': {'readonly': True},
+        'status': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'endpoint': {'key': 'endpoint', 'type': 'str'},
+        'error_message': {'key': 'errorMessage', 'type': 'str'},
+        'job_service_type': {'key': 'jobServiceType', 'type': 'str'},
+        'port': {'key': 'port', 'type': 'int'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'status': {'key': 'status', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword endpoint: Url for endpoint.
+        :paramtype endpoint: str
+        :keyword job_service_type: Endpoint type.
+        :paramtype job_service_type: str
+        :keyword port: Port for endpoint.
+        :paramtype port: int
+        :keyword properties: Additional properties to set on the endpoint.
+        :paramtype properties: dict[str, str]
+        """
+        super(JobService, self).__init__(**kwargs)
+        self.endpoint = kwargs.get('endpoint', None)
+        self.error_message = None
+        self.job_service_type = kwargs.get('job_service_type', None)
+        self.port = kwargs.get('port', None)
+        self.properties = kwargs.get('properties', None)
+        self.status = None
+
+
+class KubernetesSchema(msrest.serialization.Model):
+    """Kubernetes Compute Schema.
+
+    :ivar properties: Properties of Kubernetes.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'KubernetesProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of Kubernetes.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+        """
+        super(KubernetesSchema, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class Kubernetes(Compute, KubernetesSchema):
+    """A Machine Learning compute based on Kubernetes Compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: Properties of Kubernetes.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'KubernetesProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of Kubernetes.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(Kubernetes, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+        self.compute_type = 'Kubernetes'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = kwargs.get('description', None)
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = kwargs.get('resource_id', None)
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class OnlineDeploymentProperties(EndpointDeploymentPropertiesBase):
+    """OnlineDeploymentProperties.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: KubernetesOnlineDeployment, ManagedOnlineDeployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar code_configuration: Code configuration for the endpoint deployment.
+    :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
+     endpoint deployment.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables configuration for the deployment.
+    :vartype environment_variables: dict[str, str]
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar app_insights_enabled: If true, enables Application Insights logging.
+    :vartype app_insights_enabled: bool
+    :ivar egress_public_network_access: If Enabled, allow egress public network access. If
+     Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+     "Disabled".
+    :vartype egress_public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+    :ivar endpoint_compute_type: Required. [Required] The compute type of the endpoint.Constant
+     filled by server. Possible values include: "Managed", "Kubernetes", "AzureMLCompute".
+    :vartype endpoint_compute_type: str or
+     ~azure.mgmt.machinelearningservices.models.EndpointComputeType
+    :ivar instance_type: Compute instance type.
+    :vartype instance_type: str
+    :ivar liveness_probe: Liveness probe monitors the health of the container regularly.
+    :vartype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar model: The URI path to the model.
+    :vartype model: str
+    :ivar model_mount_path: The path to mount the model in custom container.
+    :vartype model_mount_path: str
+    :ivar provisioning_state: Provisioning state for the endpoint deployment. Possible values
+     include: "Creating", "Deleting", "Scaling", "Updating", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.DeploymentProvisioningState
+    :ivar readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+     The properties and defaults are the same as liveness probe.
+    :vartype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar request_settings: Request settings for the deployment.
+    :vartype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+    :ivar scale_settings: Scale settings for the deployment.
+     If it is null or not provided,
+     it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+     and to DefaultScaleSettings for ManagedOnlineDeployment.
+    :vartype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+    """
+
+    _validation = {
+        'endpoint_compute_type': {'required': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code_configuration': {'key': 'codeConfiguration', 'type': 'CodeConfiguration'},
+        'description': {'key': 'description', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
+        'egress_public_network_access': {'key': 'egressPublicNetworkAccess', 'type': 'str'},
+        'endpoint_compute_type': {'key': 'endpointComputeType', 'type': 'str'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'liveness_probe': {'key': 'livenessProbe', 'type': 'ProbeSettings'},
+        'model': {'key': 'model', 'type': 'str'},
+        'model_mount_path': {'key': 'modelMountPath', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'readiness_probe': {'key': 'readinessProbe', 'type': 'ProbeSettings'},
+        'request_settings': {'key': 'requestSettings', 'type': 'OnlineRequestSettings'},
+        'scale_settings': {'key': 'scaleSettings', 'type': 'OnlineScaleSettings'},
+    }
+
+    _subtype_map = {
+        'endpoint_compute_type': {'Kubernetes': 'KubernetesOnlineDeployment', 'Managed': 'ManagedOnlineDeployment'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword code_configuration: Code configuration for the endpoint deployment.
+        :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
+         endpoint deployment.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables configuration for the deployment.
+        :paramtype environment_variables: dict[str, str]
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword app_insights_enabled: If true, enables Application Insights logging.
+        :paramtype app_insights_enabled: bool
+        :keyword egress_public_network_access: If Enabled, allow egress public network access. If
+         Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+         "Disabled".
+        :paramtype egress_public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+        :keyword instance_type: Compute instance type.
+        :paramtype instance_type: str
+        :keyword liveness_probe: Liveness probe monitors the health of the container regularly.
+        :paramtype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword model: The URI path to the model.
+        :paramtype model: str
+        :keyword model_mount_path: The path to mount the model in custom container.
+        :paramtype model_mount_path: str
+        :keyword readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+         The properties and defaults are the same as liveness probe.
+        :paramtype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword request_settings: Request settings for the deployment.
+        :paramtype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+        :keyword scale_settings: Scale settings for the deployment.
+         If it is null or not provided,
+         it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+         and to DefaultScaleSettings for ManagedOnlineDeployment.
+        :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+        """
+        super(OnlineDeploymentProperties, self).__init__(**kwargs)
+        self.app_insights_enabled = kwargs.get('app_insights_enabled', False)
+        self.egress_public_network_access = kwargs.get('egress_public_network_access', None)
+        self.endpoint_compute_type = 'OnlineDeploymentProperties'  # type: str
+        self.instance_type = kwargs.get('instance_type', None)
+        self.liveness_probe = kwargs.get('liveness_probe', None)
+        self.model = kwargs.get('model', None)
+        self.model_mount_path = kwargs.get('model_mount_path', None)
+        self.provisioning_state = None
+        self.readiness_probe = kwargs.get('readiness_probe', None)
+        self.request_settings = kwargs.get('request_settings', None)
+        self.scale_settings = kwargs.get('scale_settings', None)
+
+
+class KubernetesOnlineDeployment(OnlineDeploymentProperties):
+    """Properties specific to a KubernetesOnlineDeployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar code_configuration: Code configuration for the endpoint deployment.
+    :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
+     endpoint deployment.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables configuration for the deployment.
+    :vartype environment_variables: dict[str, str]
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar app_insights_enabled: If true, enables Application Insights logging.
+    :vartype app_insights_enabled: bool
+    :ivar egress_public_network_access: If Enabled, allow egress public network access. If
+     Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+     "Disabled".
+    :vartype egress_public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+    :ivar endpoint_compute_type: Required. [Required] The compute type of the endpoint.Constant
+     filled by server. Possible values include: "Managed", "Kubernetes", "AzureMLCompute".
+    :vartype endpoint_compute_type: str or
+     ~azure.mgmt.machinelearningservices.models.EndpointComputeType
+    :ivar instance_type: Compute instance type.
+    :vartype instance_type: str
+    :ivar liveness_probe: Liveness probe monitors the health of the container regularly.
+    :vartype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar model: The URI path to the model.
+    :vartype model: str
+    :ivar model_mount_path: The path to mount the model in custom container.
+    :vartype model_mount_path: str
+    :ivar provisioning_state: Provisioning state for the endpoint deployment. Possible values
+     include: "Creating", "Deleting", "Scaling", "Updating", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.DeploymentProvisioningState
+    :ivar readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+     The properties and defaults are the same as liveness probe.
+    :vartype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar request_settings: Request settings for the deployment.
+    :vartype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+    :ivar scale_settings: Scale settings for the deployment.
+     If it is null or not provided,
+     it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+     and to DefaultScaleSettings for ManagedOnlineDeployment.
+    :vartype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+    :ivar container_resource_requirements: The resource requirements for the container (cpu and
+     memory).
+    :vartype container_resource_requirements:
+     ~azure.mgmt.machinelearningservices.models.ContainerResourceRequirements
+    """
+
+    _validation = {
+        'endpoint_compute_type': {'required': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code_configuration': {'key': 'codeConfiguration', 'type': 'CodeConfiguration'},
+        'description': {'key': 'description', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
+        'egress_public_network_access': {'key': 'egressPublicNetworkAccess', 'type': 'str'},
+        'endpoint_compute_type': {'key': 'endpointComputeType', 'type': 'str'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'liveness_probe': {'key': 'livenessProbe', 'type': 'ProbeSettings'},
+        'model': {'key': 'model', 'type': 'str'},
+        'model_mount_path': {'key': 'modelMountPath', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'readiness_probe': {'key': 'readinessProbe', 'type': 'ProbeSettings'},
+        'request_settings': {'key': 'requestSettings', 'type': 'OnlineRequestSettings'},
+        'scale_settings': {'key': 'scaleSettings', 'type': 'OnlineScaleSettings'},
+        'container_resource_requirements': {'key': 'containerResourceRequirements', 'type': 'ContainerResourceRequirements'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword code_configuration: Code configuration for the endpoint deployment.
+        :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
+         endpoint deployment.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables configuration for the deployment.
+        :paramtype environment_variables: dict[str, str]
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword app_insights_enabled: If true, enables Application Insights logging.
+        :paramtype app_insights_enabled: bool
+        :keyword egress_public_network_access: If Enabled, allow egress public network access. If
+         Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+         "Disabled".
+        :paramtype egress_public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+        :keyword instance_type: Compute instance type.
+        :paramtype instance_type: str
+        :keyword liveness_probe: Liveness probe monitors the health of the container regularly.
+        :paramtype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword model: The URI path to the model.
+        :paramtype model: str
+        :keyword model_mount_path: The path to mount the model in custom container.
+        :paramtype model_mount_path: str
+        :keyword readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+         The properties and defaults are the same as liveness probe.
+        :paramtype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword request_settings: Request settings for the deployment.
+        :paramtype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+        :keyword scale_settings: Scale settings for the deployment.
+         If it is null or not provided,
+         it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+         and to DefaultScaleSettings for ManagedOnlineDeployment.
+        :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+        :keyword container_resource_requirements: The resource requirements for the container (cpu and
+         memory).
+        :paramtype container_resource_requirements:
+         ~azure.mgmt.machinelearningservices.models.ContainerResourceRequirements
+        """
+        super(KubernetesOnlineDeployment, self).__init__(**kwargs)
+        self.endpoint_compute_type = 'Kubernetes'  # type: str
+        self.container_resource_requirements = kwargs.get('container_resource_requirements', None)
+
+
+class KubernetesProperties(msrest.serialization.Model):
+    """Kubernetes properties.
+
+    :ivar relay_connection_string: Relay connection string.
+    :vartype relay_connection_string: str
+    :ivar service_bus_connection_string: ServiceBus connection string.
+    :vartype service_bus_connection_string: str
+    :ivar extension_principal_id: Extension principal-id.
+    :vartype extension_principal_id: str
+    :ivar extension_instance_release_train: Extension instance release train.
+    :vartype extension_instance_release_train: str
+    :ivar vc_name: VC name.
+    :vartype vc_name: str
+    :ivar namespace: Compute namespace.
+    :vartype namespace: str
+    :ivar default_instance_type: Default instance type.
+    :vartype default_instance_type: str
+    :ivar instance_types: Instance Type Schema.
+    :vartype instance_types: dict[str,
+     ~azure.mgmt.machinelearningservices.models.InstanceTypeSchema]
+    """
+
+    _attribute_map = {
+        'relay_connection_string': {'key': 'relayConnectionString', 'type': 'str'},
+        'service_bus_connection_string': {'key': 'serviceBusConnectionString', 'type': 'str'},
+        'extension_principal_id': {'key': 'extensionPrincipalId', 'type': 'str'},
+        'extension_instance_release_train': {'key': 'extensionInstanceReleaseTrain', 'type': 'str'},
+        'vc_name': {'key': 'vcName', 'type': 'str'},
+        'namespace': {'key': 'namespace', 'type': 'str'},
+        'default_instance_type': {'key': 'defaultInstanceType', 'type': 'str'},
+        'instance_types': {'key': 'instanceTypes', 'type': '{InstanceTypeSchema}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword relay_connection_string: Relay connection string.
+        :paramtype relay_connection_string: str
+        :keyword service_bus_connection_string: ServiceBus connection string.
+        :paramtype service_bus_connection_string: str
+        :keyword extension_principal_id: Extension principal-id.
+        :paramtype extension_principal_id: str
+        :keyword extension_instance_release_train: Extension instance release train.
+        :paramtype extension_instance_release_train: str
+        :keyword vc_name: VC name.
+        :paramtype vc_name: str
+        :keyword namespace: Compute namespace.
+        :paramtype namespace: str
+        :keyword default_instance_type: Default instance type.
+        :paramtype default_instance_type: str
+        :keyword instance_types: Instance Type Schema.
+        :paramtype instance_types: dict[str,
+         ~azure.mgmt.machinelearningservices.models.InstanceTypeSchema]
+        """
+        super(KubernetesProperties, self).__init__(**kwargs)
+        self.relay_connection_string = kwargs.get('relay_connection_string', None)
+        self.service_bus_connection_string = kwargs.get('service_bus_connection_string', None)
+        self.extension_principal_id = kwargs.get('extension_principal_id', None)
+        self.extension_instance_release_train = kwargs.get('extension_instance_release_train', None)
+        self.vc_name = kwargs.get('vc_name', None)
+        self.namespace = kwargs.get('namespace', "default")
+        self.default_instance_type = kwargs.get('default_instance_type', None)
+        self.instance_types = kwargs.get('instance_types', None)
+
+
+class ListAmlUserFeatureResult(msrest.serialization.Model):
+    """The List Aml user feature operation response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of AML user facing features.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.AmlUserFeature]
+    :ivar next_link: The URI to fetch the next page of AML user features information. Call
+     ListNext() with this to fetch the next page of AML user features information.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[AmlUserFeature]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListAmlUserFeatureResult, self).__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class ListNotebookKeysResult(msrest.serialization.Model):
+    """ListNotebookKeysResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar primary_access_key:
+    :vartype primary_access_key: str
+    :ivar secondary_access_key:
+    :vartype secondary_access_key: str
+    """
+
+    _validation = {
+        'primary_access_key': {'readonly': True},
+        'secondary_access_key': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'primary_access_key': {'key': 'primaryAccessKey', 'type': 'str'},
+        'secondary_access_key': {'key': 'secondaryAccessKey', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListNotebookKeysResult, self).__init__(**kwargs)
+        self.primary_access_key = None
+        self.secondary_access_key = None
+
+
+class ListStorageAccountKeysResult(msrest.serialization.Model):
+    """ListStorageAccountKeysResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar user_storage_key:
+    :vartype user_storage_key: str
+    """
+
+    _validation = {
+        'user_storage_key': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'user_storage_key': {'key': 'userStorageKey', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListStorageAccountKeysResult, self).__init__(**kwargs)
+        self.user_storage_key = None
+
+
+class ListUsagesResult(msrest.serialization.Model):
+    """The List Usages operation response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of AML resource usages.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Usage]
+    :ivar next_link: The URI to fetch the next page of AML resource usage information. Call
+     ListNext() with this to fetch the next page of AML resource usage information.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[Usage]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListUsagesResult, self).__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class ListWorkspaceKeysResult(msrest.serialization.Model):
+    """ListWorkspaceKeysResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar user_storage_key:
+    :vartype user_storage_key: str
+    :ivar user_storage_resource_id:
+    :vartype user_storage_resource_id: str
+    :ivar app_insights_instrumentation_key:
+    :vartype app_insights_instrumentation_key: str
+    :ivar container_registry_credentials:
+    :vartype container_registry_credentials:
+     ~azure.mgmt.machinelearningservices.models.RegistryListCredentialsResult
+    :ivar notebook_access_keys:
+    :vartype notebook_access_keys:
+     ~azure.mgmt.machinelearningservices.models.ListNotebookKeysResult
+    """
+
+    _validation = {
+        'user_storage_key': {'readonly': True},
+        'user_storage_resource_id': {'readonly': True},
+        'app_insights_instrumentation_key': {'readonly': True},
+        'container_registry_credentials': {'readonly': True},
+        'notebook_access_keys': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'user_storage_key': {'key': 'userStorageKey', 'type': 'str'},
+        'user_storage_resource_id': {'key': 'userStorageResourceId', 'type': 'str'},
+        'app_insights_instrumentation_key': {'key': 'appInsightsInstrumentationKey', 'type': 'str'},
+        'container_registry_credentials': {'key': 'containerRegistryCredentials', 'type': 'RegistryListCredentialsResult'},
+        'notebook_access_keys': {'key': 'notebookAccessKeys', 'type': 'ListNotebookKeysResult'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListWorkspaceKeysResult, self).__init__(**kwargs)
+        self.user_storage_key = None
+        self.user_storage_resource_id = None
+        self.app_insights_instrumentation_key = None
+        self.container_registry_credentials = None
+        self.notebook_access_keys = None
+
+
+class ListWorkspaceQuotas(msrest.serialization.Model):
+    """The List WorkspaceQuotasByVMFamily operation response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of Workspace Quotas by VM Family.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ResourceQuota]
+    :ivar next_link: The URI to fetch the next page of workspace quota information by VM Family.
+     Call ListNext() with this to fetch the next page of Workspace Quota information.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[ResourceQuota]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListWorkspaceQuotas, self).__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class LiteralJobInput(JobInput):
+    """Literal input type.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    :ivar value: Required. [Required] Literal value for the input.
+    :vartype value: str
+    """
+
+    _validation = {
+        'job_input_type': {'required': True},
+        'value': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: Description for the input.
+        :paramtype description: str
+        :keyword value: Required. [Required] Literal value for the input.
+        :paramtype value: str
+        """
+        super(LiteralJobInput, self).__init__(**kwargs)
+        self.job_input_type = 'literal'  # type: str
+        self.value = kwargs['value']
+
+
+class ManagedIdentity(IdentityConfiguration):
+    """Managed identity configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar identity_type: Required. [Required] Specifies the type of identity framework.Constant
+     filled by server. Possible values include: "Managed", "AMLToken", "UserIdentity".
+    :vartype identity_type: str or
+     ~azure.mgmt.machinelearningservices.models.IdentityConfigurationType
+    :ivar client_id: Specifies a user-assigned identity by client ID. For system-assigned, do not
+     set this field.
+    :vartype client_id: str
+    :ivar object_id: Specifies a user-assigned identity by object ID. For system-assigned, do not
+     set this field.
+    :vartype object_id: str
+    :ivar resource_id: Specifies a user-assigned identity by ARM resource ID. For system-assigned,
+     do not set this field.
+    :vartype resource_id: str
+    """
+
+    _validation = {
+        'identity_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'identity_type': {'key': 'identityType', 'type': 'str'},
+        'client_id': {'key': 'clientId', 'type': 'str'},
+        'object_id': {'key': 'objectId', 'type': 'str'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword client_id: Specifies a user-assigned identity by client ID. For system-assigned, do
+         not set this field.
+        :paramtype client_id: str
+        :keyword object_id: Specifies a user-assigned identity by object ID. For system-assigned, do
+         not set this field.
+        :paramtype object_id: str
+        :keyword resource_id: Specifies a user-assigned identity by ARM resource ID. For
+         system-assigned, do not set this field.
+        :paramtype resource_id: str
+        """
+        super(ManagedIdentity, self).__init__(**kwargs)
+        self.identity_type = 'Managed'  # type: str
+        self.client_id = kwargs.get('client_id', None)
+        self.object_id = kwargs.get('object_id', None)
+        self.resource_id = kwargs.get('resource_id', None)
+
+
+class WorkspaceConnectionPropertiesV2(msrest.serialization.Model):
+    """WorkspaceConnectionPropertiesV2.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: ManagedIdentityAuthTypeWorkspaceConnectionProperties, NoneAuthTypeWorkspaceConnectionProperties, PATAuthTypeWorkspaceConnectionProperties, SASAuthTypeWorkspaceConnectionProperties, UsernamePasswordAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'auth_type': {'ManagedIdentity': 'ManagedIdentityAuthTypeWorkspaceConnectionProperties', 'None': 'NoneAuthTypeWorkspaceConnectionProperties', 'PAT': 'PATAuthTypeWorkspaceConnectionProperties', 'SAS': 'SASAuthTypeWorkspaceConnectionProperties', 'UsernamePassword': 'UsernamePasswordAuthTypeWorkspaceConnectionProperties'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        """
+        super(WorkspaceConnectionPropertiesV2, self).__init__(**kwargs)
+        self.auth_type = None  # type: Optional[str]
+        self.category = kwargs.get('category', None)
+        self.target = kwargs.get('target', None)
+        self.value = kwargs.get('value', None)
+        self.value_format = kwargs.get('value_format', None)
+
+
+class ManagedIdentityAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """ManagedIdentityAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar credentials:
+    :vartype credentials:
+     ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionManagedIdentity
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+        'credentials': {'key': 'credentials', 'type': 'WorkspaceConnectionManagedIdentity'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword credentials:
+        :paramtype credentials:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionManagedIdentity
+        """
+        super(ManagedIdentityAuthTypeWorkspaceConnectionProperties, self).__init__(**kwargs)
+        self.auth_type = 'ManagedIdentity'  # type: str
+        self.credentials = kwargs.get('credentials', None)
+
+
+class ManagedOnlineDeployment(OnlineDeploymentProperties):
+    """Properties specific to a ManagedOnlineDeployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar code_configuration: Code configuration for the endpoint deployment.
+    :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
+     endpoint deployment.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables configuration for the deployment.
+    :vartype environment_variables: dict[str, str]
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar app_insights_enabled: If true, enables Application Insights logging.
+    :vartype app_insights_enabled: bool
+    :ivar egress_public_network_access: If Enabled, allow egress public network access. If
+     Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+     "Disabled".
+    :vartype egress_public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+    :ivar endpoint_compute_type: Required. [Required] The compute type of the endpoint.Constant
+     filled by server. Possible values include: "Managed", "Kubernetes", "AzureMLCompute".
+    :vartype endpoint_compute_type: str or
+     ~azure.mgmt.machinelearningservices.models.EndpointComputeType
+    :ivar instance_type: Compute instance type.
+    :vartype instance_type: str
+    :ivar liveness_probe: Liveness probe monitors the health of the container regularly.
+    :vartype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar model: The URI path to the model.
+    :vartype model: str
+    :ivar model_mount_path: The path to mount the model in custom container.
+    :vartype model_mount_path: str
+    :ivar provisioning_state: Provisioning state for the endpoint deployment. Possible values
+     include: "Creating", "Deleting", "Scaling", "Updating", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.DeploymentProvisioningState
+    :ivar readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+     The properties and defaults are the same as liveness probe.
+    :vartype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar request_settings: Request settings for the deployment.
+    :vartype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+    :ivar scale_settings: Scale settings for the deployment.
+     If it is null or not provided,
+     it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+     and to DefaultScaleSettings for ManagedOnlineDeployment.
+    :vartype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+    """
+
+    _validation = {
+        'endpoint_compute_type': {'required': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code_configuration': {'key': 'codeConfiguration', 'type': 'CodeConfiguration'},
+        'description': {'key': 'description', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
+        'egress_public_network_access': {'key': 'egressPublicNetworkAccess', 'type': 'str'},
+        'endpoint_compute_type': {'key': 'endpointComputeType', 'type': 'str'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'liveness_probe': {'key': 'livenessProbe', 'type': 'ProbeSettings'},
+        'model': {'key': 'model', 'type': 'str'},
+        'model_mount_path': {'key': 'modelMountPath', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'readiness_probe': {'key': 'readinessProbe', 'type': 'ProbeSettings'},
+        'request_settings': {'key': 'requestSettings', 'type': 'OnlineRequestSettings'},
+        'scale_settings': {'key': 'scaleSettings', 'type': 'OnlineScaleSettings'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword code_configuration: Code configuration for the endpoint deployment.
+        :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
+         endpoint deployment.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables configuration for the deployment.
+        :paramtype environment_variables: dict[str, str]
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword app_insights_enabled: If true, enables Application Insights logging.
+        :paramtype app_insights_enabled: bool
+        :keyword egress_public_network_access: If Enabled, allow egress public network access. If
+         Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+         "Disabled".
+        :paramtype egress_public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+        :keyword instance_type: Compute instance type.
+        :paramtype instance_type: str
+        :keyword liveness_probe: Liveness probe monitors the health of the container regularly.
+        :paramtype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword model: The URI path to the model.
+        :paramtype model: str
+        :keyword model_mount_path: The path to mount the model in custom container.
+        :paramtype model_mount_path: str
+        :keyword readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+         The properties and defaults are the same as liveness probe.
+        :paramtype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword request_settings: Request settings for the deployment.
+        :paramtype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+        :keyword scale_settings: Scale settings for the deployment.
+         If it is null or not provided,
+         it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+         and to DefaultScaleSettings for ManagedOnlineDeployment.
+        :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+        """
+        super(ManagedOnlineDeployment, self).__init__(**kwargs)
+        self.endpoint_compute_type = 'Managed'  # type: str
+
+
+class ManagedServiceIdentity(msrest.serialization.Model):
+    """Managed service identity (system assigned and/or user assigned identities).
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar principal_id: The service principal ID of the system assigned identity. This property
+     will only be provided for a system assigned identity.
+    :vartype principal_id: str
+    :ivar tenant_id: The tenant ID of the system assigned identity. This property will only be
+     provided for a system assigned identity.
+    :vartype tenant_id: str
+    :ivar type: Required. Type of managed service identity (where both SystemAssigned and
+     UserAssigned types are allowed). Possible values include: "None", "SystemAssigned",
+     "UserAssigned", "SystemAssigned,UserAssigned".
+    :vartype type: str or ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentityType
+    :ivar user_assigned_identities: The set of user assigned identities associated with the
+     resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form:
+     '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}.
+     The dictionary values can be empty objects ({}) in requests.
+    :vartype user_assigned_identities: dict[str,
+     ~azure.mgmt.machinelearningservices.models.UserAssignedIdentity]
+    """
+
+    _validation = {
+        'principal_id': {'readonly': True},
+        'tenant_id': {'readonly': True},
+        'type': {'required': True},
+    }
+
+    _attribute_map = {
+        'principal_id': {'key': 'principalId', 'type': 'str'},
+        'tenant_id': {'key': 'tenantId', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'user_assigned_identities': {'key': 'userAssignedIdentities', 'type': '{UserAssignedIdentity}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword type: Required. Type of managed service identity (where both SystemAssigned and
+         UserAssigned types are allowed). Possible values include: "None", "SystemAssigned",
+         "UserAssigned", "SystemAssigned,UserAssigned".
+        :paramtype type: str or ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentityType
+        :keyword user_assigned_identities: The set of user assigned identities associated with the
+         resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form:
+         '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}.
+         The dictionary values can be empty objects ({}) in requests.
+        :paramtype user_assigned_identities: dict[str,
+         ~azure.mgmt.machinelearningservices.models.UserAssignedIdentity]
+        """
+        super(ManagedServiceIdentity, self).__init__(**kwargs)
+        self.principal_id = None
+        self.tenant_id = None
+        self.type = kwargs['type']
+        self.user_assigned_identities = kwargs.get('user_assigned_identities', None)
+
+
+class MedianStoppingPolicy(EarlyTerminationPolicy):
+    """Defines an early termination policy based on running averages of the primary metric of all runs.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar delay_evaluation: Number of intervals by which to delay the first evaluation.
+    :vartype delay_evaluation: int
+    :ivar evaluation_interval: Interval (number of runs) between policy evaluations.
+    :vartype evaluation_interval: int
+    :ivar policy_type: Required. [Required] Name of policy configuration.Constant filled by server.
+     Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
+    :vartype policy_type: str or
+     ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicyType
+    """
+
+    _validation = {
+        'policy_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
+        'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
+        'policy_type': {'key': 'policyType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
+        :paramtype delay_evaluation: int
+        :keyword evaluation_interval: Interval (number of runs) between policy evaluations.
+        :paramtype evaluation_interval: int
+        """
+        super(MedianStoppingPolicy, self).__init__(**kwargs)
+        self.policy_type = 'MedianStopping'  # type: str
+
+
+class MLFlowModelJobInput(JobInput, AssetJobInput):
+    """MLFlowModelJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(MLFlowModelJobInput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs['uri']
+        self.job_input_type = 'mlflow_model'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class MLFlowModelJobOutput(JobOutput, AssetJobOutput):
+    """MLFlowModelJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(MLFlowModelJobOutput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs.get('uri', None)
+        self.job_output_type = 'mlflow_model'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class MLTableData(DataVersionBaseProperties):
+    """MLTable data definition.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar data_type: Required. [Required] Specifies the type of data.Constant filled by server.
+     Possible values include: "uri_file", "uri_folder", "mltable".
+    :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+    :ivar data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+    :vartype data_uri: str
+    :ivar referenced_uris: Uris referenced in the MLTable definition (required for lineage).
+    :vartype referenced_uris: list[str]
+    """
+
+    _validation = {
+        'data_type': {'required': True},
+        'data_uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'data_type': {'key': 'dataType', 'type': 'str'},
+        'data_uri': {'key': 'dataUri', 'type': 'str'},
+        'referenced_uris': {'key': 'referencedUris', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+        :paramtype data_uri: str
+        :keyword referenced_uris: Uris referenced in the MLTable definition (required for lineage).
+        :paramtype referenced_uris: list[str]
+        """
+        super(MLTableData, self).__init__(**kwargs)
+        self.data_type = 'mltable'  # type: str
+        self.referenced_uris = kwargs.get('referenced_uris', None)
+
+
+class MLTableJobInput(JobInput, AssetJobInput):
+    """MLTableJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(MLTableJobInput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs['uri']
+        self.job_input_type = 'mltable'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class MLTableJobOutput(JobOutput, AssetJobOutput):
+    """MLTableJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(MLTableJobOutput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs.get('uri', None)
+        self.job_output_type = 'mltable'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class ModelContainer(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ModelContainerProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'ModelContainerProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ModelContainerProperties
+        """
+        super(ModelContainer, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class ModelContainerProperties(AssetContainer):
+    """ModelContainerProperties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(ModelContainerProperties, self).__init__(**kwargs)
+
+
+class ModelContainerResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of ModelContainer entities.
+
+    :ivar next_link: The link to the next page of ModelContainer objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type ModelContainer.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ModelContainer]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[ModelContainer]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of ModelContainer objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type ModelContainer.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.ModelContainer]
+        """
+        super(ModelContainerResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class ModelVersion(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ModelVersionProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'ModelVersionProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ModelVersionProperties
+        """
+        super(ModelVersion, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class ModelVersionProperties(AssetBase):
+    """Model asset version details.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar flavors: Mapping of model flavors to their properties.
+    :vartype flavors: dict[str, ~azure.mgmt.machinelearningservices.models.FlavorData]
+    :ivar job_name: Name of the training job which produced this model.
+    :vartype job_name: str
+    :ivar model_type: The storage format for this entity. Used for NCD.
+    :vartype model_type: str
+    :ivar model_uri: The URI path to the model contents.
+    :vartype model_uri: str
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'flavors': {'key': 'flavors', 'type': '{FlavorData}'},
+        'job_name': {'key': 'jobName', 'type': 'str'},
+        'model_type': {'key': 'modelType', 'type': 'str'},
+        'model_uri': {'key': 'modelUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword flavors: Mapping of model flavors to their properties.
+        :paramtype flavors: dict[str, ~azure.mgmt.machinelearningservices.models.FlavorData]
+        :keyword job_name: Name of the training job which produced this model.
+        :paramtype job_name: str
+        :keyword model_type: The storage format for this entity. Used for NCD.
+        :paramtype model_type: str
+        :keyword model_uri: The URI path to the model contents.
+        :paramtype model_uri: str
+        """
+        super(ModelVersionProperties, self).__init__(**kwargs)
+        self.flavors = kwargs.get('flavors', None)
+        self.job_name = kwargs.get('job_name', None)
+        self.model_type = kwargs.get('model_type', None)
+        self.model_uri = kwargs.get('model_uri', None)
+
+
+class ModelVersionResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of ModelVersion entities.
+
+    :ivar next_link: The link to the next page of ModelVersion objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type ModelVersion.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ModelVersion]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[ModelVersion]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of ModelVersion objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type ModelVersion.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.ModelVersion]
+        """
+        super(ModelVersionResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class Mpi(DistributionConfiguration):
+    """MPI distribution configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar distribution_type: Required. [Required] Specifies the type of distribution
+     framework.Constant filled by server. Possible values include: "PyTorch", "TensorFlow", "Mpi".
+    :vartype distribution_type: str or ~azure.mgmt.machinelearningservices.models.DistributionType
+    :ivar process_count_per_instance: Number of processes per MPI node.
+    :vartype process_count_per_instance: int
+    """
+
+    _validation = {
+        'distribution_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'distribution_type': {'key': 'distributionType', 'type': 'str'},
+        'process_count_per_instance': {'key': 'processCountPerInstance', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword process_count_per_instance: Number of processes per MPI node.
+        :paramtype process_count_per_instance: int
+        """
+        super(Mpi, self).__init__(**kwargs)
+        self.distribution_type = 'Mpi'  # type: str
+        self.process_count_per_instance = kwargs.get('process_count_per_instance', None)
+
+
+class NlpVertical(msrest.serialization.Model):
+    """Abstract class for NLP related AutoML tasks.
+NLP - Natural Language Processing.
+
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    """
+
+    _attribute_map = {
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'NlpVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'NlpVerticalLimitSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        """
+        super(NlpVertical, self).__init__(**kwargs)
+        self.featurization_settings = kwargs.get('featurization_settings', None)
+        self.limit_settings = kwargs.get('limit_settings', None)
+        self.validation_data = kwargs.get('validation_data', None)
+
+
+class NlpVerticalFeaturizationSettings(FeaturizationSettings):
+    """NlpVerticalFeaturizationSettings.
+
+    :ivar dataset_language: Dataset language, useful for the text data.
+    :vartype dataset_language: str
+    """
+
+    _attribute_map = {
+        'dataset_language': {'key': 'datasetLanguage', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword dataset_language: Dataset language, useful for the text data.
+        :paramtype dataset_language: str
+        """
+        super(NlpVerticalFeaturizationSettings, self).__init__(**kwargs)
+
+
+class NlpVerticalLimitSettings(msrest.serialization.Model):
+    """Job execution constraints.
+
+    :ivar max_concurrent_trials: Maximum Concurrent AutoML iterations.
+    :vartype max_concurrent_trials: int
+    :ivar max_trials: Number of AutoML iterations.
+    :vartype max_trials: int
+    :ivar timeout: AutoML job timeout.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
+        'max_trials': {'key': 'maxTrials', 'type': 'int'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword max_concurrent_trials: Maximum Concurrent AutoML iterations.
+        :paramtype max_concurrent_trials: int
+        :keyword max_trials: Number of AutoML iterations.
+        :paramtype max_trials: int
+        :keyword timeout: AutoML job timeout.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(NlpVerticalLimitSettings, self).__init__(**kwargs)
+        self.max_concurrent_trials = kwargs.get('max_concurrent_trials', 1)
+        self.max_trials = kwargs.get('max_trials', 1)
+        self.timeout = kwargs.get('timeout', None)
+
+
+class NodeStateCounts(msrest.serialization.Model):
+    """Counts of various compute node states on the amlCompute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar idle_node_count: Number of compute nodes in idle state.
+    :vartype idle_node_count: int
+    :ivar running_node_count: Number of compute nodes which are running jobs.
+    :vartype running_node_count: int
+    :ivar preparing_node_count: Number of compute nodes which are being prepared.
+    :vartype preparing_node_count: int
+    :ivar unusable_node_count: Number of compute nodes which are in unusable state.
+    :vartype unusable_node_count: int
+    :ivar leaving_node_count: Number of compute nodes which are leaving the amlCompute.
+    :vartype leaving_node_count: int
+    :ivar preempted_node_count: Number of compute nodes which are in preempted state.
+    :vartype preempted_node_count: int
+    """
+
+    _validation = {
+        'idle_node_count': {'readonly': True},
+        'running_node_count': {'readonly': True},
+        'preparing_node_count': {'readonly': True},
+        'unusable_node_count': {'readonly': True},
+        'leaving_node_count': {'readonly': True},
+        'preempted_node_count': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'idle_node_count': {'key': 'idleNodeCount', 'type': 'int'},
+        'running_node_count': {'key': 'runningNodeCount', 'type': 'int'},
+        'preparing_node_count': {'key': 'preparingNodeCount', 'type': 'int'},
+        'unusable_node_count': {'key': 'unusableNodeCount', 'type': 'int'},
+        'leaving_node_count': {'key': 'leavingNodeCount', 'type': 'int'},
+        'preempted_node_count': {'key': 'preemptedNodeCount', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(NodeStateCounts, self).__init__(**kwargs)
+        self.idle_node_count = None
+        self.running_node_count = None
+        self.preparing_node_count = None
+        self.unusable_node_count = None
+        self.leaving_node_count = None
+        self.preempted_node_count = None
+
+
+class NoneAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """NoneAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        """
+        super(NoneAuthTypeWorkspaceConnectionProperties, self).__init__(**kwargs)
+        self.auth_type = 'None'  # type: str
+
+
+class NoneDatastoreCredentials(DatastoreCredentials):
+    """Empty/none datastore credentials.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(NoneDatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = 'None'  # type: str
+
+
+class NotebookAccessTokenResult(msrest.serialization.Model):
+    """NotebookAccessTokenResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar notebook_resource_id:
+    :vartype notebook_resource_id: str
+    :ivar host_name:
+    :vartype host_name: str
+    :ivar public_dns:
+    :vartype public_dns: str
+    :ivar access_token:
+    :vartype access_token: str
+    :ivar token_type:
+    :vartype token_type: str
+    :ivar expires_in:
+    :vartype expires_in: int
+    :ivar refresh_token:
+    :vartype refresh_token: str
+    :ivar scope:
+    :vartype scope: str
+    """
+
+    _validation = {
+        'notebook_resource_id': {'readonly': True},
+        'host_name': {'readonly': True},
+        'public_dns': {'readonly': True},
+        'access_token': {'readonly': True},
+        'token_type': {'readonly': True},
+        'expires_in': {'readonly': True},
+        'refresh_token': {'readonly': True},
+        'scope': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'notebook_resource_id': {'key': 'notebookResourceId', 'type': 'str'},
+        'host_name': {'key': 'hostName', 'type': 'str'},
+        'public_dns': {'key': 'publicDns', 'type': 'str'},
+        'access_token': {'key': 'accessToken', 'type': 'str'},
+        'token_type': {'key': 'tokenType', 'type': 'str'},
+        'expires_in': {'key': 'expiresIn', 'type': 'int'},
+        'refresh_token': {'key': 'refreshToken', 'type': 'str'},
+        'scope': {'key': 'scope', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(NotebookAccessTokenResult, self).__init__(**kwargs)
+        self.notebook_resource_id = None
+        self.host_name = None
+        self.public_dns = None
+        self.access_token = None
+        self.token_type = None
+        self.expires_in = None
+        self.refresh_token = None
+        self.scope = None
+
+
+class NotebookPreparationError(msrest.serialization.Model):
+    """NotebookPreparationError.
+
+    :ivar error_message:
+    :vartype error_message: str
+    :ivar status_code:
+    :vartype status_code: int
+    """
+
+    _attribute_map = {
+        'error_message': {'key': 'errorMessage', 'type': 'str'},
+        'status_code': {'key': 'statusCode', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword error_message:
+        :paramtype error_message: str
+        :keyword status_code:
+        :paramtype status_code: int
+        """
+        super(NotebookPreparationError, self).__init__(**kwargs)
+        self.error_message = kwargs.get('error_message', None)
+        self.status_code = kwargs.get('status_code', None)
+
+
+class NotebookResourceInfo(msrest.serialization.Model):
+    """NotebookResourceInfo.
+
+    :ivar fqdn:
+    :vartype fqdn: str
+    :ivar resource_id: the data plane resourceId that used to initialize notebook component.
+    :vartype resource_id: str
+    :ivar notebook_preparation_error: The error that occurs when preparing notebook.
+    :vartype notebook_preparation_error:
+     ~azure.mgmt.machinelearningservices.models.NotebookPreparationError
+    """
+
+    _attribute_map = {
+        'fqdn': {'key': 'fqdn', 'type': 'str'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'notebook_preparation_error': {'key': 'notebookPreparationError', 'type': 'NotebookPreparationError'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword fqdn:
+        :paramtype fqdn: str
+        :keyword resource_id: the data plane resourceId that used to initialize notebook component.
+        :paramtype resource_id: str
+        :keyword notebook_preparation_error: The error that occurs when preparing notebook.
+        :paramtype notebook_preparation_error:
+         ~azure.mgmt.machinelearningservices.models.NotebookPreparationError
+        """
+        super(NotebookResourceInfo, self).__init__(**kwargs)
+        self.fqdn = kwargs.get('fqdn', None)
+        self.resource_id = kwargs.get('resource_id', None)
+        self.notebook_preparation_error = kwargs.get('notebook_preparation_error', None)
+
+
+class Objective(msrest.serialization.Model):
+    """Optimization objective.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar goal: Required. [Required] Defines supported metric goals for hyperparameter tuning.
+     Possible values include: "Minimize", "Maximize".
+    :vartype goal: str or ~azure.mgmt.machinelearningservices.models.Goal
+    :ivar primary_metric: Required. [Required] Name of the metric to optimize.
+    :vartype primary_metric: str
+    """
+
+    _validation = {
+        'goal': {'required': True},
+        'primary_metric': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'goal': {'key': 'goal', 'type': 'str'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword goal: Required. [Required] Defines supported metric goals for hyperparameter tuning.
+         Possible values include: "Minimize", "Maximize".
+        :paramtype goal: str or ~azure.mgmt.machinelearningservices.models.Goal
+        :keyword primary_metric: Required. [Required] Name of the metric to optimize.
+        :paramtype primary_metric: str
+        """
+        super(Objective, self).__init__(**kwargs)
+        self.goal = kwargs['goal']
+        self.primary_metric = kwargs['primary_metric']
+
+
+class OnlineDeployment(TrackedResource):
+    """OnlineDeployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: Required. The geo-location where the resource lives.
+    :vartype location: str
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.OnlineDeploymentProperties
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'location': {'required': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'location': {'key': 'location', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'kind': {'key': 'kind', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': 'OnlineDeploymentProperties'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: Required. The geo-location where the resource lives.
+        :paramtype location: str
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.OnlineDeploymentProperties
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super(OnlineDeployment, self).__init__(**kwargs)
+        self.identity = kwargs.get('identity', None)
+        self.kind = kwargs.get('kind', None)
+        self.properties = kwargs['properties']
+        self.sku = kwargs.get('sku', None)
+
+
+class OnlineDeploymentTrackedResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of OnlineDeployment entities.
+
+    :ivar next_link: The link to the next page of OnlineDeployment objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type OnlineDeployment.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.OnlineDeployment]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[OnlineDeployment]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of OnlineDeployment objects. If null, there are
+         no additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type OnlineDeployment.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.OnlineDeployment]
+        """
+        super(OnlineDeploymentTrackedResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class OnlineEndpoint(TrackedResource):
+    """OnlineEndpoint.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: Required. The geo-location where the resource lives.
+    :vartype location: str
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.OnlineEndpointProperties
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'location': {'required': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'location': {'key': 'location', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'kind': {'key': 'kind', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': 'OnlineEndpointProperties'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: Required. The geo-location where the resource lives.
+        :paramtype location: str
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.OnlineEndpointProperties
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super(OnlineEndpoint, self).__init__(**kwargs)
+        self.identity = kwargs.get('identity', None)
+        self.kind = kwargs.get('kind', None)
+        self.properties = kwargs['properties']
+        self.sku = kwargs.get('sku', None)
+
+
+class OnlineEndpointProperties(EndpointPropertiesBase):
+    """Online endpoint configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken' for
+     Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
+     Possible values include: "AMLToken", "Key", "AADToken".
+    :vartype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+    :ivar description: Description of the inference endpoint.
+    :vartype description: str
+    :ivar keys: EndpointAuthKeys to set initially on an Endpoint.
+     This property will always be returned as null. AuthKey values must be retrieved using the
+     ListKeys API.
+    :vartype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar scoring_uri: Endpoint URI.
+    :vartype scoring_uri: str
+    :ivar swagger_uri: Endpoint Swagger URI.
+    :vartype swagger_uri: str
+    :ivar compute: ARM resource ID of the compute if it exists.
+     optional.
+    :vartype compute: str
+    :ivar provisioning_state: Provisioning state for the endpoint. Possible values include:
+     "Creating", "Deleting", "Succeeded", "Failed", "Updating", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.EndpointProvisioningState
+    :ivar public_network_access: Set to "Enabled" for endpoints that should allow public access
+     when Private Link is enabled. Possible values include: "Enabled", "Disabled".
+    :vartype public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.PublicNetworkAccessType
+    :ivar traffic: Percentage of traffic from endpoint to divert to each deployment. Traffic values
+     need to sum to 100.
+    :vartype traffic: dict[str, int]
+    """
+
+    _validation = {
+        'auth_mode': {'required': True},
+        'scoring_uri': {'readonly': True},
+        'swagger_uri': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'auth_mode': {'key': 'authMode', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'keys': {'key': 'keys', 'type': 'EndpointAuthKeys'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'scoring_uri': {'key': 'scoringUri', 'type': 'str'},
+        'swagger_uri': {'key': 'swaggerUri', 'type': 'str'},
+        'compute': {'key': 'compute', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'public_network_access': {'key': 'publicNetworkAccess', 'type': 'str'},
+        'traffic': {'key': 'traffic', 'type': '{int}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken'
+         for Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken'
+         does. Possible values include: "AMLToken", "Key", "AADToken".
+        :paramtype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+        :keyword description: Description of the inference endpoint.
+        :paramtype description: str
+        :keyword keys: EndpointAuthKeys to set initially on an Endpoint.
+         This property will always be returned as null. AuthKey values must be retrieved using the
+         ListKeys API.
+        :paramtype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword compute: ARM resource ID of the compute if it exists.
+         optional.
+        :paramtype compute: str
+        :keyword public_network_access: Set to "Enabled" for endpoints that should allow public access
+         when Private Link is enabled. Possible values include: "Enabled", "Disabled".
+        :paramtype public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.PublicNetworkAccessType
+        :keyword traffic: Percentage of traffic from endpoint to divert to each deployment. Traffic
+         values need to sum to 100.
+        :paramtype traffic: dict[str, int]
+        """
+        super(OnlineEndpointProperties, self).__init__(**kwargs)
+        self.compute = kwargs.get('compute', None)
+        self.provisioning_state = None
+        self.public_network_access = kwargs.get('public_network_access', None)
+        self.traffic = kwargs.get('traffic', None)
+
+
+class OnlineEndpointTrackedResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of OnlineEndpoint entities.
+
+    :ivar next_link: The link to the next page of OnlineEndpoint objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type OnlineEndpoint.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.OnlineEndpoint]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[OnlineEndpoint]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of OnlineEndpoint objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type OnlineEndpoint.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.OnlineEndpoint]
+        """
+        super(OnlineEndpointTrackedResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class OnlineRequestSettings(msrest.serialization.Model):
+    """Online deployment scoring requests configuration.
+
+    :ivar max_concurrent_requests_per_instance: The number of maximum concurrent requests per node
+     allowed per deployment. Defaults to 1.
+    :vartype max_concurrent_requests_per_instance: int
+    :ivar max_queue_wait: The maximum amount of time a request will stay in the queue in ISO 8601
+     format.
+     Defaults to 500ms.
+    :vartype max_queue_wait: ~datetime.timedelta
+    :ivar request_timeout: The scoring timeout in ISO 8601 format.
+     Defaults to 5000ms.
+    :vartype request_timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'max_concurrent_requests_per_instance': {'key': 'maxConcurrentRequestsPerInstance', 'type': 'int'},
+        'max_queue_wait': {'key': 'maxQueueWait', 'type': 'duration'},
+        'request_timeout': {'key': 'requestTimeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword max_concurrent_requests_per_instance: The number of maximum concurrent requests per
+         node allowed per deployment. Defaults to 1.
+        :paramtype max_concurrent_requests_per_instance: int
+        :keyword max_queue_wait: The maximum amount of time a request will stay in the queue in ISO
+         8601 format.
+         Defaults to 500ms.
+        :paramtype max_queue_wait: ~datetime.timedelta
+        :keyword request_timeout: The scoring timeout in ISO 8601 format.
+         Defaults to 5000ms.
+        :paramtype request_timeout: ~datetime.timedelta
+        """
+        super(OnlineRequestSettings, self).__init__(**kwargs)
+        self.max_concurrent_requests_per_instance = kwargs.get('max_concurrent_requests_per_instance', 1)
+        self.max_queue_wait = kwargs.get('max_queue_wait', "PT0.5S")
+        self.request_timeout = kwargs.get('request_timeout', "PT5S")
+
+
+class OutputPathAssetReference(AssetReferenceBase):
+    """Reference to an asset via its path in a job output.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar reference_type: Required. [Required] Specifies the type of asset reference.Constant
+     filled by server. Possible values include: "Id", "DataPath", "OutputPath".
+    :vartype reference_type: str or ~azure.mgmt.machinelearningservices.models.ReferenceType
+    :ivar job_id: ARM resource ID of the job.
+    :vartype job_id: str
+    :ivar path: The path of the file/directory in the job output.
+    :vartype path: str
+    """
+
+    _validation = {
+        'reference_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'reference_type': {'key': 'referenceType', 'type': 'str'},
+        'job_id': {'key': 'jobId', 'type': 'str'},
+        'path': {'key': 'path', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword job_id: ARM resource ID of the job.
+        :paramtype job_id: str
+        :keyword path: The path of the file/directory in the job output.
+        :paramtype path: str
+        """
+        super(OutputPathAssetReference, self).__init__(**kwargs)
+        self.reference_type = 'OutputPath'  # type: str
+        self.job_id = kwargs.get('job_id', None)
+        self.path = kwargs.get('path', None)
+
+
+class PaginatedComputeResourcesList(msrest.serialization.Model):
+    """Paginated list of Machine Learning compute objects wrapped in ARM resource envelope.
+
+    :ivar value: An array of Machine Learning compute objects wrapped in ARM resource envelope.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ComputeResource]
+    :ivar next_link: A continuation link (absolute URI) to the next page of results in the list.
+    :vartype next_link: str
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[ComputeResource]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: An array of Machine Learning compute objects wrapped in ARM resource envelope.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.ComputeResource]
+        :keyword next_link: A continuation link (absolute URI) to the next page of results in the list.
+        :paramtype next_link: str
+        """
+        super(PaginatedComputeResourcesList, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+        self.next_link = kwargs.get('next_link', None)
+
+
+class PartialBatchDeployment(msrest.serialization.Model):
+    """Mutable batch inference settings per deployment.
+
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        """
+        super(PartialBatchDeployment, self).__init__(**kwargs)
+        self.description = kwargs.get('description', None)
+
+
+class PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties(msrest.serialization.Model):
+    """Strictly used in update requests.
+
+    :ivar properties: Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.PartialBatchDeployment
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'PartialBatchDeployment'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.PartialBatchDeployment
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        """
+        super(PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+        self.tags = kwargs.get('tags', None)
+
+
+class PartialManagedServiceIdentity(msrest.serialization.Model):
+    """Managed service identity (system assigned and/or user assigned identities).
+
+    :ivar type: Managed service identity (system assigned and/or user assigned identities).
+     Possible values include: "None", "SystemAssigned", "UserAssigned",
+     "SystemAssigned,UserAssigned".
+    :vartype type: str or ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentityType
+    :ivar user_assigned_identities: The set of user assigned identities associated with the
+     resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form:
+     '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}.
+     The dictionary values can be empty objects ({}) in requests.
+    :vartype user_assigned_identities: dict[str, any]
+    """
+
+    _attribute_map = {
+        'type': {'key': 'type', 'type': 'str'},
+        'user_assigned_identities': {'key': 'userAssignedIdentities', 'type': '{object}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword type: Managed service identity (system assigned and/or user assigned identities).
+         Possible values include: "None", "SystemAssigned", "UserAssigned",
+         "SystemAssigned,UserAssigned".
+        :paramtype type: str or ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentityType
+        :keyword user_assigned_identities: The set of user assigned identities associated with the
+         resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form:
+         '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}.
+         The dictionary values can be empty objects ({}) in requests.
+        :paramtype user_assigned_identities: dict[str, any]
+        """
+        super(PartialManagedServiceIdentity, self).__init__(**kwargs)
+        self.type = kwargs.get('type', None)
+        self.user_assigned_identities = kwargs.get('user_assigned_identities', None)
+
+
+class PartialMinimalTrackedResource(msrest.serialization.Model):
+    """Strictly used in update requests.
+
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    """
+
+    _attribute_map = {
+        'tags': {'key': 'tags', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        """
+        super(PartialMinimalTrackedResource, self).__init__(**kwargs)
+        self.tags = kwargs.get('tags', None)
+
+
+class PartialMinimalTrackedResourceWithIdentity(PartialMinimalTrackedResource):
+    """Strictly used in update requests.
+
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.PartialManagedServiceIdentity
+    """
+
+    _attribute_map = {
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'identity': {'key': 'identity', 'type': 'PartialManagedServiceIdentity'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.PartialManagedServiceIdentity
+        """
+        super(PartialMinimalTrackedResourceWithIdentity, self).__init__(**kwargs)
+        self.identity = kwargs.get('identity', None)
+
+
+class PartialMinimalTrackedResourceWithSku(PartialMinimalTrackedResource):
+    """Strictly used in update requests.
+
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.PartialSku
+    """
+
+    _attribute_map = {
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'PartialSku'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.PartialSku
+        """
+        super(PartialMinimalTrackedResourceWithSku, self).__init__(**kwargs)
+        self.sku = kwargs.get('sku', None)
+
+
+class PartialSku(msrest.serialization.Model):
+    """Common SKU definition.
+
+    :ivar capacity: If the SKU supports scale out/in then the capacity integer should be included.
+     If scale out/in is not possible for the resource this may be omitted.
+    :vartype capacity: int
+    :ivar family: If the service has different generations of hardware, for the same SKU, then that
+     can be captured here.
+    :vartype family: str
+    :ivar name: The name of the SKU. Ex - P3. It is typically a letter+number code.
+    :vartype name: str
+    :ivar size: The SKU size. When the name field is the combination of tier and some other value,
+     this would be the standalone code.
+    :vartype size: str
+    :ivar tier: This field is required to be implemented by the Resource Provider if the service
+     has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+     "Standard", "Premium".
+    :vartype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+    """
+
+    _attribute_map = {
+        'capacity': {'key': 'capacity', 'type': 'int'},
+        'family': {'key': 'family', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'size': {'key': 'size', 'type': 'str'},
+        'tier': {'key': 'tier', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword capacity: If the SKU supports scale out/in then the capacity integer should be
+         included. If scale out/in is not possible for the resource this may be omitted.
+        :paramtype capacity: int
+        :keyword family: If the service has different generations of hardware, for the same SKU, then
+         that can be captured here.
+        :paramtype family: str
+        :keyword name: The name of the SKU. Ex - P3. It is typically a letter+number code.
+        :paramtype name: str
+        :keyword size: The SKU size. When the name field is the combination of tier and some other
+         value, this would be the standalone code.
+        :paramtype size: str
+        :keyword tier: This field is required to be implemented by the Resource Provider if the service
+         has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+         "Standard", "Premium".
+        :paramtype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+        """
+        super(PartialSku, self).__init__(**kwargs)
+        self.capacity = kwargs.get('capacity', None)
+        self.family = kwargs.get('family', None)
+        self.name = kwargs.get('name', None)
+        self.size = kwargs.get('size', None)
+        self.tier = kwargs.get('tier', None)
+
+
+class Password(msrest.serialization.Model):
+    """Password.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar name:
+    :vartype name: str
+    :ivar value:
+    :vartype value: str
+    """
+
+    _validation = {
+        'name': {'readonly': True},
+        'value': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(Password, self).__init__(**kwargs)
+        self.name = None
+        self.value = None
+
+
+class PATAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """PATAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar credentials:
+    :vartype credentials:
+     ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPersonalAccessToken
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+        'credentials': {'key': 'credentials', 'type': 'WorkspaceConnectionPersonalAccessToken'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword credentials:
+        :paramtype credentials:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPersonalAccessToken
+        """
+        super(PATAuthTypeWorkspaceConnectionProperties, self).__init__(**kwargs)
+        self.auth_type = 'PAT'  # type: str
+        self.credentials = kwargs.get('credentials', None)
+
+
+class PersonalComputeInstanceSettings(msrest.serialization.Model):
+    """Settings for a personal compute instance.
+
+    :ivar assigned_user: A user explicitly assigned to a personal compute instance.
+    :vartype assigned_user: ~azure.mgmt.machinelearningservices.models.AssignedUser
+    """
+
+    _attribute_map = {
+        'assigned_user': {'key': 'assignedUser', 'type': 'AssignedUser'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword assigned_user: A user explicitly assigned to a personal compute instance.
+        :paramtype assigned_user: ~azure.mgmt.machinelearningservices.models.AssignedUser
+        """
+        super(PersonalComputeInstanceSettings, self).__init__(**kwargs)
+        self.assigned_user = kwargs.get('assigned_user', None)
+
+
+class PipelineJob(JobBaseProperties):
+    """Pipeline Job definition: defines generic to MFE attributes.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "AutoML", "Command", "Sweep", "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Possible values include: "NotStarted", "Starting",
+     "Provisioning", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
+     "Failed", "Canceled", "NotResponding", "Paused", "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar inputs: Inputs for the pipeline job.
+    :vartype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+    :ivar jobs: Jobs construct the Pipeline Job.
+    :vartype jobs: dict[str, any]
+    :ivar outputs: Outputs for the pipeline job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar settings: Pipeline settings, for things like ContinueRunOnStepFailure etc.
+    :vartype settings: any
+    :ivar source_job_id: ARM resource ID of source job.
+    :vartype source_job_id: str
+    """
+
+    _validation = {
+        'job_type': {'required': True},
+        'status': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'component_id': {'key': 'componentId', 'type': 'str'},
+        'compute_id': {'key': 'computeId', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'experiment_name': {'key': 'experimentName', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'job_type': {'key': 'jobType', 'type': 'str'},
+        'services': {'key': 'services', 'type': '{JobService}'},
+        'status': {'key': 'status', 'type': 'str'},
+        'inputs': {'key': 'inputs', 'type': '{JobInput}'},
+        'jobs': {'key': 'jobs', 'type': '{object}'},
+        'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
+        'settings': {'key': 'settings', 'type': 'object'},
+        'source_job_id': {'key': 'sourceJobId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword inputs: Inputs for the pipeline job.
+        :paramtype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+        :keyword jobs: Jobs construct the Pipeline Job.
+        :paramtype jobs: dict[str, any]
+        :keyword outputs: Outputs for the pipeline job.
+        :paramtype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+        :keyword settings: Pipeline settings, for things like ContinueRunOnStepFailure etc.
+        :paramtype settings: any
+        :keyword source_job_id: ARM resource ID of source job.
+        :paramtype source_job_id: str
+        """
+        super(PipelineJob, self).__init__(**kwargs)
+        self.job_type = 'Pipeline'  # type: str
+        self.inputs = kwargs.get('inputs', None)
+        self.jobs = kwargs.get('jobs', None)
+        self.outputs = kwargs.get('outputs', None)
+        self.settings = kwargs.get('settings', None)
+        self.source_job_id = kwargs.get('source_job_id', None)
+
+
+class PrivateEndpoint(msrest.serialization.Model):
+    """The Private Endpoint resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: The ARM identifier for Private Endpoint.
+    :vartype id: str
+    :ivar subnet_arm_id: The ARM identifier for Subnet resource that private endpoint links to.
+    :vartype subnet_arm_id: str
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'subnet_arm_id': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'subnet_arm_id': {'key': 'subnetArmId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(PrivateEndpoint, self).__init__(**kwargs)
+        self.id = None
+        self.subnet_arm_id = None
+
+
+class PrivateEndpointConnection(Resource):
+    """The Private Endpoint Connection resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar identity: The identity of the resource.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar location: Specifies the location of the resource.
+    :vartype location: str
+    :ivar tags: A set of tags. Contains resource tags defined as key/value pairs.
+    :vartype tags: dict[str, str]
+    :ivar sku: The sku of the workspace.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    :ivar private_endpoint: The resource of private end point.
+    :vartype private_endpoint: ~azure.mgmt.machinelearningservices.models.PrivateEndpoint
+    :ivar private_link_service_connection_state: A collection of information about the state of the
+     connection between service consumer and provider.
+    :vartype private_link_service_connection_state:
+     ~azure.mgmt.machinelearningservices.models.PrivateLinkServiceConnectionState
+    :ivar provisioning_state: The provisioning state of the private endpoint connection resource.
+     Possible values include: "Succeeded", "Creating", "Deleting", "Failed".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.PrivateEndpointConnectionProvisioningState
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'location': {'key': 'location', 'type': 'str'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+        'private_endpoint': {'key': 'properties.privateEndpoint', 'type': 'PrivateEndpoint'},
+        'private_link_service_connection_state': {'key': 'properties.privateLinkServiceConnectionState', 'type': 'PrivateLinkServiceConnectionState'},
+        'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword identity: The identity of the resource.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword location: Specifies the location of the resource.
+        :paramtype location: str
+        :keyword tags: A set of tags. Contains resource tags defined as key/value pairs.
+        :paramtype tags: dict[str, str]
+        :keyword sku: The sku of the workspace.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        :keyword private_endpoint: The resource of private end point.
+        :paramtype private_endpoint: ~azure.mgmt.machinelearningservices.models.PrivateEndpoint
+        :keyword private_link_service_connection_state: A collection of information about the state of
+         the connection between service consumer and provider.
+        :paramtype private_link_service_connection_state:
+         ~azure.mgmt.machinelearningservices.models.PrivateLinkServiceConnectionState
+        """
+        super(PrivateEndpointConnection, self).__init__(**kwargs)
+        self.identity = kwargs.get('identity', None)
+        self.location = kwargs.get('location', None)
+        self.tags = kwargs.get('tags', None)
+        self.sku = kwargs.get('sku', None)
+        self.private_endpoint = kwargs.get('private_endpoint', None)
+        self.private_link_service_connection_state = kwargs.get('private_link_service_connection_state', None)
+        self.provisioning_state = None
+
+
+class PrivateEndpointConnectionListResult(msrest.serialization.Model):
+    """List of private endpoint connection associated with the specified workspace.
+
+    :ivar value: Array of private endpoint connections.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection]
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[PrivateEndpointConnection]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: Array of private endpoint connections.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection]
+        """
+        super(PrivateEndpointConnectionListResult, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+
+
+class PrivateLinkResource(Resource):
+    """A private link resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar identity: The identity of the resource.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar location: Specifies the location of the resource.
+    :vartype location: str
+    :ivar tags: A set of tags. Contains resource tags defined as key/value pairs.
+    :vartype tags: dict[str, str]
+    :ivar sku: The sku of the workspace.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    :ivar group_id: The private link resource group id.
+    :vartype group_id: str
+    :ivar required_members: The private link resource required member names.
+    :vartype required_members: list[str]
+    :ivar required_zone_names: The private link resource Private link DNS zone name.
+    :vartype required_zone_names: list[str]
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'group_id': {'readonly': True},
+        'required_members': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'location': {'key': 'location', 'type': 'str'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+        'group_id': {'key': 'properties.groupId', 'type': 'str'},
+        'required_members': {'key': 'properties.requiredMembers', 'type': '[str]'},
+        'required_zone_names': {'key': 'properties.requiredZoneNames', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword identity: The identity of the resource.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword location: Specifies the location of the resource.
+        :paramtype location: str
+        :keyword tags: A set of tags. Contains resource tags defined as key/value pairs.
+        :paramtype tags: dict[str, str]
+        :keyword sku: The sku of the workspace.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        :keyword required_zone_names: The private link resource Private link DNS zone name.
+        :paramtype required_zone_names: list[str]
+        """
+        super(PrivateLinkResource, self).__init__(**kwargs)
+        self.identity = kwargs.get('identity', None)
+        self.location = kwargs.get('location', None)
+        self.tags = kwargs.get('tags', None)
+        self.sku = kwargs.get('sku', None)
+        self.group_id = None
+        self.required_members = None
+        self.required_zone_names = kwargs.get('required_zone_names', None)
+
+
+class PrivateLinkResourceListResult(msrest.serialization.Model):
+    """A list of private link resources.
+
+    :ivar value: Array of private link resources.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.PrivateLinkResource]
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[PrivateLinkResource]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: Array of private link resources.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.PrivateLinkResource]
+        """
+        super(PrivateLinkResourceListResult, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+
+
+class PrivateLinkServiceConnectionState(msrest.serialization.Model):
+    """A collection of information about the state of the connection between service consumer and provider.
+
+    :ivar status: Indicates whether the connection has been Approved/Rejected/Removed by the owner
+     of the service. Possible values include: "Pending", "Approved", "Rejected", "Disconnected",
+     "Timeout".
+    :vartype status: str or
+     ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+    :ivar description: The reason for approval/rejection of the connection.
+    :vartype description: str
+    :ivar actions_required: A message indicating if changes on the service provider require any
+     updates on the consumer.
+    :vartype actions_required: str
+    """
+
+    _attribute_map = {
+        'status': {'key': 'status', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'actions_required': {'key': 'actionsRequired', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword status: Indicates whether the connection has been Approved/Rejected/Removed by the
+         owner of the service. Possible values include: "Pending", "Approved", "Rejected",
+         "Disconnected", "Timeout".
+        :paramtype status: str or
+         ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+        :keyword description: The reason for approval/rejection of the connection.
+        :paramtype description: str
+        :keyword actions_required: A message indicating if changes on the service provider require any
+         updates on the consumer.
+        :paramtype actions_required: str
+        """
+        super(PrivateLinkServiceConnectionState, self).__init__(**kwargs)
+        self.status = kwargs.get('status', None)
+        self.description = kwargs.get('description', None)
+        self.actions_required = kwargs.get('actions_required', None)
+
+
+class ProbeSettings(msrest.serialization.Model):
+    """Deployment container liveness/readiness probe configuration.
+
+    :ivar failure_threshold: The number of failures to allow before returning an unhealthy status.
+    :vartype failure_threshold: int
+    :ivar initial_delay: The delay before the first probe in ISO 8601 format.
+    :vartype initial_delay: ~datetime.timedelta
+    :ivar period: The length of time between probes in ISO 8601 format.
+    :vartype period: ~datetime.timedelta
+    :ivar success_threshold: The number of successful probes before returning a healthy status.
+    :vartype success_threshold: int
+    :ivar timeout: The probe timeout in ISO 8601 format.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'failure_threshold': {'key': 'failureThreshold', 'type': 'int'},
+        'initial_delay': {'key': 'initialDelay', 'type': 'duration'},
+        'period': {'key': 'period', 'type': 'duration'},
+        'success_threshold': {'key': 'successThreshold', 'type': 'int'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword failure_threshold: The number of failures to allow before returning an unhealthy
+         status.
+        :paramtype failure_threshold: int
+        :keyword initial_delay: The delay before the first probe in ISO 8601 format.
+        :paramtype initial_delay: ~datetime.timedelta
+        :keyword period: The length of time between probes in ISO 8601 format.
+        :paramtype period: ~datetime.timedelta
+        :keyword success_threshold: The number of successful probes before returning a healthy status.
+        :paramtype success_threshold: int
+        :keyword timeout: The probe timeout in ISO 8601 format.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(ProbeSettings, self).__init__(**kwargs)
+        self.failure_threshold = kwargs.get('failure_threshold', 30)
+        self.initial_delay = kwargs.get('initial_delay', None)
+        self.period = kwargs.get('period', "PT10S")
+        self.success_threshold = kwargs.get('success_threshold', 1)
+        self.timeout = kwargs.get('timeout', "PT2S")
+
+
+class PyTorch(DistributionConfiguration):
+    """PyTorch distribution configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar distribution_type: Required. [Required] Specifies the type of distribution
+     framework.Constant filled by server. Possible values include: "PyTorch", "TensorFlow", "Mpi".
+    :vartype distribution_type: str or ~azure.mgmt.machinelearningservices.models.DistributionType
+    :ivar process_count_per_instance: Number of processes per node.
+    :vartype process_count_per_instance: int
+    """
+
+    _validation = {
+        'distribution_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'distribution_type': {'key': 'distributionType', 'type': 'str'},
+        'process_count_per_instance': {'key': 'processCountPerInstance', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword process_count_per_instance: Number of processes per node.
+        :paramtype process_count_per_instance: int
+        """
+        super(PyTorch, self).__init__(**kwargs)
+        self.distribution_type = 'PyTorch'  # type: str
+        self.process_count_per_instance = kwargs.get('process_count_per_instance', None)
+
+
+class QuotaBaseProperties(msrest.serialization.Model):
+    """The properties for Quota update or retrieval.
+
+    :ivar id: Specifies the resource ID.
+    :vartype id: str
+    :ivar type: Specifies the resource type.
+    :vartype type: str
+    :ivar limit: The maximum permitted quota of the resource.
+    :vartype limit: long
+    :ivar unit: An enum describing the unit of quota measurement. Possible values include: "Count".
+    :vartype unit: str or ~azure.mgmt.machinelearningservices.models.QuotaUnit
+    """
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'limit': {'key': 'limit', 'type': 'long'},
+        'unit': {'key': 'unit', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword id: Specifies the resource ID.
+        :paramtype id: str
+        :keyword type: Specifies the resource type.
+        :paramtype type: str
+        :keyword limit: The maximum permitted quota of the resource.
+        :paramtype limit: long
+        :keyword unit: An enum describing the unit of quota measurement. Possible values include:
+         "Count".
+        :paramtype unit: str or ~azure.mgmt.machinelearningservices.models.QuotaUnit
+        """
+        super(QuotaBaseProperties, self).__init__(**kwargs)
+        self.id = kwargs.get('id', None)
+        self.type = kwargs.get('type', None)
+        self.limit = kwargs.get('limit', None)
+        self.unit = kwargs.get('unit', None)
+
+
+class QuotaUpdateParameters(msrest.serialization.Model):
+    """Quota update parameters.
+
+    :ivar value: The list for update quota.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.QuotaBaseProperties]
+    :ivar location: Region of workspace quota to be updated.
+    :vartype location: str
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[QuotaBaseProperties]'},
+        'location': {'key': 'location', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: The list for update quota.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.QuotaBaseProperties]
+        :keyword location: Region of workspace quota to be updated.
+        :paramtype location: str
+        """
+        super(QuotaUpdateParameters, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+        self.location = kwargs.get('location', None)
+
+
+class RandomSamplingAlgorithm(SamplingAlgorithm):
+    """Defines a Sampling Algorithm that generates values randomly.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar sampling_algorithm_type: Required. [Required] The algorithm used for generating
+     hyperparameter values, along with configuration properties.Constant filled by server. Possible
+     values include: "Grid", "Random", "Bayesian".
+    :vartype sampling_algorithm_type: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    :ivar rule: The specific type of random algorithm. Possible values include: "Random", "Sobol".
+    :vartype rule: str or ~azure.mgmt.machinelearningservices.models.RandomSamplingAlgorithmRule
+    :ivar seed: An optional integer to use as the seed for random number generation.
+    :vartype seed: int
+    """
+
+    _validation = {
+        'sampling_algorithm_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'sampling_algorithm_type': {'key': 'samplingAlgorithmType', 'type': 'str'},
+        'rule': {'key': 'rule', 'type': 'str'},
+        'seed': {'key': 'seed', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword rule: The specific type of random algorithm. Possible values include: "Random",
+         "Sobol".
+        :paramtype rule: str or ~azure.mgmt.machinelearningservices.models.RandomSamplingAlgorithmRule
+        :keyword seed: An optional integer to use as the seed for random number generation.
+        :paramtype seed: int
+        """
+        super(RandomSamplingAlgorithm, self).__init__(**kwargs)
+        self.sampling_algorithm_type = 'Random'  # type: str
+        self.rule = kwargs.get('rule', None)
+        self.seed = kwargs.get('seed', None)
+
+
+class RecurrenceSchedule(msrest.serialization.Model):
+    """RecurrenceSchedule.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar hours: Required. [Required] List of hours for the schedule.
+    :vartype hours: list[int]
+    :ivar minutes: Required. [Required] List of minutes for the schedule.
+    :vartype minutes: list[int]
+    :ivar month_days: List of month days for the schedule.
+    :vartype month_days: list[int]
+    :ivar week_days: List of days for the schedule.
+    :vartype week_days: list[str or ~azure.mgmt.machinelearningservices.models.WeekDay]
+    """
+
+    _validation = {
+        'hours': {'required': True},
+        'minutes': {'required': True},
+    }
+
+    _attribute_map = {
+        'hours': {'key': 'hours', 'type': '[int]'},
+        'minutes': {'key': 'minutes', 'type': '[int]'},
+        'month_days': {'key': 'monthDays', 'type': '[int]'},
+        'week_days': {'key': 'weekDays', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword hours: Required. [Required] List of hours for the schedule.
+        :paramtype hours: list[int]
+        :keyword minutes: Required. [Required] List of minutes for the schedule.
+        :paramtype minutes: list[int]
+        :keyword month_days: List of month days for the schedule.
+        :paramtype month_days: list[int]
+        :keyword week_days: List of days for the schedule.
+        :paramtype week_days: list[str or ~azure.mgmt.machinelearningservices.models.WeekDay]
+        """
+        super(RecurrenceSchedule, self).__init__(**kwargs)
+        self.hours = kwargs['hours']
+        self.minutes = kwargs['minutes']
+        self.month_days = kwargs.get('month_days', None)
+        self.week_days = kwargs.get('week_days', None)
+
+
+class RecurrenceTrigger(TriggerBase):
+    """RecurrenceTrigger.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+     https://en.wikipedia.org/wiki/ISO_8601.
+     Recommented format would be "2022-06-01T00:00:01"
+     If not present, the schedule will run indefinitely.
+    :vartype end_time: str
+    :ivar start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+     offset.
+    :vartype start_time: str
+    :ivar time_zone: Specifies time zone in which the schedule runs.
+     TimeZone should follow Windows time zone format. Refer:
+     https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+    :vartype time_zone: str
+    :ivar trigger_type: Required. [Required].Constant filled by server. Possible values include:
+     "Recurrence", "Cron".
+    :vartype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
+    :ivar frequency: Required. [Required] The frequency to trigger schedule. Possible values
+     include: "Minute", "Hour", "Day", "Week", "Month".
+    :vartype frequency: str or ~azure.mgmt.machinelearningservices.models.RecurrenceFrequency
+    :ivar interval: Required. [Required] Specifies schedule interval in conjunction with frequency.
+    :vartype interval: int
+    :ivar schedule: The recurrence schedule.
+    :vartype schedule: ~azure.mgmt.machinelearningservices.models.RecurrenceSchedule
+    """
+
+    _validation = {
+        'trigger_type': {'required': True},
+        'frequency': {'required': True},
+        'interval': {'required': True},
+    }
+
+    _attribute_map = {
+        'end_time': {'key': 'endTime', 'type': 'str'},
+        'start_time': {'key': 'startTime', 'type': 'str'},
+        'time_zone': {'key': 'timeZone', 'type': 'str'},
+        'trigger_type': {'key': 'triggerType', 'type': 'str'},
+        'frequency': {'key': 'frequency', 'type': 'str'},
+        'interval': {'key': 'interval', 'type': 'int'},
+        'schedule': {'key': 'schedule', 'type': 'RecurrenceSchedule'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+         https://en.wikipedia.org/wiki/ISO_8601.
+         Recommented format would be "2022-06-01T00:00:01"
+         If not present, the schedule will run indefinitely.
+        :paramtype end_time: str
+        :keyword start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+         offset.
+        :paramtype start_time: str
+        :keyword time_zone: Specifies time zone in which the schedule runs.
+         TimeZone should follow Windows time zone format. Refer:
+         https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+        :paramtype time_zone: str
+        :keyword frequency: Required. [Required] The frequency to trigger schedule. Possible values
+         include: "Minute", "Hour", "Day", "Week", "Month".
+        :paramtype frequency: str or ~azure.mgmt.machinelearningservices.models.RecurrenceFrequency
+        :keyword interval: Required. [Required] Specifies schedule interval in conjunction with
+         frequency.
+        :paramtype interval: int
+        :keyword schedule: The recurrence schedule.
+        :paramtype schedule: ~azure.mgmt.machinelearningservices.models.RecurrenceSchedule
+        """
+        super(RecurrenceTrigger, self).__init__(**kwargs)
+        self.trigger_type = 'Recurrence'  # type: str
+        self.frequency = kwargs['frequency']
+        self.interval = kwargs['interval']
+        self.schedule = kwargs.get('schedule', None)
+
+
+class RegenerateEndpointKeysRequest(msrest.serialization.Model):
+    """RegenerateEndpointKeysRequest.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar key_type: Required. [Required] Specification for which type of key to generate. Primary
+     or Secondary. Possible values include: "Primary", "Secondary".
+    :vartype key_type: str or ~azure.mgmt.machinelearningservices.models.KeyType
+    :ivar key_value: The value the key is set to.
+    :vartype key_value: str
+    """
+
+    _validation = {
+        'key_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'key_type': {'key': 'keyType', 'type': 'str'},
+        'key_value': {'key': 'keyValue', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword key_type: Required. [Required] Specification for which type of key to generate.
+         Primary or Secondary. Possible values include: "Primary", "Secondary".
+        :paramtype key_type: str or ~azure.mgmt.machinelearningservices.models.KeyType
+        :keyword key_value: The value the key is set to.
+        :paramtype key_value: str
+        """
+        super(RegenerateEndpointKeysRequest, self).__init__(**kwargs)
+        self.key_type = kwargs['key_type']
+        self.key_value = kwargs.get('key_value', None)
+
+
+class RegistryListCredentialsResult(msrest.serialization.Model):
+    """RegistryListCredentialsResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar location:
+    :vartype location: str
+    :ivar username:
+    :vartype username: str
+    :ivar passwords:
+    :vartype passwords: list[~azure.mgmt.machinelearningservices.models.Password]
+    """
+
+    _validation = {
+        'location': {'readonly': True},
+        'username': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'location': {'key': 'location', 'type': 'str'},
+        'username': {'key': 'username', 'type': 'str'},
+        'passwords': {'key': 'passwords', 'type': '[Password]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword passwords:
+        :paramtype passwords: list[~azure.mgmt.machinelearningservices.models.Password]
+        """
+        super(RegistryListCredentialsResult, self).__init__(**kwargs)
+        self.location = None
+        self.username = None
+        self.passwords = kwargs.get('passwords', None)
+
+
+class Regression(AutoMLVertical, TableVertical):
+    """Regression task in AutoML Table vertical.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar cv_split_column_names: Columns to use for CVSplit data.
+    :vartype cv_split_column_names: list[str]
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+    :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
+     when validation dataset is not provided.
+    :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar test_data: Test data input.
+    :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
+     purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype test_data_size: float
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar weight_column_name: The name of the sample weight column. Automated ML supports a
+     weighted column as an input, causing rows in the data to be weighted up or down.
+    :vartype weight_column_name: str
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for regression task. Possible values include:
+     "SpearmanCorrelation", "NormalizedRootMeanSquaredError", "R2Score",
+     "NormalizedMeanAbsoluteError".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.RegressionPrimaryMetrics
+    :ivar training_settings: Inputs for training phase for an AutoML Job.
+    :vartype training_settings:
+     ~azure.mgmt.machinelearningservices.models.RegressionTrainingSettings
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'TableVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'TableVerticalLimitSettings'},
+        'n_cross_validations': {'key': 'nCrossValidations', 'type': 'NCrossValidations'},
+        'test_data': {'key': 'testData', 'type': 'MLTableJobInput'},
+        'test_data_size': {'key': 'testDataSize', 'type': 'float'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+        'training_settings': {'key': 'trainingSettings', 'type': 'RegressionTrainingSettings'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword cv_split_column_names: Columns to use for CVSplit data.
+        :paramtype cv_split_column_names: list[str]
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+        :keyword n_cross_validations: Number of cross validation folds to be applied on training
+         dataset
+         when validation dataset is not provided.
+        :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword test_data: Test data input.
+        :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
+         purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype test_data_size: float
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword weight_column_name: The name of the sample weight column. Automated ML supports a
+         weighted column as an input, causing rows in the data to be weighted up or down.
+        :paramtype weight_column_name: str
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric for regression task. Possible values include:
+         "SpearmanCorrelation", "NormalizedRootMeanSquaredError", "R2Score",
+         "NormalizedMeanAbsoluteError".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.RegressionPrimaryMetrics
+        :keyword training_settings: Inputs for training phase for an AutoML Job.
+        :paramtype training_settings:
+         ~azure.mgmt.machinelearningservices.models.RegressionTrainingSettings
+        """
+        super(Regression, self).__init__(**kwargs)
+        self.cv_split_column_names = kwargs.get('cv_split_column_names', None)
+        self.featurization_settings = kwargs.get('featurization_settings', None)
+        self.limit_settings = kwargs.get('limit_settings', None)
+        self.n_cross_validations = kwargs.get('n_cross_validations', None)
+        self.test_data = kwargs.get('test_data', None)
+        self.test_data_size = kwargs.get('test_data_size', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.validation_data_size = kwargs.get('validation_data_size', None)
+        self.weight_column_name = kwargs.get('weight_column_name', None)
+        self.task_type = 'Regression'  # type: str
+        self.primary_metric = kwargs.get('primary_metric', None)
+        self.training_settings = kwargs.get('training_settings', None)
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.training_data = kwargs['training_data']
+
+
+class RegressionTrainingSettings(TrainingSettings):
+    """Regression Training related configuration.
+
+    :ivar enable_dnn_training: Enable recommendation of DNN models.
+    :vartype enable_dnn_training: bool
+    :ivar enable_model_explainability: Flag to turn on explainability on best model.
+    :vartype enable_model_explainability: bool
+    :ivar enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+    :vartype enable_onnx_compatible_models: bool
+    :ivar enable_stack_ensemble: Enable stack ensemble run.
+    :vartype enable_stack_ensemble: bool
+    :ivar enable_vote_ensemble: Enable voting ensemble run.
+    :vartype enable_vote_ensemble: bool
+    :ivar ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+     generation, multiple fitted models from the previous child runs are downloaded.
+     Configure this parameter with a higher value than 300 secs, if more time is needed.
+    :vartype ensemble_model_download_timeout: ~datetime.timedelta
+    :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+    :vartype stack_ensemble_settings:
+     ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    :ivar allowed_training_algorithms: Allowed models for regression task.
+    :vartype allowed_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.RegressionModels]
+    :ivar blocked_training_algorithms: Blocked models for regression task.
+    :vartype blocked_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.RegressionModels]
+    """
+
+    _attribute_map = {
+        'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
+        'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
+        'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
+        'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
+        'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
+        'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'duration'},
+        'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'StackEnsembleSettings'},
+        'allowed_training_algorithms': {'key': 'allowedTrainingAlgorithms', 'type': '[str]'},
+        'blocked_training_algorithms': {'key': 'blockedTrainingAlgorithms', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword enable_dnn_training: Enable recommendation of DNN models.
+        :paramtype enable_dnn_training: bool
+        :keyword enable_model_explainability: Flag to turn on explainability on best model.
+        :paramtype enable_model_explainability: bool
+        :keyword enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+        :paramtype enable_onnx_compatible_models: bool
+        :keyword enable_stack_ensemble: Enable stack ensemble run.
+        :paramtype enable_stack_ensemble: bool
+        :keyword enable_vote_ensemble: Enable voting ensemble run.
+        :paramtype enable_vote_ensemble: bool
+        :keyword ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+         generation, multiple fitted models from the previous child runs are downloaded.
+         Configure this parameter with a higher value than 300 secs, if more time is needed.
+        :paramtype ensemble_model_download_timeout: ~datetime.timedelta
+        :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+        :paramtype stack_ensemble_settings:
+         ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        :keyword allowed_training_algorithms: Allowed models for regression task.
+        :paramtype allowed_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.RegressionModels]
+        :keyword blocked_training_algorithms: Blocked models for regression task.
+        :paramtype blocked_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.RegressionModels]
+        """
+        super(RegressionTrainingSettings, self).__init__(**kwargs)
+        self.allowed_training_algorithms = kwargs.get('allowed_training_algorithms', None)
+        self.blocked_training_algorithms = kwargs.get('blocked_training_algorithms', None)
+
+
+class ResourceId(msrest.serialization.Model):
+    """Represents a resource ID. For example, for a subnet, it is the resource URL for the subnet.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Required. The ID of the resource.
+    :vartype id: str
+    """
+
+    _validation = {
+        'id': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword id: Required. The ID of the resource.
+        :paramtype id: str
+        """
+        super(ResourceId, self).__init__(**kwargs)
+        self.id = kwargs['id']
+
+
+class ResourceName(msrest.serialization.Model):
+    """The Resource Name.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The name of the resource.
+    :vartype value: str
+    :ivar localized_value: The localized name of the resource.
+    :vartype localized_value: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'localized_value': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': 'str'},
+        'localized_value': {'key': 'localizedValue', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ResourceName, self).__init__(**kwargs)
+        self.value = None
+        self.localized_value = None
+
+
+class ResourceQuota(msrest.serialization.Model):
+    """The quota assigned to a resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Specifies the resource ID.
+    :vartype id: str
+    :ivar aml_workspace_location: Region of the AML workspace in the id.
+    :vartype aml_workspace_location: str
+    :ivar type: Specifies the resource type.
+    :vartype type: str
+    :ivar name: Name of the resource.
+    :vartype name: ~azure.mgmt.machinelearningservices.models.ResourceName
+    :ivar limit: The maximum permitted quota of the resource.
+    :vartype limit: long
+    :ivar unit: An enum describing the unit of quota measurement. Possible values include: "Count".
+    :vartype unit: str or ~azure.mgmt.machinelearningservices.models.QuotaUnit
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'aml_workspace_location': {'readonly': True},
+        'type': {'readonly': True},
+        'name': {'readonly': True},
+        'limit': {'readonly': True},
+        'unit': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'aml_workspace_location': {'key': 'amlWorkspaceLocation', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'ResourceName'},
+        'limit': {'key': 'limit', 'type': 'long'},
+        'unit': {'key': 'unit', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ResourceQuota, self).__init__(**kwargs)
+        self.id = None
+        self.aml_workspace_location = None
+        self.type = None
+        self.name = None
+        self.limit = None
+        self.unit = None
+
+
+class Route(msrest.serialization.Model):
+    """Route.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar path: Required. [Required] The path for the route.
+    :vartype path: str
+    :ivar port: Required. [Required] The port for the route.
+    :vartype port: int
+    """
+
+    _validation = {
+        'path': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'port': {'required': True},
+    }
+
+    _attribute_map = {
+        'path': {'key': 'path', 'type': 'str'},
+        'port': {'key': 'port', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword path: Required. [Required] The path for the route.
+        :paramtype path: str
+        :keyword port: Required. [Required] The port for the route.
+        :paramtype port: int
+        """
+        super(Route, self).__init__(**kwargs)
+        self.path = kwargs['path']
+        self.port = kwargs['port']
+
+
+class SASAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """SASAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar credentials:
+    :vartype credentials:
+     ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionSharedAccessSignature
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+        'credentials': {'key': 'credentials', 'type': 'WorkspaceConnectionSharedAccessSignature'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword credentials:
+        :paramtype credentials:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionSharedAccessSignature
+        """
+        super(SASAuthTypeWorkspaceConnectionProperties, self).__init__(**kwargs)
+        self.auth_type = 'SAS'  # type: str
+        self.credentials = kwargs.get('credentials', None)
+
+
+class SasDatastoreCredentials(DatastoreCredentials):
+    """SAS datastore credentials configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    :ivar secrets: Required. [Required] Storage container secrets.
+    :vartype secrets: ~azure.mgmt.machinelearningservices.models.SasDatastoreSecrets
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+        'secrets': {'required': True},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+        'secrets': {'key': 'secrets', 'type': 'SasDatastoreSecrets'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword secrets: Required. [Required] Storage container secrets.
+        :paramtype secrets: ~azure.mgmt.machinelearningservices.models.SasDatastoreSecrets
+        """
+        super(SasDatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = 'Sas'  # type: str
+        self.secrets = kwargs['secrets']
+
+
+class SasDatastoreSecrets(DatastoreSecrets):
+    """Datastore SAS secrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate", "Sas",
+     "ServicePrincipal".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    :ivar sas_token: Storage container SAS token.
+    :vartype sas_token: str
+    """
+
+    _validation = {
+        'secrets_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'secrets_type': {'key': 'secretsType', 'type': 'str'},
+        'sas_token': {'key': 'sasToken', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword sas_token: Storage container SAS token.
+        :paramtype sas_token: str
+        """
+        super(SasDatastoreSecrets, self).__init__(**kwargs)
+        self.secrets_type = 'Sas'  # type: str
+        self.sas_token = kwargs.get('sas_token', None)
+
+
+class ScaleSettings(msrest.serialization.Model):
+    """scale settings for AML Compute.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar max_node_count: Required. Max number of nodes to use.
+    :vartype max_node_count: int
+    :ivar min_node_count: Min number of nodes to use.
+    :vartype min_node_count: int
+    :ivar node_idle_time_before_scale_down: Node Idle Time before scaling down amlCompute. This
+     string needs to be in the RFC Format.
+    :vartype node_idle_time_before_scale_down: ~datetime.timedelta
+    """
+
+    _validation = {
+        'max_node_count': {'required': True},
+    }
+
+    _attribute_map = {
+        'max_node_count': {'key': 'maxNodeCount', 'type': 'int'},
+        'min_node_count': {'key': 'minNodeCount', 'type': 'int'},
+        'node_idle_time_before_scale_down': {'key': 'nodeIdleTimeBeforeScaleDown', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword max_node_count: Required. Max number of nodes to use.
+        :paramtype max_node_count: int
+        :keyword min_node_count: Min number of nodes to use.
+        :paramtype min_node_count: int
+        :keyword node_idle_time_before_scale_down: Node Idle Time before scaling down amlCompute. This
+         string needs to be in the RFC Format.
+        :paramtype node_idle_time_before_scale_down: ~datetime.timedelta
+        """
+        super(ScaleSettings, self).__init__(**kwargs)
+        self.max_node_count = kwargs['max_node_count']
+        self.min_node_count = kwargs.get('min_node_count', 0)
+        self.node_idle_time_before_scale_down = kwargs.get('node_idle_time_before_scale_down', None)
+
+
+class ScaleSettingsInformation(msrest.serialization.Model):
+    """Desired scale settings for the amlCompute.
+
+    :ivar scale_settings: scale settings for AML Compute.
+    :vartype scale_settings: ~azure.mgmt.machinelearningservices.models.ScaleSettings
+    """
+
+    _attribute_map = {
+        'scale_settings': {'key': 'scaleSettings', 'type': 'ScaleSettings'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword scale_settings: scale settings for AML Compute.
+        :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.ScaleSettings
+        """
+        super(ScaleSettingsInformation, self).__init__(**kwargs)
+        self.scale_settings = kwargs.get('scale_settings', None)
+
+
+class Schedule(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ScheduleProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'ScheduleProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ScheduleProperties
+        """
+        super(Schedule, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class ScheduleBase(msrest.serialization.Model):
+    """ScheduleBase.
+
+    :ivar id: A system assigned id for the schedule.
+    :vartype id: str
+    :ivar provisioning_status: The current deployment state of schedule. Possible values include:
+     "Completed", "Provisioning", "Failed".
+    :vartype provisioning_status: str or
+     ~azure.mgmt.machinelearningservices.models.ScheduleProvisioningState
+    :ivar status: Is the schedule enabled or disabled?. Possible values include: "Enabled",
+     "Disabled".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.ScheduleStatus
+    """
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'provisioning_status': {'key': 'provisioningStatus', 'type': 'str'},
+        'status': {'key': 'status', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword id: A system assigned id for the schedule.
+        :paramtype id: str
+        :keyword provisioning_status: The current deployment state of schedule. Possible values
+         include: "Completed", "Provisioning", "Failed".
+        :paramtype provisioning_status: str or
+         ~azure.mgmt.machinelearningservices.models.ScheduleProvisioningState
+        :keyword status: Is the schedule enabled or disabled?. Possible values include: "Enabled",
+         "Disabled".
+        :paramtype status: str or ~azure.mgmt.machinelearningservices.models.ScheduleStatus
+        """
+        super(ScheduleBase, self).__init__(**kwargs)
+        self.id = kwargs.get('id', None)
+        self.provisioning_status = kwargs.get('provisioning_status', None)
+        self.status = kwargs.get('status', None)
+
+
+class ScheduleProperties(ResourceBase):
+    """Base definition of a schedule.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar action: Required. [Required] Specifies the action of the schedule.
+    :vartype action: ~azure.mgmt.machinelearningservices.models.ScheduleActionBase
+    :ivar display_name: Display name of schedule.
+    :vartype display_name: str
+    :ivar is_enabled: Is the schedule enabled?.
+    :vartype is_enabled: bool
+    :ivar provisioning_state: Provisioning state for the schedule. Possible values include:
+     "Creating", "Updating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ScheduleProvisioningStatus
+    :ivar trigger: Required. [Required] Specifies the trigger details.
+    :vartype trigger: ~azure.mgmt.machinelearningservices.models.TriggerBase
+    """
+
+    _validation = {
+        'action': {'required': True},
+        'provisioning_state': {'readonly': True},
+        'trigger': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'action': {'key': 'action', 'type': 'ScheduleActionBase'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'is_enabled': {'key': 'isEnabled', 'type': 'bool'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'trigger': {'key': 'trigger', 'type': 'TriggerBase'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword action: Required. [Required] Specifies the action of the schedule.
+        :paramtype action: ~azure.mgmt.machinelearningservices.models.ScheduleActionBase
+        :keyword display_name: Display name of schedule.
+        :paramtype display_name: str
+        :keyword is_enabled: Is the schedule enabled?.
+        :paramtype is_enabled: bool
+        :keyword trigger: Required. [Required] Specifies the trigger details.
+        :paramtype trigger: ~azure.mgmt.machinelearningservices.models.TriggerBase
+        """
+        super(ScheduleProperties, self).__init__(**kwargs)
+        self.action = kwargs['action']
+        self.display_name = kwargs.get('display_name', None)
+        self.is_enabled = kwargs.get('is_enabled', True)
+        self.provisioning_state = None
+        self.trigger = kwargs['trigger']
+
+
+class ScheduleResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of Schedule entities.
+
+    :ivar next_link: The link to the next page of Schedule objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type Schedule.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Schedule]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[Schedule]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of Schedule objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type Schedule.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.Schedule]
+        """
+        super(ScheduleResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class ScriptReference(msrest.serialization.Model):
+    """Script reference.
+
+    :ivar script_source: The storage source of the script: inline, workspace.
+    :vartype script_source: str
+    :ivar script_data: The location of scripts in the mounted volume.
+    :vartype script_data: str
+    :ivar script_arguments: Optional command line arguments passed to the script to run.
+    :vartype script_arguments: str
+    :ivar timeout: Optional time period passed to timeout command.
+    :vartype timeout: str
+    """
+
+    _attribute_map = {
+        'script_source': {'key': 'scriptSource', 'type': 'str'},
+        'script_data': {'key': 'scriptData', 'type': 'str'},
+        'script_arguments': {'key': 'scriptArguments', 'type': 'str'},
+        'timeout': {'key': 'timeout', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword script_source: The storage source of the script: inline, workspace.
+        :paramtype script_source: str
+        :keyword script_data: The location of scripts in the mounted volume.
+        :paramtype script_data: str
+        :keyword script_arguments: Optional command line arguments passed to the script to run.
+        :paramtype script_arguments: str
+        :keyword timeout: Optional time period passed to timeout command.
+        :paramtype timeout: str
+        """
+        super(ScriptReference, self).__init__(**kwargs)
+        self.script_source = kwargs.get('script_source', None)
+        self.script_data = kwargs.get('script_data', None)
+        self.script_arguments = kwargs.get('script_arguments', None)
+        self.timeout = kwargs.get('timeout', None)
+
+
+class ScriptsToExecute(msrest.serialization.Model):
+    """Customized setup scripts.
+
+    :ivar startup_script: Script that's run every time the machine starts.
+    :vartype startup_script: ~azure.mgmt.machinelearningservices.models.ScriptReference
+    :ivar creation_script: Script that's run only once during provision of the compute.
+    :vartype creation_script: ~azure.mgmt.machinelearningservices.models.ScriptReference
+    """
+
+    _attribute_map = {
+        'startup_script': {'key': 'startupScript', 'type': 'ScriptReference'},
+        'creation_script': {'key': 'creationScript', 'type': 'ScriptReference'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword startup_script: Script that's run every time the machine starts.
+        :paramtype startup_script: ~azure.mgmt.machinelearningservices.models.ScriptReference
+        :keyword creation_script: Script that's run only once during provision of the compute.
+        :paramtype creation_script: ~azure.mgmt.machinelearningservices.models.ScriptReference
+        """
+        super(ScriptsToExecute, self).__init__(**kwargs)
+        self.startup_script = kwargs.get('startup_script', None)
+        self.creation_script = kwargs.get('creation_script', None)
+
+
+class ServiceManagedResourcesSettings(msrest.serialization.Model):
+    """ServiceManagedResourcesSettings.
+
+    :ivar cosmos_db: The settings for the service managed cosmosdb account.
+    :vartype cosmos_db: ~azure.mgmt.machinelearningservices.models.CosmosDbSettings
+    """
+
+    _attribute_map = {
+        'cosmos_db': {'key': 'cosmosDb', 'type': 'CosmosDbSettings'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword cosmos_db: The settings for the service managed cosmosdb account.
+        :paramtype cosmos_db: ~azure.mgmt.machinelearningservices.models.CosmosDbSettings
+        """
+        super(ServiceManagedResourcesSettings, self).__init__(**kwargs)
+        self.cosmos_db = kwargs.get('cosmos_db', None)
+
+
+class ServicePrincipalDatastoreCredentials(DatastoreCredentials):
+    """Service Principal datastore credentials configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    :ivar authority_url: Authority URL used for authentication.
+    :vartype authority_url: str
+    :ivar client_id: Required. [Required] Service principal client ID.
+    :vartype client_id: str
+    :ivar resource_url: Resource the service principal has access to.
+    :vartype resource_url: str
+    :ivar secrets: Required. [Required] Service principal secrets.
+    :vartype secrets: ~azure.mgmt.machinelearningservices.models.ServicePrincipalDatastoreSecrets
+    :ivar tenant_id: Required. [Required] ID of the tenant to which the service principal belongs.
+    :vartype tenant_id: str
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+        'client_id': {'required': True},
+        'secrets': {'required': True},
+        'tenant_id': {'required': True},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+        'authority_url': {'key': 'authorityUrl', 'type': 'str'},
+        'client_id': {'key': 'clientId', 'type': 'str'},
+        'resource_url': {'key': 'resourceUrl', 'type': 'str'},
+        'secrets': {'key': 'secrets', 'type': 'ServicePrincipalDatastoreSecrets'},
+        'tenant_id': {'key': 'tenantId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword authority_url: Authority URL used for authentication.
+        :paramtype authority_url: str
+        :keyword client_id: Required. [Required] Service principal client ID.
+        :paramtype client_id: str
+        :keyword resource_url: Resource the service principal has access to.
+        :paramtype resource_url: str
+        :keyword secrets: Required. [Required] Service principal secrets.
+        :paramtype secrets: ~azure.mgmt.machinelearningservices.models.ServicePrincipalDatastoreSecrets
+        :keyword tenant_id: Required. [Required] ID of the tenant to which the service principal
+         belongs.
+        :paramtype tenant_id: str
+        """
+        super(ServicePrincipalDatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = 'ServicePrincipal'  # type: str
+        self.authority_url = kwargs.get('authority_url', None)
+        self.client_id = kwargs['client_id']
+        self.resource_url = kwargs.get('resource_url', None)
+        self.secrets = kwargs['secrets']
+        self.tenant_id = kwargs['tenant_id']
+
+
+class ServicePrincipalDatastoreSecrets(DatastoreSecrets):
+    """Datastore Service Principal secrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate", "Sas",
+     "ServicePrincipal".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    :ivar client_secret: Service principal secret.
+    :vartype client_secret: str
+    """
+
+    _validation = {
+        'secrets_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'secrets_type': {'key': 'secretsType', 'type': 'str'},
+        'client_secret': {'key': 'clientSecret', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword client_secret: Service principal secret.
+        :paramtype client_secret: str
+        """
+        super(ServicePrincipalDatastoreSecrets, self).__init__(**kwargs)
+        self.secrets_type = 'ServicePrincipal'  # type: str
+        self.client_secret = kwargs.get('client_secret', None)
+
+
+class SetupScripts(msrest.serialization.Model):
+    """Details of customized scripts to execute for setting up the cluster.
+
+    :ivar scripts: Customized setup scripts.
+    :vartype scripts: ~azure.mgmt.machinelearningservices.models.ScriptsToExecute
+    """
+
+    _attribute_map = {
+        'scripts': {'key': 'scripts', 'type': 'ScriptsToExecute'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword scripts: Customized setup scripts.
+        :paramtype scripts: ~azure.mgmt.machinelearningservices.models.ScriptsToExecute
+        """
+        super(SetupScripts, self).__init__(**kwargs)
+        self.scripts = kwargs.get('scripts', None)
+
+
+class SharedPrivateLinkResource(msrest.serialization.Model):
+    """SharedPrivateLinkResource.
+
+    :ivar name: Unique name of the private link.
+    :vartype name: str
+    :ivar private_link_resource_id: The resource id that private link links to.
+    :vartype private_link_resource_id: str
+    :ivar group_id: The private link resource group id.
+    :vartype group_id: str
+    :ivar request_message: Request message.
+    :vartype request_message: str
+    :ivar status: Indicates whether the connection has been Approved/Rejected/Removed by the owner
+     of the service. Possible values include: "Pending", "Approved", "Rejected", "Disconnected",
+     "Timeout".
+    :vartype status: str or
+     ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+    """
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'private_link_resource_id': {'key': 'properties.privateLinkResourceId', 'type': 'str'},
+        'group_id': {'key': 'properties.groupId', 'type': 'str'},
+        'request_message': {'key': 'properties.requestMessage', 'type': 'str'},
+        'status': {'key': 'properties.status', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword name: Unique name of the private link.
+        :paramtype name: str
+        :keyword private_link_resource_id: The resource id that private link links to.
+        :paramtype private_link_resource_id: str
+        :keyword group_id: The private link resource group id.
+        :paramtype group_id: str
+        :keyword request_message: Request message.
+        :paramtype request_message: str
+        :keyword status: Indicates whether the connection has been Approved/Rejected/Removed by the
+         owner of the service. Possible values include: "Pending", "Approved", "Rejected",
+         "Disconnected", "Timeout".
+        :paramtype status: str or
+         ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+        """
+        super(SharedPrivateLinkResource, self).__init__(**kwargs)
+        self.name = kwargs.get('name', None)
+        self.private_link_resource_id = kwargs.get('private_link_resource_id', None)
+        self.group_id = kwargs.get('group_id', None)
+        self.request_message = kwargs.get('request_message', None)
+        self.status = kwargs.get('status', None)
+
+
+class Sku(msrest.serialization.Model):
+    """The resource model definition representing SKU.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar name: Required. The name of the SKU. Ex - P3. It is typically a letter+number code.
+    :vartype name: str
+    :ivar tier: This field is required to be implemented by the Resource Provider if the service
+     has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+     "Standard", "Premium".
+    :vartype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+    :ivar size: The SKU size. When the name field is the combination of tier and some other value,
+     this would be the standalone code.
+    :vartype size: str
+    :ivar family: If the service has different generations of hardware, for the same SKU, then that
+     can be captured here.
+    :vartype family: str
+    :ivar capacity: If the SKU supports scale out/in then the capacity integer should be included.
+     If scale out/in is not possible for the resource this may be omitted.
+    :vartype capacity: int
+    """
+
+    _validation = {
+        'name': {'required': True},
+    }
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'tier': {'key': 'tier', 'type': 'str'},
+        'size': {'key': 'size', 'type': 'str'},
+        'family': {'key': 'family', 'type': 'str'},
+        'capacity': {'key': 'capacity', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword name: Required. The name of the SKU. Ex - P3. It is typically a letter+number code.
+        :paramtype name: str
+        :keyword tier: This field is required to be implemented by the Resource Provider if the service
+         has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+         "Standard", "Premium".
+        :paramtype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+        :keyword size: The SKU size. When the name field is the combination of tier and some other
+         value, this would be the standalone code.
+        :paramtype size: str
+        :keyword family: If the service has different generations of hardware, for the same SKU, then
+         that can be captured here.
+        :paramtype family: str
+        :keyword capacity: If the SKU supports scale out/in then the capacity integer should be
+         included. If scale out/in is not possible for the resource this may be omitted.
+        :paramtype capacity: int
+        """
+        super(Sku, self).__init__(**kwargs)
+        self.name = kwargs['name']
+        self.tier = kwargs.get('tier', None)
+        self.size = kwargs.get('size', None)
+        self.family = kwargs.get('family', None)
+        self.capacity = kwargs.get('capacity', None)
+
+
+class SkuCapacity(msrest.serialization.Model):
+    """SKU capacity information.
+
+    :ivar default: Gets or sets the default capacity.
+    :vartype default: int
+    :ivar maximum: Gets or sets the maximum.
+    :vartype maximum: int
+    :ivar minimum: Gets or sets the minimum.
+    :vartype minimum: int
+    :ivar scale_type: Gets or sets the type of the scale. Possible values include: "Automatic",
+     "Manual", "None".
+    :vartype scale_type: str or ~azure.mgmt.machinelearningservices.models.SkuScaleType
+    """
+
+    _attribute_map = {
+        'default': {'key': 'default', 'type': 'int'},
+        'maximum': {'key': 'maximum', 'type': 'int'},
+        'minimum': {'key': 'minimum', 'type': 'int'},
+        'scale_type': {'key': 'scaleType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword default: Gets or sets the default capacity.
+        :paramtype default: int
+        :keyword maximum: Gets or sets the maximum.
+        :paramtype maximum: int
+        :keyword minimum: Gets or sets the minimum.
+        :paramtype minimum: int
+        :keyword scale_type: Gets or sets the type of the scale. Possible values include: "Automatic",
+         "Manual", "None".
+        :paramtype scale_type: str or ~azure.mgmt.machinelearningservices.models.SkuScaleType
+        """
+        super(SkuCapacity, self).__init__(**kwargs)
+        self.default = kwargs.get('default', 0)
+        self.maximum = kwargs.get('maximum', 0)
+        self.minimum = kwargs.get('minimum', 0)
+        self.scale_type = kwargs.get('scale_type', None)
+
+
+class SkuResource(msrest.serialization.Model):
+    """Fulfills ARM Contract requirement to list all available SKUS for a resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar capacity: Gets or sets the Sku Capacity.
+    :vartype capacity: ~azure.mgmt.machinelearningservices.models.SkuCapacity
+    :ivar resource_type: The resource type name.
+    :vartype resource_type: str
+    :ivar sku: Gets or sets the Sku.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.SkuSetting
+    """
+
+    _validation = {
+        'resource_type': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'capacity': {'key': 'capacity', 'type': 'SkuCapacity'},
+        'resource_type': {'key': 'resourceType', 'type': 'str'},
+        'sku': {'key': 'sku', 'type': 'SkuSetting'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword capacity: Gets or sets the Sku Capacity.
+        :paramtype capacity: ~azure.mgmt.machinelearningservices.models.SkuCapacity
+        :keyword sku: Gets or sets the Sku.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.SkuSetting
+        """
+        super(SkuResource, self).__init__(**kwargs)
+        self.capacity = kwargs.get('capacity', None)
+        self.resource_type = None
+        self.sku = kwargs.get('sku', None)
+
+
+class SkuResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of SkuResource entities.
+
+    :ivar next_link: The link to the next page of SkuResource objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type SkuResource.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.SkuResource]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[SkuResource]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of SkuResource objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type SkuResource.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.SkuResource]
+        """
+        super(SkuResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = kwargs.get('next_link', None)
+        self.value = kwargs.get('value', None)
+
+
+class SkuSetting(msrest.serialization.Model):
+    """SkuSetting fulfills the need for stripped down SKU info in ARM contract.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar name: Required. [Required] The name of the SKU. Ex - P3. It is typically a letter+number
+     code.
+    :vartype name: str
+    :ivar tier: This field is required to be implemented by the Resource Provider if the service
+     has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+     "Standard", "Premium".
+    :vartype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+    """
+
+    _validation = {
+        'name': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'tier': {'key': 'tier', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword name: Required. [Required] The name of the SKU. Ex - P3. It is typically a
+         letter+number code.
+        :paramtype name: str
+        :keyword tier: This field is required to be implemented by the Resource Provider if the service
+         has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+         "Standard", "Premium".
+        :paramtype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+        """
+        super(SkuSetting, self).__init__(**kwargs)
+        self.name = kwargs['name']
+        self.tier = kwargs.get('tier', None)
+
+
+class SslConfiguration(msrest.serialization.Model):
+    """The ssl configuration for scoring.
+
+    :ivar status: Enable or disable ssl for scoring. Possible values include: "Disabled",
+     "Enabled", "Auto".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.SslConfigStatus
+    :ivar cert: Cert data.
+    :vartype cert: str
+    :ivar key: Key data.
+    :vartype key: str
+    :ivar cname: CNAME of the cert.
+    :vartype cname: str
+    :ivar leaf_domain_label: Leaf domain label of public endpoint.
+    :vartype leaf_domain_label: str
+    :ivar overwrite_existing_domain: Indicates whether to overwrite existing domain label.
+    :vartype overwrite_existing_domain: bool
+    """
+
+    _attribute_map = {
+        'status': {'key': 'status', 'type': 'str'},
+        'cert': {'key': 'cert', 'type': 'str'},
+        'key': {'key': 'key', 'type': 'str'},
+        'cname': {'key': 'cname', 'type': 'str'},
+        'leaf_domain_label': {'key': 'leafDomainLabel', 'type': 'str'},
+        'overwrite_existing_domain': {'key': 'overwriteExistingDomain', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword status: Enable or disable ssl for scoring. Possible values include: "Disabled",
+         "Enabled", "Auto".
+        :paramtype status: str or ~azure.mgmt.machinelearningservices.models.SslConfigStatus
+        :keyword cert: Cert data.
+        :paramtype cert: str
+        :keyword key: Key data.
+        :paramtype key: str
+        :keyword cname: CNAME of the cert.
+        :paramtype cname: str
+        :keyword leaf_domain_label: Leaf domain label of public endpoint.
+        :paramtype leaf_domain_label: str
+        :keyword overwrite_existing_domain: Indicates whether to overwrite existing domain label.
+        :paramtype overwrite_existing_domain: bool
+        """
+        super(SslConfiguration, self).__init__(**kwargs)
+        self.status = kwargs.get('status', None)
+        self.cert = kwargs.get('cert', None)
+        self.key = kwargs.get('key', None)
+        self.cname = kwargs.get('cname', None)
+        self.leaf_domain_label = kwargs.get('leaf_domain_label', None)
+        self.overwrite_existing_domain = kwargs.get('overwrite_existing_domain', None)
+
+
+class StackEnsembleSettings(msrest.serialization.Model):
+    """Advances setting to customize StackEnsemble run.
+
+    :ivar stack_meta_learner_k_wargs: Optional parameters to pass to the initializer of the
+     meta-learner.
+    :vartype stack_meta_learner_k_wargs: any
+    :ivar stack_meta_learner_train_percentage: Specifies the proportion of the training set (when
+     choosing train and validation type of training) to be reserved for training the meta-learner.
+     Default value is 0.2.
+    :vartype stack_meta_learner_train_percentage: float
+    :ivar stack_meta_learner_type: The meta-learner is a model trained on the output of the
+     individual heterogeneous models. Possible values include: "None", "LogisticRegression",
+     "LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
+     "LightGBMRegressor", "LinearRegression".
+    :vartype stack_meta_learner_type: str or
+     ~azure.mgmt.machinelearningservices.models.StackMetaLearnerType
+    """
+
+    _attribute_map = {
+        'stack_meta_learner_k_wargs': {'key': 'stackMetaLearnerKWargs', 'type': 'object'},
+        'stack_meta_learner_train_percentage': {'key': 'stackMetaLearnerTrainPercentage', 'type': 'float'},
+        'stack_meta_learner_type': {'key': 'stackMetaLearnerType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword stack_meta_learner_k_wargs: Optional parameters to pass to the initializer of the
+         meta-learner.
+        :paramtype stack_meta_learner_k_wargs: any
+        :keyword stack_meta_learner_train_percentage: Specifies the proportion of the training set
+         (when choosing train and validation type of training) to be reserved for training the
+         meta-learner. Default value is 0.2.
+        :paramtype stack_meta_learner_train_percentage: float
+        :keyword stack_meta_learner_type: The meta-learner is a model trained on the output of the
+         individual heterogeneous models. Possible values include: "None", "LogisticRegression",
+         "LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
+         "LightGBMRegressor", "LinearRegression".
+        :paramtype stack_meta_learner_type: str or
+         ~azure.mgmt.machinelearningservices.models.StackMetaLearnerType
+        """
+        super(StackEnsembleSettings, self).__init__(**kwargs)
+        self.stack_meta_learner_k_wargs = kwargs.get('stack_meta_learner_k_wargs', None)
+        self.stack_meta_learner_train_percentage = kwargs.get('stack_meta_learner_train_percentage', 0.2)
+        self.stack_meta_learner_type = kwargs.get('stack_meta_learner_type', None)
+
+
+class SweepJob(JobBaseProperties):
+    """Sweep job definition.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "AutoML", "Command", "Sweep", "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Possible values include: "NotStarted", "Starting",
+     "Provisioning", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
+     "Failed", "Canceled", "NotResponding", "Paused", "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar early_termination: Early termination policies enable canceling poor-performing runs
+     before they complete.
+    :vartype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+    :ivar inputs: Mapping of input data bindings used in the job.
+    :vartype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+    :ivar limits: Sweep Job limit.
+    :vartype limits: ~azure.mgmt.machinelearningservices.models.SweepJobLimits
+    :ivar objective: Required. [Required] Optimization objective.
+    :vartype objective: ~azure.mgmt.machinelearningservices.models.Objective
+    :ivar outputs: Mapping of output data bindings used in the job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar sampling_algorithm: Required. [Required] The hyperparameter sampling algorithm.
+    :vartype sampling_algorithm: ~azure.mgmt.machinelearningservices.models.SamplingAlgorithm
+    :ivar search_space: Required. [Required] A dictionary containing each parameter and its
+     distribution. The dictionary key is the name of the parameter.
+    :vartype search_space: any
+    :ivar trial: Required. [Required] Trial component definition.
+    :vartype trial: ~azure.mgmt.machinelearningservices.models.TrialComponent
+    """
+
+    _validation = {
+        'job_type': {'required': True},
+        'status': {'readonly': True},
+        'objective': {'required': True},
+        'sampling_algorithm': {'required': True},
+        'search_space': {'required': True},
+        'trial': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'component_id': {'key': 'componentId', 'type': 'str'},
+        'compute_id': {'key': 'computeId', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'experiment_name': {'key': 'experimentName', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'job_type': {'key': 'jobType', 'type': 'str'},
+        'services': {'key': 'services', 'type': '{JobService}'},
+        'status': {'key': 'status', 'type': 'str'},
+        'early_termination': {'key': 'earlyTermination', 'type': 'EarlyTerminationPolicy'},
+        'inputs': {'key': 'inputs', 'type': '{JobInput}'},
+        'limits': {'key': 'limits', 'type': 'SweepJobLimits'},
+        'objective': {'key': 'objective', 'type': 'Objective'},
+        'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
+        'sampling_algorithm': {'key': 'samplingAlgorithm', 'type': 'SamplingAlgorithm'},
+        'search_space': {'key': 'searchSpace', 'type': 'object'},
+        'trial': {'key': 'trial', 'type': 'TrialComponent'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword early_termination: Early termination policies enable canceling poor-performing runs
+         before they complete.
+        :paramtype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+        :keyword inputs: Mapping of input data bindings used in the job.
+        :paramtype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+        :keyword limits: Sweep Job limit.
+        :paramtype limits: ~azure.mgmt.machinelearningservices.models.SweepJobLimits
+        :keyword objective: Required. [Required] Optimization objective.
+        :paramtype objective: ~azure.mgmt.machinelearningservices.models.Objective
+        :keyword outputs: Mapping of output data bindings used in the job.
+        :paramtype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+        :keyword sampling_algorithm: Required. [Required] The hyperparameter sampling algorithm.
+        :paramtype sampling_algorithm: ~azure.mgmt.machinelearningservices.models.SamplingAlgorithm
+        :keyword search_space: Required. [Required] A dictionary containing each parameter and its
+         distribution. The dictionary key is the name of the parameter.
+        :paramtype search_space: any
+        :keyword trial: Required. [Required] Trial component definition.
+        :paramtype trial: ~azure.mgmt.machinelearningservices.models.TrialComponent
+        """
+        super(SweepJob, self).__init__(**kwargs)
+        self.job_type = 'Sweep'  # type: str
+        self.early_termination = kwargs.get('early_termination', None)
+        self.inputs = kwargs.get('inputs', None)
+        self.limits = kwargs.get('limits', None)
+        self.objective = kwargs['objective']
+        self.outputs = kwargs.get('outputs', None)
+        self.sampling_algorithm = kwargs['sampling_algorithm']
+        self.search_space = kwargs['search_space']
+        self.trial = kwargs['trial']
+
+
+class SweepJobLimits(JobLimits):
+    """Sweep Job limit class.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar job_limits_type: Required. [Required] JobLimit type.Constant filled by server. Possible
+     values include: "Command", "Sweep".
+    :vartype job_limits_type: str or ~azure.mgmt.machinelearningservices.models.JobLimitsType
+    :ivar timeout: The max run duration in ISO 8601 format, after which the job will be cancelled.
+     Only supports duration with precision as low as Seconds.
+    :vartype timeout: ~datetime.timedelta
+    :ivar max_concurrent_trials: Sweep Job max concurrent trials.
+    :vartype max_concurrent_trials: int
+    :ivar max_total_trials: Sweep Job max total trials.
+    :vartype max_total_trials: int
+    :ivar trial_timeout: Sweep Job Trial timeout value.
+    :vartype trial_timeout: ~datetime.timedelta
+    """
+
+    _validation = {
+        'job_limits_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'job_limits_type': {'key': 'jobLimitsType', 'type': 'str'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+        'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
+        'max_total_trials': {'key': 'maxTotalTrials', 'type': 'int'},
+        'trial_timeout': {'key': 'trialTimeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
+         cancelled. Only supports duration with precision as low as Seconds.
+        :paramtype timeout: ~datetime.timedelta
+        :keyword max_concurrent_trials: Sweep Job max concurrent trials.
+        :paramtype max_concurrent_trials: int
+        :keyword max_total_trials: Sweep Job max total trials.
+        :paramtype max_total_trials: int
+        :keyword trial_timeout: Sweep Job Trial timeout value.
+        :paramtype trial_timeout: ~datetime.timedelta
+        """
+        super(SweepJobLimits, self).__init__(**kwargs)
+        self.job_limits_type = 'Sweep'  # type: str
+        self.max_concurrent_trials = kwargs.get('max_concurrent_trials', None)
+        self.max_total_trials = kwargs.get('max_total_trials', None)
+        self.trial_timeout = kwargs.get('trial_timeout', None)
+
+
+class SynapseSpark(Compute):
+    """A SynapseSpark compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    :ivar properties:
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.SynapseSparkProperties
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+        'properties': {'key': 'properties', 'type': 'SynapseSparkProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        :keyword properties:
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.SynapseSparkProperties
+        """
+        super(SynapseSpark, self).__init__(**kwargs)
+        self.compute_type = 'SynapseSpark'  # type: str
+        self.properties = kwargs.get('properties', None)
+
+
+class SynapseSparkProperties(msrest.serialization.Model):
+    """SynapseSparkProperties.
+
+    :ivar auto_scale_properties: Auto scale properties.
+    :vartype auto_scale_properties: ~azure.mgmt.machinelearningservices.models.AutoScaleProperties
+    :ivar auto_pause_properties: Auto pause properties.
+    :vartype auto_pause_properties: ~azure.mgmt.machinelearningservices.models.AutoPauseProperties
+    :ivar spark_version: Spark version.
+    :vartype spark_version: str
+    :ivar node_count: The number of compute nodes currently assigned to the compute.
+    :vartype node_count: int
+    :ivar node_size: Node size.
+    :vartype node_size: str
+    :ivar node_size_family: Node size family.
+    :vartype node_size_family: str
+    :ivar subscription_id: Azure subscription identifier.
+    :vartype subscription_id: str
+    :ivar resource_group: Name of the resource group in which workspace is located.
+    :vartype resource_group: str
+    :ivar workspace_name: Name of Azure Machine Learning workspace.
+    :vartype workspace_name: str
+    :ivar pool_name: Pool name.
+    :vartype pool_name: str
+    """
+
+    _attribute_map = {
+        'auto_scale_properties': {'key': 'autoScaleProperties', 'type': 'AutoScaleProperties'},
+        'auto_pause_properties': {'key': 'autoPauseProperties', 'type': 'AutoPauseProperties'},
+        'spark_version': {'key': 'sparkVersion', 'type': 'str'},
+        'node_count': {'key': 'nodeCount', 'type': 'int'},
+        'node_size': {'key': 'nodeSize', 'type': 'str'},
+        'node_size_family': {'key': 'nodeSizeFamily', 'type': 'str'},
+        'subscription_id': {'key': 'subscriptionId', 'type': 'str'},
+        'resource_group': {'key': 'resourceGroup', 'type': 'str'},
+        'workspace_name': {'key': 'workspaceName', 'type': 'str'},
+        'pool_name': {'key': 'poolName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword auto_scale_properties: Auto scale properties.
+        :paramtype auto_scale_properties:
+         ~azure.mgmt.machinelearningservices.models.AutoScaleProperties
+        :keyword auto_pause_properties: Auto pause properties.
+        :paramtype auto_pause_properties:
+         ~azure.mgmt.machinelearningservices.models.AutoPauseProperties
+        :keyword spark_version: Spark version.
+        :paramtype spark_version: str
+        :keyword node_count: The number of compute nodes currently assigned to the compute.
+        :paramtype node_count: int
+        :keyword node_size: Node size.
+        :paramtype node_size: str
+        :keyword node_size_family: Node size family.
+        :paramtype node_size_family: str
+        :keyword subscription_id: Azure subscription identifier.
+        :paramtype subscription_id: str
+        :keyword resource_group: Name of the resource group in which workspace is located.
+        :paramtype resource_group: str
+        :keyword workspace_name: Name of Azure Machine Learning workspace.
+        :paramtype workspace_name: str
+        :keyword pool_name: Pool name.
+        :paramtype pool_name: str
+        """
+        super(SynapseSparkProperties, self).__init__(**kwargs)
+        self.auto_scale_properties = kwargs.get('auto_scale_properties', None)
+        self.auto_pause_properties = kwargs.get('auto_pause_properties', None)
+        self.spark_version = kwargs.get('spark_version', None)
+        self.node_count = kwargs.get('node_count', None)
+        self.node_size = kwargs.get('node_size', None)
+        self.node_size_family = kwargs.get('node_size_family', None)
+        self.subscription_id = kwargs.get('subscription_id', None)
+        self.resource_group = kwargs.get('resource_group', None)
+        self.workspace_name = kwargs.get('workspace_name', None)
+        self.pool_name = kwargs.get('pool_name', None)
+
+
+class SystemData(msrest.serialization.Model):
+    """Metadata pertaining to creation and last modification of the resource.
+
+    :ivar created_by: The identity that created the resource.
+    :vartype created_by: str
+    :ivar created_by_type: The type of identity that created the resource. Possible values include:
+     "User", "Application", "ManagedIdentity", "Key".
+    :vartype created_by_type: str or ~azure.mgmt.machinelearningservices.models.CreatedByType
+    :ivar created_at: The timestamp of resource creation (UTC).
+    :vartype created_at: ~datetime.datetime
+    :ivar last_modified_by: The identity that last modified the resource.
+    :vartype last_modified_by: str
+    :ivar last_modified_by_type: The type of identity that last modified the resource. Possible
+     values include: "User", "Application", "ManagedIdentity", "Key".
+    :vartype last_modified_by_type: str or ~azure.mgmt.machinelearningservices.models.CreatedByType
+    :ivar last_modified_at: The timestamp of resource last modification (UTC).
+    :vartype last_modified_at: ~datetime.datetime
+    """
+
+    _attribute_map = {
+        'created_by': {'key': 'createdBy', 'type': 'str'},
+        'created_by_type': {'key': 'createdByType', 'type': 'str'},
+        'created_at': {'key': 'createdAt', 'type': 'iso-8601'},
+        'last_modified_by': {'key': 'lastModifiedBy', 'type': 'str'},
+        'last_modified_by_type': {'key': 'lastModifiedByType', 'type': 'str'},
+        'last_modified_at': {'key': 'lastModifiedAt', 'type': 'iso-8601'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword created_by: The identity that created the resource.
+        :paramtype created_by: str
+        :keyword created_by_type: The type of identity that created the resource. Possible values
+         include: "User", "Application", "ManagedIdentity", "Key".
+        :paramtype created_by_type: str or ~azure.mgmt.machinelearningservices.models.CreatedByType
+        :keyword created_at: The timestamp of resource creation (UTC).
+        :paramtype created_at: ~datetime.datetime
+        :keyword last_modified_by: The identity that last modified the resource.
+        :paramtype last_modified_by: str
+        :keyword last_modified_by_type: The type of identity that last modified the resource. Possible
+         values include: "User", "Application", "ManagedIdentity", "Key".
+        :paramtype last_modified_by_type: str or
+         ~azure.mgmt.machinelearningservices.models.CreatedByType
+        :keyword last_modified_at: The timestamp of resource last modification (UTC).
+        :paramtype last_modified_at: ~datetime.datetime
+        """
+        super(SystemData, self).__init__(**kwargs)
+        self.created_by = kwargs.get('created_by', None)
+        self.created_by_type = kwargs.get('created_by_type', None)
+        self.created_at = kwargs.get('created_at', None)
+        self.last_modified_by = kwargs.get('last_modified_by', None)
+        self.last_modified_by_type = kwargs.get('last_modified_by_type', None)
+        self.last_modified_at = kwargs.get('last_modified_at', None)
+
+
+class SystemService(msrest.serialization.Model):
+    """A system service running on a compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar system_service_type: The type of this system service.
+    :vartype system_service_type: str
+    :ivar public_ip_address: Public IP address.
+    :vartype public_ip_address: str
+    :ivar version: The version for this type.
+    :vartype version: str
+    """
+
+    _validation = {
+        'system_service_type': {'readonly': True},
+        'public_ip_address': {'readonly': True},
+        'version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'system_service_type': {'key': 'systemServiceType', 'type': 'str'},
+        'public_ip_address': {'key': 'publicIpAddress', 'type': 'str'},
+        'version': {'key': 'version', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(SystemService, self).__init__(**kwargs)
+        self.system_service_type = None
+        self.public_ip_address = None
+        self.version = None
+
+
+class TableVerticalFeaturizationSettings(FeaturizationSettings):
+    """Featurization Configuration.
+
+    :ivar dataset_language: Dataset language, useful for the text data.
+    :vartype dataset_language: str
+    :ivar blocked_transformers: These transformers shall not be used in featurization.
+    :vartype blocked_transformers: list[str or
+     ~azure.mgmt.machinelearningservices.models.BlockedTransformers]
+    :ivar column_name_and_types: Dictionary of column name and its type (int, float, string,
+     datetime etc).
+    :vartype column_name_and_types: dict[str, str]
+    :ivar enable_dnn_featurization: Determines whether to use Dnn based featurizers for data
+     featurization.
+    :vartype enable_dnn_featurization: bool
+    :ivar mode: Featurization mode - User can keep the default 'Auto' mode and AutoML will take
+     care of necessary transformation of the data in featurization phase.
+     If 'Off' is selected then no featurization is done.
+     If 'Custom' is selected then user can specify additional inputs to customize how featurization
+     is done. Possible values include: "Auto", "Custom", "Off".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.FeaturizationMode
+    :ivar transformer_params: User can specify additional transformers to be used along with the
+     columns to which it would be applied and parameters for the transformer constructor.
+    :vartype transformer_params: dict[str,
+     list[~azure.mgmt.machinelearningservices.models.ColumnTransformer]]
+    """
+
+    _attribute_map = {
+        'dataset_language': {'key': 'datasetLanguage', 'type': 'str'},
+        'blocked_transformers': {'key': 'blockedTransformers', 'type': '[str]'},
+        'column_name_and_types': {'key': 'columnNameAndTypes', 'type': '{str}'},
+        'enable_dnn_featurization': {'key': 'enableDnnFeaturization', 'type': 'bool'},
+        'mode': {'key': 'mode', 'type': 'str'},
+        'transformer_params': {'key': 'transformerParams', 'type': '{[ColumnTransformer]}'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword dataset_language: Dataset language, useful for the text data.
+        :paramtype dataset_language: str
+        :keyword blocked_transformers: These transformers shall not be used in featurization.
+        :paramtype blocked_transformers: list[str or
+         ~azure.mgmt.machinelearningservices.models.BlockedTransformers]
+        :keyword column_name_and_types: Dictionary of column name and its type (int, float, string,
+         datetime etc).
+        :paramtype column_name_and_types: dict[str, str]
+        :keyword enable_dnn_featurization: Determines whether to use Dnn based featurizers for data
+         featurization.
+        :paramtype enable_dnn_featurization: bool
+        :keyword mode: Featurization mode - User can keep the default 'Auto' mode and AutoML will take
+         care of necessary transformation of the data in featurization phase.
+         If 'Off' is selected then no featurization is done.
+         If 'Custom' is selected then user can specify additional inputs to customize how featurization
+         is done. Possible values include: "Auto", "Custom", "Off".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.FeaturizationMode
+        :keyword transformer_params: User can specify additional transformers to be used along with the
+         columns to which it would be applied and parameters for the transformer constructor.
+        :paramtype transformer_params: dict[str,
+         list[~azure.mgmt.machinelearningservices.models.ColumnTransformer]]
+        """
+        super(TableVerticalFeaturizationSettings, self).__init__(**kwargs)
+        self.blocked_transformers = kwargs.get('blocked_transformers', None)
+        self.column_name_and_types = kwargs.get('column_name_and_types', None)
+        self.enable_dnn_featurization = kwargs.get('enable_dnn_featurization', False)
+        self.mode = kwargs.get('mode', None)
+        self.transformer_params = kwargs.get('transformer_params', None)
+
+
+class TableVerticalLimitSettings(msrest.serialization.Model):
+    """Job execution constraints.
+
+    :ivar enable_early_termination: Enable early termination, determines whether or not if
+     AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
+    :vartype enable_early_termination: bool
+    :ivar exit_score: Exit score for the AutoML job.
+    :vartype exit_score: float
+    :ivar max_concurrent_trials: Maximum Concurrent iterations.
+    :vartype max_concurrent_trials: int
+    :ivar max_cores_per_trial: Max cores per iteration.
+    :vartype max_cores_per_trial: int
+    :ivar max_trials: Number of iterations.
+    :vartype max_trials: int
+    :ivar timeout: AutoML job timeout.
+    :vartype timeout: ~datetime.timedelta
+    :ivar trial_timeout: Iteration timeout.
+    :vartype trial_timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'enable_early_termination': {'key': 'enableEarlyTermination', 'type': 'bool'},
+        'exit_score': {'key': 'exitScore', 'type': 'float'},
+        'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
+        'max_cores_per_trial': {'key': 'maxCoresPerTrial', 'type': 'int'},
+        'max_trials': {'key': 'maxTrials', 'type': 'int'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+        'trial_timeout': {'key': 'trialTimeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword enable_early_termination: Enable early termination, determines whether or not if
+         AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
+        :paramtype enable_early_termination: bool
+        :keyword exit_score: Exit score for the AutoML job.
+        :paramtype exit_score: float
+        :keyword max_concurrent_trials: Maximum Concurrent iterations.
+        :paramtype max_concurrent_trials: int
+        :keyword max_cores_per_trial: Max cores per iteration.
+        :paramtype max_cores_per_trial: int
+        :keyword max_trials: Number of iterations.
+        :paramtype max_trials: int
+        :keyword timeout: AutoML job timeout.
+        :paramtype timeout: ~datetime.timedelta
+        :keyword trial_timeout: Iteration timeout.
+        :paramtype trial_timeout: ~datetime.timedelta
+        """
+        super(TableVerticalLimitSettings, self).__init__(**kwargs)
+        self.enable_early_termination = kwargs.get('enable_early_termination', True)
+        self.exit_score = kwargs.get('exit_score', None)
+        self.max_concurrent_trials = kwargs.get('max_concurrent_trials', 1)
+        self.max_cores_per_trial = kwargs.get('max_cores_per_trial', -1)
+        self.max_trials = kwargs.get('max_trials', 1000)
+        self.timeout = kwargs.get('timeout', "PT6H")
+        self.trial_timeout = kwargs.get('trial_timeout', "PT30M")
+
+
+class TargetUtilizationScaleSettings(OnlineScaleSettings):
+    """TargetUtilizationScaleSettings.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar scale_type: Required. [Required] Type of deployment scaling algorithm.Constant filled by
+     server. Possible values include: "Default", "TargetUtilization".
+    :vartype scale_type: str or ~azure.mgmt.machinelearningservices.models.ScaleType
+    :ivar max_instances: The maximum number of instances that the deployment can scale to. The
+     quota will be reserved for max_instances.
+    :vartype max_instances: int
+    :ivar min_instances: The minimum number of instances to always be present.
+    :vartype min_instances: int
+    :ivar polling_interval: The polling interval in ISO 8691 format. Only supports duration with
+     precision as low as Seconds.
+    :vartype polling_interval: ~datetime.timedelta
+    :ivar target_utilization_percentage: Target CPU usage for the autoscaler.
+    :vartype target_utilization_percentage: int
+    """
+
+    _validation = {
+        'scale_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'scale_type': {'key': 'scaleType', 'type': 'str'},
+        'max_instances': {'key': 'maxInstances', 'type': 'int'},
+        'min_instances': {'key': 'minInstances', 'type': 'int'},
+        'polling_interval': {'key': 'pollingInterval', 'type': 'duration'},
+        'target_utilization_percentage': {'key': 'targetUtilizationPercentage', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword max_instances: The maximum number of instances that the deployment can scale to. The
+         quota will be reserved for max_instances.
+        :paramtype max_instances: int
+        :keyword min_instances: The minimum number of instances to always be present.
+        :paramtype min_instances: int
+        :keyword polling_interval: The polling interval in ISO 8691 format. Only supports duration with
+         precision as low as Seconds.
+        :paramtype polling_interval: ~datetime.timedelta
+        :keyword target_utilization_percentage: Target CPU usage for the autoscaler.
+        :paramtype target_utilization_percentage: int
+        """
+        super(TargetUtilizationScaleSettings, self).__init__(**kwargs)
+        self.scale_type = 'TargetUtilization'  # type: str
+        self.max_instances = kwargs.get('max_instances', 1)
+        self.min_instances = kwargs.get('min_instances', 1)
+        self.polling_interval = kwargs.get('polling_interval', "PT1S")
+        self.target_utilization_percentage = kwargs.get('target_utilization_percentage', 70)
+
+
+class TensorFlow(DistributionConfiguration):
+    """TensorFlow distribution configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar distribution_type: Required. [Required] Specifies the type of distribution
+     framework.Constant filled by server. Possible values include: "PyTorch", "TensorFlow", "Mpi".
+    :vartype distribution_type: str or ~azure.mgmt.machinelearningservices.models.DistributionType
+    :ivar parameter_server_count: Number of parameter server tasks.
+    :vartype parameter_server_count: int
+    :ivar worker_count: Number of workers. If not specified, will default to the instance count.
+    :vartype worker_count: int
+    """
+
+    _validation = {
+        'distribution_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'distribution_type': {'key': 'distributionType', 'type': 'str'},
+        'parameter_server_count': {'key': 'parameterServerCount', 'type': 'int'},
+        'worker_count': {'key': 'workerCount', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword parameter_server_count: Number of parameter server tasks.
+        :paramtype parameter_server_count: int
+        :keyword worker_count: Number of workers. If not specified, will default to the instance count.
+        :paramtype worker_count: int
+        """
+        super(TensorFlow, self).__init__(**kwargs)
+        self.distribution_type = 'TensorFlow'  # type: str
+        self.parameter_server_count = kwargs.get('parameter_server_count', 0)
+        self.worker_count = kwargs.get('worker_count', None)
+
+
+class TextClassification(AutoMLVertical, NlpVertical):
+    """Text Classification task in AutoML NLP vertical.
+NLP - Natural Language Processing.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-Classification task. Possible values include:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'NlpVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'NlpVerticalLimitSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric for Text-Classification task. Possible values include:
+         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+         "PrecisionScoreWeighted".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+        """
+        super(TextClassification, self).__init__(**kwargs)
+        self.featurization_settings = kwargs.get('featurization_settings', None)
+        self.limit_settings = kwargs.get('limit_settings', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.task_type = 'TextClassification'  # type: str
+        self.primary_metric = kwargs.get('primary_metric', None)
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.training_data = kwargs['training_data']
+
+
+class TextClassificationMultilabel(AutoMLVertical, NlpVertical):
+    """Text Classification Multilabel task in AutoML NLP vertical.
+NLP - Natural Language Processing.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-Classification-Multilabel task.
+     Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
+     Possible values include: "AUCWeighted", "Accuracy", "NormMacroRecall",
+     "AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", "IOU".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+        'primary_metric': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'NlpVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'NlpVerticalLimitSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        """
+        super(TextClassificationMultilabel, self).__init__(**kwargs)
+        self.featurization_settings = kwargs.get('featurization_settings', None)
+        self.limit_settings = kwargs.get('limit_settings', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.task_type = 'TextClassificationMultilabel'  # type: str
+        self.primary_metric = None
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.training_data = kwargs['training_data']
+
+
+class TextNer(AutoMLVertical, NlpVertical):
+    """Text-NER task in AutoML NLP vertical.
+NER - Named Entity Recognition.
+NLP - Natural Language Processing.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-NER task.
+     Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly. Possible
+     values include: "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+        'primary_metric': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'NlpVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'NlpVerticalLimitSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        """
+        super(TextNer, self).__init__(**kwargs)
+        self.featurization_settings = kwargs.get('featurization_settings', None)
+        self.limit_settings = kwargs.get('limit_settings', None)
+        self.validation_data = kwargs.get('validation_data', None)
+        self.task_type = 'TextNER'  # type: str
+        self.primary_metric = None
+        self.log_verbosity = kwargs.get('log_verbosity', None)
+        self.target_column_name = kwargs.get('target_column_name', None)
+        self.training_data = kwargs['training_data']
+
+
+class TrialComponent(msrest.serialization.Model):
+    """Trial component definition.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar code_id: ARM resource ID of the code asset.
+    :vartype code_id: str
+    :ivar command: Required. [Required] The command to execute on startup of the job. eg. "python
+     train.py".
+    :vartype command: str
+    :ivar distribution: Distribution configuration of the job. If set, this should be one of Mpi,
+     Tensorflow, PyTorch, or null.
+    :vartype distribution: ~azure.mgmt.machinelearningservices.models.DistributionConfiguration
+    :ivar environment_id: Required. [Required] The ARM resource ID of the Environment specification
+     for the job.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables included in the job.
+    :vartype environment_variables: dict[str, str]
+    :ivar resources: Compute Resource configuration for the job.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+    """
+
+    _validation = {
+        'command': {'required': True, 'min_length': 1, 'pattern': r'[a-zA-Z0-9_]'},
+        'environment_id': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'code_id': {'key': 'codeId', 'type': 'str'},
+        'command': {'key': 'command', 'type': 'str'},
+        'distribution': {'key': 'distribution', 'type': 'DistributionConfiguration'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'resources': {'key': 'resources', 'type': 'JobResourceConfiguration'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword code_id: ARM resource ID of the code asset.
+        :paramtype code_id: str
+        :keyword command: Required. [Required] The command to execute on startup of the job. eg.
+         "python train.py".
+        :paramtype command: str
+        :keyword distribution: Distribution configuration of the job. If set, this should be one of
+         Mpi, Tensorflow, PyTorch, or null.
+        :paramtype distribution: ~azure.mgmt.machinelearningservices.models.DistributionConfiguration
+        :keyword environment_id: Required. [Required] The ARM resource ID of the Environment
+         specification for the job.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables included in the job.
+        :paramtype environment_variables: dict[str, str]
+        :keyword resources: Compute Resource configuration for the job.
+        :paramtype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+        """
+        super(TrialComponent, self).__init__(**kwargs)
+        self.code_id = kwargs.get('code_id', None)
+        self.command = kwargs['command']
+        self.distribution = kwargs.get('distribution', None)
+        self.environment_id = kwargs['environment_id']
+        self.environment_variables = kwargs.get('environment_variables', None)
+        self.resources = kwargs.get('resources', None)
+
+
+class TritonModelJobInput(JobInput, AssetJobInput):
+    """TritonModelJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(TritonModelJobInput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs['uri']
+        self.job_input_type = 'triton_model'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class TritonModelJobOutput(JobOutput, AssetJobOutput):
+    """TritonModelJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(TritonModelJobOutput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs.get('uri', None)
+        self.job_output_type = 'triton_model'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class TruncationSelectionPolicy(EarlyTerminationPolicy):
+    """Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar delay_evaluation: Number of intervals by which to delay the first evaluation.
+    :vartype delay_evaluation: int
+    :ivar evaluation_interval: Interval (number of runs) between policy evaluations.
+    :vartype evaluation_interval: int
+    :ivar policy_type: Required. [Required] Name of policy configuration.Constant filled by server.
+     Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
+    :vartype policy_type: str or
+     ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicyType
+    :ivar truncation_percentage: The percentage of runs to cancel at each evaluation interval.
+    :vartype truncation_percentage: int
+    """
+
+    _validation = {
+        'policy_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
+        'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
+        'policy_type': {'key': 'policyType', 'type': 'str'},
+        'truncation_percentage': {'key': 'truncationPercentage', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
+        :paramtype delay_evaluation: int
+        :keyword evaluation_interval: Interval (number of runs) between policy evaluations.
+        :paramtype evaluation_interval: int
+        :keyword truncation_percentage: The percentage of runs to cancel at each evaluation interval.
+        :paramtype truncation_percentage: int
+        """
+        super(TruncationSelectionPolicy, self).__init__(**kwargs)
+        self.policy_type = 'TruncationSelection'  # type: str
+        self.truncation_percentage = kwargs.get('truncation_percentage', 0)
+
+
+class UpdateWorkspaceQuotas(msrest.serialization.Model):
+    """The properties for update Quota response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Specifies the resource ID.
+    :vartype id: str
+    :ivar type: Specifies the resource type.
+    :vartype type: str
+    :ivar limit: The maximum permitted quota of the resource.
+    :vartype limit: long
+    :ivar unit: An enum describing the unit of quota measurement. Possible values include: "Count".
+    :vartype unit: str or ~azure.mgmt.machinelearningservices.models.QuotaUnit
+    :ivar status: Status of update workspace quota. Possible values include: "Undefined",
+     "Success", "Failure", "InvalidQuotaBelowClusterMinimum",
+     "InvalidQuotaExceedsSubscriptionLimit", "InvalidVMFamilyName", "OperationNotSupportedForSku",
+     "OperationNotEnabledForRegion".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.Status
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'type': {'readonly': True},
+        'unit': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'limit': {'key': 'limit', 'type': 'long'},
+        'unit': {'key': 'unit', 'type': 'str'},
+        'status': {'key': 'status', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword limit: The maximum permitted quota of the resource.
+        :paramtype limit: long
+        :keyword status: Status of update workspace quota. Possible values include: "Undefined",
+         "Success", "Failure", "InvalidQuotaBelowClusterMinimum",
+         "InvalidQuotaExceedsSubscriptionLimit", "InvalidVMFamilyName", "OperationNotSupportedForSku",
+         "OperationNotEnabledForRegion".
+        :paramtype status: str or ~azure.mgmt.machinelearningservices.models.Status
+        """
+        super(UpdateWorkspaceQuotas, self).__init__(**kwargs)
+        self.id = None
+        self.type = None
+        self.limit = kwargs.get('limit', None)
+        self.unit = None
+        self.status = kwargs.get('status', None)
+
+
+class UpdateWorkspaceQuotasResult(msrest.serialization.Model):
+    """The result of update workspace quota.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of workspace quota update result.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.UpdateWorkspaceQuotas]
+    :ivar next_link: The URI to fetch the next page of workspace quota update result. Call
+     ListNext() with this to fetch the next page of Workspace Quota update result.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[UpdateWorkspaceQuotas]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(UpdateWorkspaceQuotasResult, self).__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class UriFileDataVersion(DataVersionBaseProperties):
+    """uri-file data version entity.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar data_type: Required. [Required] Specifies the type of data.Constant filled by server.
+     Possible values include: "uri_file", "uri_folder", "mltable".
+    :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+    :ivar data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+    :vartype data_uri: str
+    """
+
+    _validation = {
+        'data_type': {'required': True},
+        'data_uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'data_type': {'key': 'dataType', 'type': 'str'},
+        'data_uri': {'key': 'dataUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+        :paramtype data_uri: str
+        """
+        super(UriFileDataVersion, self).__init__(**kwargs)
+        self.data_type = 'uri_file'  # type: str
+
+
+class UriFileJobInput(JobInput, AssetJobInput):
+    """UriFileJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(UriFileJobInput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs['uri']
+        self.job_input_type = 'uri_file'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class UriFileJobOutput(JobOutput, AssetJobOutput):
+    """UriFileJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(UriFileJobOutput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs.get('uri', None)
+        self.job_output_type = 'uri_file'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class UriFolderDataVersion(DataVersionBaseProperties):
+    """uri-folder data version entity.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar data_type: Required. [Required] Specifies the type of data.Constant filled by server.
+     Possible values include: "uri_file", "uri_folder", "mltable".
+    :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+    :ivar data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+    :vartype data_uri: str
+    """
+
+    _validation = {
+        'data_type': {'required': True},
+        'data_uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'data_type': {'key': 'dataType', 'type': 'str'},
+        'data_uri': {'key': 'dataUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+        :paramtype data_uri: str
+        """
+        super(UriFolderDataVersion, self).__init__(**kwargs)
+        self.data_type = 'uri_folder'  # type: str
+
+
+class UriFolderJobInput(JobInput, AssetJobInput):
+    """UriFolderJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(UriFolderJobInput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs['uri']
+        self.job_input_type = 'uri_folder'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class UriFolderJobOutput(JobOutput, AssetJobOutput):
+    """UriFolderJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(UriFolderJobOutput, self).__init__(**kwargs)
+        self.mode = kwargs.get('mode', None)
+        self.uri = kwargs.get('uri', None)
+        self.job_output_type = 'uri_folder'  # type: str
+        self.description = kwargs.get('description', None)
+
+
+class Usage(msrest.serialization.Model):
+    """Describes AML Resource Usage.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Specifies the resource ID.
+    :vartype id: str
+    :ivar aml_workspace_location: Region of the AML workspace in the id.
+    :vartype aml_workspace_location: str
+    :ivar type: Specifies the resource type.
+    :vartype type: str
+    :ivar unit: An enum describing the unit of usage measurement. Possible values include: "Count".
+    :vartype unit: str or ~azure.mgmt.machinelearningservices.models.UsageUnit
+    :ivar current_value: The current usage of the resource.
+    :vartype current_value: long
+    :ivar limit: The maximum permitted usage of the resource.
+    :vartype limit: long
+    :ivar name: The name of the type of usage.
+    :vartype name: ~azure.mgmt.machinelearningservices.models.UsageName
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'aml_workspace_location': {'readonly': True},
+        'type': {'readonly': True},
+        'unit': {'readonly': True},
+        'current_value': {'readonly': True},
+        'limit': {'readonly': True},
+        'name': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'aml_workspace_location': {'key': 'amlWorkspaceLocation', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'unit': {'key': 'unit', 'type': 'str'},
+        'current_value': {'key': 'currentValue', 'type': 'long'},
+        'limit': {'key': 'limit', 'type': 'long'},
+        'name': {'key': 'name', 'type': 'UsageName'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(Usage, self).__init__(**kwargs)
+        self.id = None
+        self.aml_workspace_location = None
+        self.type = None
+        self.unit = None
+        self.current_value = None
+        self.limit = None
+        self.name = None
+
+
+class UsageName(msrest.serialization.Model):
+    """The Usage Names.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The name of the resource.
+    :vartype value: str
+    :ivar localized_value: The localized name of the resource.
+    :vartype localized_value: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'localized_value': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': 'str'},
+        'localized_value': {'key': 'localizedValue', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(UsageName, self).__init__(**kwargs)
+        self.value = None
+        self.localized_value = None
+
+
+class UserAccountCredentials(msrest.serialization.Model):
+    """Settings for user account that gets created on each on the nodes of a compute.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar admin_user_name: Required. Name of the administrator user account which can be used to
+     SSH to nodes.
+    :vartype admin_user_name: str
+    :ivar admin_user_ssh_public_key: SSH public key of the administrator user account.
+    :vartype admin_user_ssh_public_key: str
+    :ivar admin_user_password: Password of the administrator user account.
+    :vartype admin_user_password: str
+    """
+
+    _validation = {
+        'admin_user_name': {'required': True},
+    }
+
+    _attribute_map = {
+        'admin_user_name': {'key': 'adminUserName', 'type': 'str'},
+        'admin_user_ssh_public_key': {'key': 'adminUserSshPublicKey', 'type': 'str'},
+        'admin_user_password': {'key': 'adminUserPassword', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword admin_user_name: Required. Name of the administrator user account which can be used to
+         SSH to nodes.
+        :paramtype admin_user_name: str
+        :keyword admin_user_ssh_public_key: SSH public key of the administrator user account.
+        :paramtype admin_user_ssh_public_key: str
+        :keyword admin_user_password: Password of the administrator user account.
+        :paramtype admin_user_password: str
+        """
+        super(UserAccountCredentials, self).__init__(**kwargs)
+        self.admin_user_name = kwargs['admin_user_name']
+        self.admin_user_ssh_public_key = kwargs.get('admin_user_ssh_public_key', None)
+        self.admin_user_password = kwargs.get('admin_user_password', None)
+
+
+class UserAssignedIdentity(msrest.serialization.Model):
+    """User assigned identity properties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar principal_id: The principal ID of the assigned identity.
+    :vartype principal_id: str
+    :ivar client_id: The client ID of the assigned identity.
+    :vartype client_id: str
+    """
+
+    _validation = {
+        'principal_id': {'readonly': True},
+        'client_id': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'principal_id': {'key': 'principalId', 'type': 'str'},
+        'client_id': {'key': 'clientId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(UserAssignedIdentity, self).__init__(**kwargs)
+        self.principal_id = None
+        self.client_id = None
+
+
+class UserIdentity(IdentityConfiguration):
+    """User identity configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar identity_type: Required. [Required] Specifies the type of identity framework.Constant
+     filled by server. Possible values include: "Managed", "AMLToken", "UserIdentity".
+    :vartype identity_type: str or
+     ~azure.mgmt.machinelearningservices.models.IdentityConfigurationType
+    """
+
+    _validation = {
+        'identity_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'identity_type': {'key': 'identityType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(UserIdentity, self).__init__(**kwargs)
+        self.identity_type = 'UserIdentity'  # type: str
+
+
+class UsernamePasswordAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """UsernamePasswordAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar credentials:
+    :vartype credentials:
+     ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionUsernamePassword
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+        'credentials': {'key': 'credentials', 'type': 'WorkspaceConnectionUsernamePassword'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword credentials:
+        :paramtype credentials:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionUsernamePassword
+        """
+        super(UsernamePasswordAuthTypeWorkspaceConnectionProperties, self).__init__(**kwargs)
+        self.auth_type = 'UsernamePassword'  # type: str
+        self.credentials = kwargs.get('credentials', None)
+
+
+class VirtualMachineSchema(msrest.serialization.Model):
+    """VirtualMachineSchema.
+
+    :ivar properties:
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.VirtualMachineSchemaProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'VirtualMachineSchemaProperties'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties:
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSchemaProperties
+        """
+        super(VirtualMachineSchema, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+
+
+class VirtualMachine(Compute, VirtualMachineSchema):
+    """A Machine Learning compute based on Azure Virtual Machines.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties:
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.VirtualMachineSchemaProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'VirtualMachineSchemaProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties:
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSchemaProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(VirtualMachine, self).__init__(**kwargs)
+        self.properties = kwargs.get('properties', None)
+        self.compute_type = 'VirtualMachine'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = kwargs.get('description', None)
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = kwargs.get('resource_id', None)
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class VirtualMachineImage(msrest.serialization.Model):
+    """Virtual Machine image for Windows AML Compute.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Required. Virtual Machine image path.
+    :vartype id: str
+    """
+
+    _validation = {
+        'id': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword id: Required. Virtual Machine image path.
+        :paramtype id: str
+        """
+        super(VirtualMachineImage, self).__init__(**kwargs)
+        self.id = kwargs['id']
+
+
+class VirtualMachineSchemaProperties(msrest.serialization.Model):
+    """VirtualMachineSchemaProperties.
+
+    :ivar virtual_machine_size: Virtual Machine size.
+    :vartype virtual_machine_size: str
+    :ivar ssh_port: Port open for ssh connections.
+    :vartype ssh_port: int
+    :ivar notebook_server_port: Notebook server port open for ssh connections.
+    :vartype notebook_server_port: int
+    :ivar address: Public IP address of the virtual machine.
+    :vartype address: str
+    :ivar administrator_account: Admin credentials for virtual machine.
+    :vartype administrator_account:
+     ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+    :ivar is_notebook_instance_compute: Indicates whether this compute will be used for running
+     notebooks.
+    :vartype is_notebook_instance_compute: bool
+    """
+
+    _attribute_map = {
+        'virtual_machine_size': {'key': 'virtualMachineSize', 'type': 'str'},
+        'ssh_port': {'key': 'sshPort', 'type': 'int'},
+        'notebook_server_port': {'key': 'notebookServerPort', 'type': 'int'},
+        'address': {'key': 'address', 'type': 'str'},
+        'administrator_account': {'key': 'administratorAccount', 'type': 'VirtualMachineSshCredentials'},
+        'is_notebook_instance_compute': {'key': 'isNotebookInstanceCompute', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword virtual_machine_size: Virtual Machine size.
+        :paramtype virtual_machine_size: str
+        :keyword ssh_port: Port open for ssh connections.
+        :paramtype ssh_port: int
+        :keyword notebook_server_port: Notebook server port open for ssh connections.
+        :paramtype notebook_server_port: int
+        :keyword address: Public IP address of the virtual machine.
+        :paramtype address: str
+        :keyword administrator_account: Admin credentials for virtual machine.
+        :paramtype administrator_account:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+        :keyword is_notebook_instance_compute: Indicates whether this compute will be used for running
+         notebooks.
+        :paramtype is_notebook_instance_compute: bool
+        """
+        super(VirtualMachineSchemaProperties, self).__init__(**kwargs)
+        self.virtual_machine_size = kwargs.get('virtual_machine_size', None)
+        self.ssh_port = kwargs.get('ssh_port', None)
+        self.notebook_server_port = kwargs.get('notebook_server_port', None)
+        self.address = kwargs.get('address', None)
+        self.administrator_account = kwargs.get('administrator_account', None)
+        self.is_notebook_instance_compute = kwargs.get('is_notebook_instance_compute', None)
+
+
+class VirtualMachineSecretsSchema(msrest.serialization.Model):
+    """VirtualMachineSecretsSchema.
+
+    :ivar administrator_account: Admin credentials for virtual machine.
+    :vartype administrator_account:
+     ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+    """
+
+    _attribute_map = {
+        'administrator_account': {'key': 'administratorAccount', 'type': 'VirtualMachineSshCredentials'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword administrator_account: Admin credentials for virtual machine.
+        :paramtype administrator_account:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+        """
+        super(VirtualMachineSecretsSchema, self).__init__(**kwargs)
+        self.administrator_account = kwargs.get('administrator_account', None)
+
+
+class VirtualMachineSecrets(ComputeSecrets, VirtualMachineSecretsSchema):
+    """Secrets related to a Machine Learning compute based on AKS.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar administrator_account: Admin credentials for virtual machine.
+    :vartype administrator_account:
+     ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'administrator_account': {'key': 'administratorAccount', 'type': 'VirtualMachineSshCredentials'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword administrator_account: Admin credentials for virtual machine.
+        :paramtype administrator_account:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+        """
+        super(VirtualMachineSecrets, self).__init__(**kwargs)
+        self.administrator_account = kwargs.get('administrator_account', None)
+        self.compute_type = 'VirtualMachine'  # type: str
+
+
+class VirtualMachineSize(msrest.serialization.Model):
+    """Describes the properties of a VM size.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar name: The name of the virtual machine size.
+    :vartype name: str
+    :ivar family: The family name of the virtual machine size.
+    :vartype family: str
+    :ivar v_cp_us: The number of vCPUs supported by the virtual machine size.
+    :vartype v_cp_us: int
+    :ivar gpus: The number of gPUs supported by the virtual machine size.
+    :vartype gpus: int
+    :ivar os_vhd_size_mb: The OS VHD disk size, in MB, allowed by the virtual machine size.
+    :vartype os_vhd_size_mb: int
+    :ivar max_resource_volume_mb: The resource volume size, in MB, allowed by the virtual machine
+     size.
+    :vartype max_resource_volume_mb: int
+    :ivar memory_gb: The amount of memory, in GB, supported by the virtual machine size.
+    :vartype memory_gb: float
+    :ivar low_priority_capable: Specifies if the virtual machine size supports low priority VMs.
+    :vartype low_priority_capable: bool
+    :ivar premium_io: Specifies if the virtual machine size supports premium IO.
+    :vartype premium_io: bool
+    :ivar estimated_vm_prices: The estimated price information for using a VM.
+    :vartype estimated_vm_prices: ~azure.mgmt.machinelearningservices.models.EstimatedVMPrices
+    :ivar supported_compute_types: Specifies the compute types supported by the virtual machine
+     size.
+    :vartype supported_compute_types: list[str]
+    """
+
+    _validation = {
+        'name': {'readonly': True},
+        'family': {'readonly': True},
+        'v_cp_us': {'readonly': True},
+        'gpus': {'readonly': True},
+        'os_vhd_size_mb': {'readonly': True},
+        'max_resource_volume_mb': {'readonly': True},
+        'memory_gb': {'readonly': True},
+        'low_priority_capable': {'readonly': True},
+        'premium_io': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'family': {'key': 'family', 'type': 'str'},
+        'v_cp_us': {'key': 'vCPUs', 'type': 'int'},
+        'gpus': {'key': 'gpus', 'type': 'int'},
+        'os_vhd_size_mb': {'key': 'osVhdSizeMB', 'type': 'int'},
+        'max_resource_volume_mb': {'key': 'maxResourceVolumeMB', 'type': 'int'},
+        'memory_gb': {'key': 'memoryGB', 'type': 'float'},
+        'low_priority_capable': {'key': 'lowPriorityCapable', 'type': 'bool'},
+        'premium_io': {'key': 'premiumIO', 'type': 'bool'},
+        'estimated_vm_prices': {'key': 'estimatedVMPrices', 'type': 'EstimatedVMPrices'},
+        'supported_compute_types': {'key': 'supportedComputeTypes', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword estimated_vm_prices: The estimated price information for using a VM.
+        :paramtype estimated_vm_prices: ~azure.mgmt.machinelearningservices.models.EstimatedVMPrices
+        :keyword supported_compute_types: Specifies the compute types supported by the virtual machine
+         size.
+        :paramtype supported_compute_types: list[str]
+        """
+        super(VirtualMachineSize, self).__init__(**kwargs)
+        self.name = None
+        self.family = None
+        self.v_cp_us = None
+        self.gpus = None
+        self.os_vhd_size_mb = None
+        self.max_resource_volume_mb = None
+        self.memory_gb = None
+        self.low_priority_capable = None
+        self.premium_io = None
+        self.estimated_vm_prices = kwargs.get('estimated_vm_prices', None)
+        self.supported_compute_types = kwargs.get('supported_compute_types', None)
+
+
+class VirtualMachineSizeListResult(msrest.serialization.Model):
+    """The List Virtual Machine size operation response.
+
+    :ivar value: The list of virtual machine sizes supported by AmlCompute.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.VirtualMachineSize]
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[VirtualMachineSize]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: The list of virtual machine sizes supported by AmlCompute.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.VirtualMachineSize]
+        """
+        super(VirtualMachineSizeListResult, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+
+
+class VirtualMachineSshCredentials(msrest.serialization.Model):
+    """Admin credentials for virtual machine.
+
+    :ivar username: Username of admin account.
+    :vartype username: str
+    :ivar password: Password of admin account.
+    :vartype password: str
+    :ivar public_key_data: Public key data.
+    :vartype public_key_data: str
+    :ivar private_key_data: Private key data.
+    :vartype private_key_data: str
+    """
+
+    _attribute_map = {
+        'username': {'key': 'username', 'type': 'str'},
+        'password': {'key': 'password', 'type': 'str'},
+        'public_key_data': {'key': 'publicKeyData', 'type': 'str'},
+        'private_key_data': {'key': 'privateKeyData', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword username: Username of admin account.
+        :paramtype username: str
+        :keyword password: Password of admin account.
+        :paramtype password: str
+        :keyword public_key_data: Public key data.
+        :paramtype public_key_data: str
+        :keyword private_key_data: Private key data.
+        :paramtype private_key_data: str
+        """
+        super(VirtualMachineSshCredentials, self).__init__(**kwargs)
+        self.username = kwargs.get('username', None)
+        self.password = kwargs.get('password', None)
+        self.public_key_data = kwargs.get('public_key_data', None)
+        self.private_key_data = kwargs.get('private_key_data', None)
+
+
+class Workspace(Resource):
+    """An object that represents a machine learning workspace.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar identity: The identity of the resource.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar location: Specifies the location of the resource.
+    :vartype location: str
+    :ivar tags: A set of tags. Contains resource tags defined as key/value pairs.
+    :vartype tags: dict[str, str]
+    :ivar sku: The sku of the workspace.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    :ivar workspace_id: The immutable id associated with this workspace.
+    :vartype workspace_id: str
+    :ivar description: The description of this workspace.
+    :vartype description: str
+    :ivar friendly_name: The friendly name for this workspace. This name in mutable.
+    :vartype friendly_name: str
+    :ivar key_vault: ARM id of the key vault associated with this workspace. This cannot be changed
+     once the workspace has been created.
+    :vartype key_vault: str
+    :ivar application_insights: ARM id of the application insights associated with this workspace.
+    :vartype application_insights: str
+    :ivar container_registry: ARM id of the container registry associated with this workspace.
+    :vartype container_registry: str
+    :ivar storage_account: ARM id of the storage account associated with this workspace. This
+     cannot be changed once the workspace has been created.
+    :vartype storage_account: str
+    :ivar discovery_url: Url for the discovery service to identify regional endpoints for machine
+     learning experimentation services.
+    :vartype discovery_url: str
+    :ivar provisioning_state: The current deployment state of workspace resource. The
+     provisioningState is to indicate states for resource provisioning. Possible values include:
+     "Unknown", "Updating", "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar encryption: The encryption settings of Azure ML workspace.
+    :vartype encryption: ~azure.mgmt.machinelearningservices.models.EncryptionProperty
+    :ivar hbi_workspace: The flag to signal HBI data in the workspace and reduce diagnostic data
+     collected by the service.
+    :vartype hbi_workspace: bool
+    :ivar service_provisioned_resource_group: The name of the managed resource group created by
+     workspace RP in customer subscription if the workspace is CMK workspace.
+    :vartype service_provisioned_resource_group: str
+    :ivar private_link_count: Count of private connections in the workspace.
+    :vartype private_link_count: int
+    :ivar image_build_compute: The compute name for image build.
+    :vartype image_build_compute: str
+    :ivar allow_public_access_when_behind_vnet: The flag to indicate whether to allow public access
+     when behind VNet.
+    :vartype allow_public_access_when_behind_vnet: bool
+    :ivar public_network_access: Whether requests from Public Network are allowed. Possible values
+     include: "Enabled", "Disabled".
+    :vartype public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.PublicNetworkAccess
+    :ivar private_endpoint_connections: The list of private endpoint connections in the workspace.
+    :vartype private_endpoint_connections:
+     list[~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection]
+    :ivar shared_private_link_resources: The list of shared private link resources in this
+     workspace.
+    :vartype shared_private_link_resources:
+     list[~azure.mgmt.machinelearningservices.models.SharedPrivateLinkResource]
+    :ivar notebook_info: The notebook info of Azure ML workspace.
+    :vartype notebook_info: ~azure.mgmt.machinelearningservices.models.NotebookResourceInfo
+    :ivar service_managed_resources_settings: The service managed resource settings.
+    :vartype service_managed_resources_settings:
+     ~azure.mgmt.machinelearningservices.models.ServiceManagedResourcesSettings
+    :ivar primary_user_assigned_identity: The user assigned identity resource id that represents
+     the workspace identity.
+    :vartype primary_user_assigned_identity: str
+    :ivar tenant_id: The tenant id associated with this workspace.
+    :vartype tenant_id: str
+    :ivar storage_hns_enabled: If the storage associated with the workspace has hierarchical
+     namespace(HNS) enabled.
+    :vartype storage_hns_enabled: bool
+    :ivar ml_flow_tracking_uri: The URI associated with this workspace that machine learning flow
+     must point at to set up tracking.
+    :vartype ml_flow_tracking_uri: str
+    :ivar v1_legacy_mode: Enabling v1_legacy_mode may prevent you from using features provided by
+     the v2 API.
+    :vartype v1_legacy_mode: bool
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'workspace_id': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'service_provisioned_resource_group': {'readonly': True},
+        'private_link_count': {'readonly': True},
+        'private_endpoint_connections': {'readonly': True},
+        'notebook_info': {'readonly': True},
+        'tenant_id': {'readonly': True},
+        'storage_hns_enabled': {'readonly': True},
+        'ml_flow_tracking_uri': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'location': {'key': 'location', 'type': 'str'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+        'workspace_id': {'key': 'properties.workspaceId', 'type': 'str'},
+        'description': {'key': 'properties.description', 'type': 'str'},
+        'friendly_name': {'key': 'properties.friendlyName', 'type': 'str'},
+        'key_vault': {'key': 'properties.keyVault', 'type': 'str'},
+        'application_insights': {'key': 'properties.applicationInsights', 'type': 'str'},
+        'container_registry': {'key': 'properties.containerRegistry', 'type': 'str'},
+        'storage_account': {'key': 'properties.storageAccount', 'type': 'str'},
+        'discovery_url': {'key': 'properties.discoveryUrl', 'type': 'str'},
+        'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'},
+        'encryption': {'key': 'properties.encryption', 'type': 'EncryptionProperty'},
+        'hbi_workspace': {'key': 'properties.hbiWorkspace', 'type': 'bool'},
+        'service_provisioned_resource_group': {'key': 'properties.serviceProvisionedResourceGroup', 'type': 'str'},
+        'private_link_count': {'key': 'properties.privateLinkCount', 'type': 'int'},
+        'image_build_compute': {'key': 'properties.imageBuildCompute', 'type': 'str'},
+        'allow_public_access_when_behind_vnet': {'key': 'properties.allowPublicAccessWhenBehindVnet', 'type': 'bool'},
+        'public_network_access': {'key': 'properties.publicNetworkAccess', 'type': 'str'},
+        'private_endpoint_connections': {'key': 'properties.privateEndpointConnections', 'type': '[PrivateEndpointConnection]'},
+        'shared_private_link_resources': {'key': 'properties.sharedPrivateLinkResources', 'type': '[SharedPrivateLinkResource]'},
+        'notebook_info': {'key': 'properties.notebookInfo', 'type': 'NotebookResourceInfo'},
+        'service_managed_resources_settings': {'key': 'properties.serviceManagedResourcesSettings', 'type': 'ServiceManagedResourcesSettings'},
+        'primary_user_assigned_identity': {'key': 'properties.primaryUserAssignedIdentity', 'type': 'str'},
+        'tenant_id': {'key': 'properties.tenantId', 'type': 'str'},
+        'storage_hns_enabled': {'key': 'properties.storageHnsEnabled', 'type': 'bool'},
+        'ml_flow_tracking_uri': {'key': 'properties.mlFlowTrackingUri', 'type': 'str'},
+        'v1_legacy_mode': {'key': 'properties.v1LegacyMode', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword identity: The identity of the resource.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword location: Specifies the location of the resource.
+        :paramtype location: str
+        :keyword tags: A set of tags. Contains resource tags defined as key/value pairs.
+        :paramtype tags: dict[str, str]
+        :keyword sku: The sku of the workspace.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        :keyword description: The description of this workspace.
+        :paramtype description: str
+        :keyword friendly_name: The friendly name for this workspace. This name in mutable.
+        :paramtype friendly_name: str
+        :keyword key_vault: ARM id of the key vault associated with this workspace. This cannot be
+         changed once the workspace has been created.
+        :paramtype key_vault: str
+        :keyword application_insights: ARM id of the application insights associated with this
+         workspace.
+        :paramtype application_insights: str
+        :keyword container_registry: ARM id of the container registry associated with this workspace.
+        :paramtype container_registry: str
+        :keyword storage_account: ARM id of the storage account associated with this workspace. This
+         cannot be changed once the workspace has been created.
+        :paramtype storage_account: str
+        :keyword discovery_url: Url for the discovery service to identify regional endpoints for
+         machine learning experimentation services.
+        :paramtype discovery_url: str
+        :keyword encryption: The encryption settings of Azure ML workspace.
+        :paramtype encryption: ~azure.mgmt.machinelearningservices.models.EncryptionProperty
+        :keyword hbi_workspace: The flag to signal HBI data in the workspace and reduce diagnostic data
+         collected by the service.
+        :paramtype hbi_workspace: bool
+        :keyword image_build_compute: The compute name for image build.
+        :paramtype image_build_compute: str
+        :keyword allow_public_access_when_behind_vnet: The flag to indicate whether to allow public
+         access when behind VNet.
+        :paramtype allow_public_access_when_behind_vnet: bool
+        :keyword public_network_access: Whether requests from Public Network are allowed. Possible
+         values include: "Enabled", "Disabled".
+        :paramtype public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.PublicNetworkAccess
+        :keyword shared_private_link_resources: The list of shared private link resources in this
+         workspace.
+        :paramtype shared_private_link_resources:
+         list[~azure.mgmt.machinelearningservices.models.SharedPrivateLinkResource]
+        :keyword service_managed_resources_settings: The service managed resource settings.
+        :paramtype service_managed_resources_settings:
+         ~azure.mgmt.machinelearningservices.models.ServiceManagedResourcesSettings
+        :keyword primary_user_assigned_identity: The user assigned identity resource id that represents
+         the workspace identity.
+        :paramtype primary_user_assigned_identity: str
+        :keyword v1_legacy_mode: Enabling v1_legacy_mode may prevent you from using features provided
+         by the v2 API.
+        :paramtype v1_legacy_mode: bool
+        """
+        super(Workspace, self).__init__(**kwargs)
+        self.identity = kwargs.get('identity', None)
+        self.location = kwargs.get('location', None)
+        self.tags = kwargs.get('tags', None)
+        self.sku = kwargs.get('sku', None)
+        self.workspace_id = None
+        self.description = kwargs.get('description', None)
+        self.friendly_name = kwargs.get('friendly_name', None)
+        self.key_vault = kwargs.get('key_vault', None)
+        self.application_insights = kwargs.get('application_insights', None)
+        self.container_registry = kwargs.get('container_registry', None)
+        self.storage_account = kwargs.get('storage_account', None)
+        self.discovery_url = kwargs.get('discovery_url', None)
+        self.provisioning_state = None
+        self.encryption = kwargs.get('encryption', None)
+        self.hbi_workspace = kwargs.get('hbi_workspace', False)
+        self.service_provisioned_resource_group = None
+        self.private_link_count = None
+        self.image_build_compute = kwargs.get('image_build_compute', None)
+        self.allow_public_access_when_behind_vnet = kwargs.get('allow_public_access_when_behind_vnet', False)
+        self.public_network_access = kwargs.get('public_network_access', None)
+        self.private_endpoint_connections = None
+        self.shared_private_link_resources = kwargs.get('shared_private_link_resources', None)
+        self.notebook_info = None
+        self.service_managed_resources_settings = kwargs.get('service_managed_resources_settings', None)
+        self.primary_user_assigned_identity = kwargs.get('primary_user_assigned_identity', None)
+        self.tenant_id = None
+        self.storage_hns_enabled = None
+        self.ml_flow_tracking_uri = None
+        self.v1_legacy_mode = kwargs.get('v1_legacy_mode', False)
+
+
+class WorkspaceConnectionManagedIdentity(msrest.serialization.Model):
+    """WorkspaceConnectionManagedIdentity.
+
+    :ivar resource_id:
+    :vartype resource_id: str
+    :ivar client_id:
+    :vartype client_id: str
+    """
+
+    _attribute_map = {
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'client_id': {'key': 'clientId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword resource_id:
+        :paramtype resource_id: str
+        :keyword client_id:
+        :paramtype client_id: str
+        """
+        super(WorkspaceConnectionManagedIdentity, self).__init__(**kwargs)
+        self.resource_id = kwargs.get('resource_id', None)
+        self.client_id = kwargs.get('client_id', None)
+
+
+class WorkspaceConnectionPersonalAccessToken(msrest.serialization.Model):
+    """WorkspaceConnectionPersonalAccessToken.
+
+    :ivar pat:
+    :vartype pat: str
+    """
+
+    _attribute_map = {
+        'pat': {'key': 'pat', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword pat:
+        :paramtype pat: str
+        """
+        super(WorkspaceConnectionPersonalAccessToken, self).__init__(**kwargs)
+        self.pat = kwargs.get('pat', None)
+
+
+class WorkspaceConnectionPropertiesV2BasicResource(Resource):
+    """WorkspaceConnectionPropertiesV2BasicResource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'WorkspaceConnectionPropertiesV2'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword properties: Required.
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2
+        """
+        super(WorkspaceConnectionPropertiesV2BasicResource, self).__init__(**kwargs)
+        self.properties = kwargs['properties']
+
+
+class WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult(msrest.serialization.Model):
+    """WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value:
+    :vartype value:
+     list[~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource]
+    :ivar next_link:
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[WorkspaceConnectionPropertiesV2BasicResource]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value:
+        :paramtype value:
+         list[~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource]
+        """
+        super(WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+        self.next_link = None
+
+
+class WorkspaceConnectionSharedAccessSignature(msrest.serialization.Model):
+    """WorkspaceConnectionSharedAccessSignature.
+
+    :ivar sas:
+    :vartype sas: str
+    """
+
+    _attribute_map = {
+        'sas': {'key': 'sas', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword sas:
+        :paramtype sas: str
+        """
+        super(WorkspaceConnectionSharedAccessSignature, self).__init__(**kwargs)
+        self.sas = kwargs.get('sas', None)
+
+
+class WorkspaceConnectionUsernamePassword(msrest.serialization.Model):
+    """WorkspaceConnectionUsernamePassword.
+
+    :ivar username:
+    :vartype username: str
+    :ivar password:
+    :vartype password: str
+    """
+
+    _attribute_map = {
+        'username': {'key': 'username', 'type': 'str'},
+        'password': {'key': 'password', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword username:
+        :paramtype username: str
+        :keyword password:
+        :paramtype password: str
+        """
+        super(WorkspaceConnectionUsernamePassword, self).__init__(**kwargs)
+        self.username = kwargs.get('username', None)
+        self.password = kwargs.get('password', None)
+
+
+class WorkspaceListResult(msrest.serialization.Model):
+    """The result of a request to list machine learning workspaces.
+
+    :ivar value: The list of machine learning workspaces. Since this list may be incomplete, the
+     nextLink field should be used to request the next list of machine learning workspaces.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Workspace]
+    :ivar next_link: The URI that can be used to request the next list of machine learning
+     workspaces.
+    :vartype next_link: str
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[Workspace]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword value: The list of machine learning workspaces. Since this list may be incomplete, the
+         nextLink field should be used to request the next list of machine learning workspaces.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.Workspace]
+        :keyword next_link: The URI that can be used to request the next list of machine learning
+         workspaces.
+        :paramtype next_link: str
+        """
+        super(WorkspaceListResult, self).__init__(**kwargs)
+        self.value = kwargs.get('value', None)
+        self.next_link = kwargs.get('next_link', None)
+
+
+class WorkspaceUpdateParameters(msrest.serialization.Model):
+    """The parameters for updating a machine learning workspace.
+
+    :ivar tags: A set of tags. The resource tags for the machine learning workspace.
+    :vartype tags: dict[str, str]
+    :ivar sku: The sku of the workspace.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    :ivar identity: The identity of the resource.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar description: The description of this workspace.
+    :vartype description: str
+    :ivar friendly_name: The friendly name for this workspace.
+    :vartype friendly_name: str
+    :ivar image_build_compute: The compute name for image build.
+    :vartype image_build_compute: str
+    :ivar service_managed_resources_settings: The service managed resource settings.
+    :vartype service_managed_resources_settings:
+     ~azure.mgmt.machinelearningservices.models.ServiceManagedResourcesSettings
+    :ivar primary_user_assigned_identity: The user assigned identity resource id that represents
+     the workspace identity.
+    :vartype primary_user_assigned_identity: str
+    :ivar public_network_access: Whether requests from Public Network are allowed. Possible values
+     include: "Enabled", "Disabled".
+    :vartype public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.PublicNetworkAccess
+    :ivar application_insights: ARM id of the application insights associated with this workspace.
+    :vartype application_insights: str
+    :ivar container_registry: ARM id of the container registry associated with this workspace.
+    :vartype container_registry: str
+    """
+
+    _attribute_map = {
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'description': {'key': 'properties.description', 'type': 'str'},
+        'friendly_name': {'key': 'properties.friendlyName', 'type': 'str'},
+        'image_build_compute': {'key': 'properties.imageBuildCompute', 'type': 'str'},
+        'service_managed_resources_settings': {'key': 'properties.serviceManagedResourcesSettings', 'type': 'ServiceManagedResourcesSettings'},
+        'primary_user_assigned_identity': {'key': 'properties.primaryUserAssignedIdentity', 'type': 'str'},
+        'public_network_access': {'key': 'properties.publicNetworkAccess', 'type': 'str'},
+        'application_insights': {'key': 'properties.applicationInsights', 'type': 'str'},
+        'container_registry': {'key': 'properties.containerRegistry', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. The resource tags for the machine learning workspace.
+        :paramtype tags: dict[str, str]
+        :keyword sku: The sku of the workspace.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        :keyword identity: The identity of the resource.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword description: The description of this workspace.
+        :paramtype description: str
+        :keyword friendly_name: The friendly name for this workspace.
+        :paramtype friendly_name: str
+        :keyword image_build_compute: The compute name for image build.
+        :paramtype image_build_compute: str
+        :keyword service_managed_resources_settings: The service managed resource settings.
+        :paramtype service_managed_resources_settings:
+         ~azure.mgmt.machinelearningservices.models.ServiceManagedResourcesSettings
+        :keyword primary_user_assigned_identity: The user assigned identity resource id that represents
+         the workspace identity.
+        :paramtype primary_user_assigned_identity: str
+        :keyword public_network_access: Whether requests from Public Network are allowed. Possible
+         values include: "Enabled", "Disabled".
+        :paramtype public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.PublicNetworkAccess
+        :keyword application_insights: ARM id of the application insights associated with this
+         workspace.
+        :paramtype application_insights: str
+        :keyword container_registry: ARM id of the container registry associated with this workspace.
+        :paramtype container_registry: str
+        """
+        super(WorkspaceUpdateParameters, self).__init__(**kwargs)
+        self.tags = kwargs.get('tags', None)
+        self.sku = kwargs.get('sku', None)
+        self.identity = kwargs.get('identity', None)
+        self.description = kwargs.get('description', None)
+        self.friendly_name = kwargs.get('friendly_name', None)
+        self.image_build_compute = kwargs.get('image_build_compute', None)
+        self.service_managed_resources_settings = kwargs.get('service_managed_resources_settings', None)
+        self.primary_user_assigned_identity = kwargs.get('primary_user_assigned_identity', None)
+        self.public_network_access = kwargs.get('public_network_access', None)
+        self.application_insights = kwargs.get('application_insights', None)
+        self.container_registry = kwargs.get('container_registry', None)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/_models_py3.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/_models_py3.py
new file mode 100644
index 00000000..9265fff1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/models/_models_py3.py
@@ -0,0 +1,21370 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+import datetime
+from typing import Any, Dict, List, Optional, Union
+
+from azure.core.exceptions import HttpResponseError
+import msrest.serialization
+
+from ._azure_machine_learning_workspaces_enums import *
+
+
+class DatastoreCredentials(msrest.serialization.Model):
+    """Base definition for datastore credentials.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AccountKeyDatastoreCredentials, CertificateDatastoreCredentials, NoneDatastoreCredentials, SasDatastoreCredentials, ServicePrincipalDatastoreCredentials.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'credentials_type': {'AccountKey': 'AccountKeyDatastoreCredentials', 'Certificate': 'CertificateDatastoreCredentials', 'None': 'NoneDatastoreCredentials', 'Sas': 'SasDatastoreCredentials', 'ServicePrincipal': 'ServicePrincipalDatastoreCredentials'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(DatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = None  # type: Optional[str]
+
+
+class AccountKeyDatastoreCredentials(DatastoreCredentials):
+    """Account key datastore credentials configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    :ivar secrets: Required. [Required] Storage account secrets.
+    :vartype secrets: ~azure.mgmt.machinelearningservices.models.AccountKeyDatastoreSecrets
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+        'secrets': {'required': True},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+        'secrets': {'key': 'secrets', 'type': 'AccountKeyDatastoreSecrets'},
+    }
+
+    def __init__(
+        self,
+        *,
+        secrets: "AccountKeyDatastoreSecrets",
+        **kwargs
+    ):
+        """
+        :keyword secrets: Required. [Required] Storage account secrets.
+        :paramtype secrets: ~azure.mgmt.machinelearningservices.models.AccountKeyDatastoreSecrets
+        """
+        super(AccountKeyDatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = 'AccountKey'  # type: str
+        self.secrets = secrets
+
+
+class DatastoreSecrets(msrest.serialization.Model):
+    """Base definition for datastore secrets.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AccountKeyDatastoreSecrets, CertificateDatastoreSecrets, SasDatastoreSecrets, ServicePrincipalDatastoreSecrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate", "Sas",
+     "ServicePrincipal".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    """
+
+    _validation = {
+        'secrets_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'secrets_type': {'key': 'secretsType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'secrets_type': {'AccountKey': 'AccountKeyDatastoreSecrets', 'Certificate': 'CertificateDatastoreSecrets', 'Sas': 'SasDatastoreSecrets', 'ServicePrincipal': 'ServicePrincipalDatastoreSecrets'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(DatastoreSecrets, self).__init__(**kwargs)
+        self.secrets_type = None  # type: Optional[str]
+
+
+class AccountKeyDatastoreSecrets(DatastoreSecrets):
+    """Datastore account key secrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate", "Sas",
+     "ServicePrincipal".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    :ivar key: Storage account key.
+    :vartype key: str
+    """
+
+    _validation = {
+        'secrets_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'secrets_type': {'key': 'secretsType', 'type': 'str'},
+        'key': {'key': 'key', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        key: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword key: Storage account key.
+        :paramtype key: str
+        """
+        super(AccountKeyDatastoreSecrets, self).__init__(**kwargs)
+        self.secrets_type = 'AccountKey'  # type: str
+        self.key = key
+
+
+class AKSSchema(msrest.serialization.Model):
+    """AKSSchema.
+
+    :ivar properties: AKS properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'AKSSchemaProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["AKSSchemaProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: AKS properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
+        """
+        super(AKSSchema, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class Compute(msrest.serialization.Model):
+    """Machine Learning compute object.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AKS, AmlCompute, ComputeInstance, DataFactory, DataLakeAnalytics, Databricks, HDInsight, Kubernetes, SynapseSpark, VirtualMachine.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    _subtype_map = {
+        'compute_type': {'AKS': 'AKS', 'AmlCompute': 'AmlCompute', 'ComputeInstance': 'ComputeInstance', 'DataFactory': 'DataFactory', 'DataLakeAnalytics': 'DataLakeAnalytics', 'Databricks': 'Databricks', 'HDInsight': 'HDInsight', 'Kubernetes': 'Kubernetes', 'SynapseSpark': 'SynapseSpark', 'VirtualMachine': 'VirtualMachine'}
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(Compute, self).__init__(**kwargs)
+        self.compute_type = None  # type: Optional[str]
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = description
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = resource_id
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class AKS(Compute, AKSSchema):
+    """A Machine Learning compute based on AKS.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: AKS properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'AKSSchemaProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["AKSSchemaProperties"] = None,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: AKS properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(AKS, self).__init__(description=description, resource_id=resource_id, properties=properties, **kwargs)
+        self.properties = properties
+        self.compute_type = 'AKS'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = description
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = resource_id
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class AksComputeSecretsProperties(msrest.serialization.Model):
+    """Properties of AksComputeSecrets.
+
+    :ivar user_kube_config: Content of kubeconfig file that can be used to connect to the
+     Kubernetes cluster.
+    :vartype user_kube_config: str
+    :ivar admin_kube_config: Content of kubeconfig file that can be used to connect to the
+     Kubernetes cluster.
+    :vartype admin_kube_config: str
+    :ivar image_pull_secret_name: Image registry pull secret.
+    :vartype image_pull_secret_name: str
+    """
+
+    _attribute_map = {
+        'user_kube_config': {'key': 'userKubeConfig', 'type': 'str'},
+        'admin_kube_config': {'key': 'adminKubeConfig', 'type': 'str'},
+        'image_pull_secret_name': {'key': 'imagePullSecretName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        user_kube_config: Optional[str] = None,
+        admin_kube_config: Optional[str] = None,
+        image_pull_secret_name: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword user_kube_config: Content of kubeconfig file that can be used to connect to the
+         Kubernetes cluster.
+        :paramtype user_kube_config: str
+        :keyword admin_kube_config: Content of kubeconfig file that can be used to connect to the
+         Kubernetes cluster.
+        :paramtype admin_kube_config: str
+        :keyword image_pull_secret_name: Image registry pull secret.
+        :paramtype image_pull_secret_name: str
+        """
+        super(AksComputeSecretsProperties, self).__init__(**kwargs)
+        self.user_kube_config = user_kube_config
+        self.admin_kube_config = admin_kube_config
+        self.image_pull_secret_name = image_pull_secret_name
+
+
+class ComputeSecrets(msrest.serialization.Model):
+    """Secrets related to a Machine Learning compute. Might differ for every type of compute.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AksComputeSecrets, DatabricksComputeSecrets, VirtualMachineSecrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'compute_type': {'AKS': 'AksComputeSecrets', 'Databricks': 'DatabricksComputeSecrets', 'VirtualMachine': 'VirtualMachineSecrets'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ComputeSecrets, self).__init__(**kwargs)
+        self.compute_type = None  # type: Optional[str]
+
+
+class AksComputeSecrets(ComputeSecrets, AksComputeSecretsProperties):
+    """Secrets related to a Machine Learning compute based on AKS.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar user_kube_config: Content of kubeconfig file that can be used to connect to the
+     Kubernetes cluster.
+    :vartype user_kube_config: str
+    :ivar admin_kube_config: Content of kubeconfig file that can be used to connect to the
+     Kubernetes cluster.
+    :vartype admin_kube_config: str
+    :ivar image_pull_secret_name: Image registry pull secret.
+    :vartype image_pull_secret_name: str
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'user_kube_config': {'key': 'userKubeConfig', 'type': 'str'},
+        'admin_kube_config': {'key': 'adminKubeConfig', 'type': 'str'},
+        'image_pull_secret_name': {'key': 'imagePullSecretName', 'type': 'str'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        user_kube_config: Optional[str] = None,
+        admin_kube_config: Optional[str] = None,
+        image_pull_secret_name: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword user_kube_config: Content of kubeconfig file that can be used to connect to the
+         Kubernetes cluster.
+        :paramtype user_kube_config: str
+        :keyword admin_kube_config: Content of kubeconfig file that can be used to connect to the
+         Kubernetes cluster.
+        :paramtype admin_kube_config: str
+        :keyword image_pull_secret_name: Image registry pull secret.
+        :paramtype image_pull_secret_name: str
+        """
+        super(AksComputeSecrets, self).__init__(user_kube_config=user_kube_config, admin_kube_config=admin_kube_config, image_pull_secret_name=image_pull_secret_name, **kwargs)
+        self.user_kube_config = user_kube_config
+        self.admin_kube_config = admin_kube_config
+        self.image_pull_secret_name = image_pull_secret_name
+        self.compute_type = 'AKS'  # type: str
+
+
+class AksNetworkingConfiguration(msrest.serialization.Model):
+    """Advance configuration for AKS networking.
+
+    :ivar subnet_id: Virtual network subnet resource ID the compute nodes belong to.
+    :vartype subnet_id: str
+    :ivar service_cidr: A CIDR notation IP range from which to assign service cluster IPs. It must
+     not overlap with any Subnet IP ranges.
+    :vartype service_cidr: str
+    :ivar dns_service_ip: An IP address assigned to the Kubernetes DNS service. It must be within
+     the Kubernetes service address range specified in serviceCidr.
+    :vartype dns_service_ip: str
+    :ivar docker_bridge_cidr: A CIDR notation IP range assigned to the Docker bridge network. It
+     must not overlap with any Subnet IP ranges or the Kubernetes service address range.
+    :vartype docker_bridge_cidr: str
+    """
+
+    _validation = {
+        'service_cidr': {'pattern': r'^([0-9]{1,3}\.){3}[0-9]{1,3}(\/([0-9]|[1-2][0-9]|3[0-2]))?$'},
+        'dns_service_ip': {'pattern': r'^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$'},
+        'docker_bridge_cidr': {'pattern': r'^([0-9]{1,3}\.){3}[0-9]{1,3}(\/([0-9]|[1-2][0-9]|3[0-2]))?$'},
+    }
+
+    _attribute_map = {
+        'subnet_id': {'key': 'subnetId', 'type': 'str'},
+        'service_cidr': {'key': 'serviceCidr', 'type': 'str'},
+        'dns_service_ip': {'key': 'dnsServiceIP', 'type': 'str'},
+        'docker_bridge_cidr': {'key': 'dockerBridgeCidr', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        subnet_id: Optional[str] = None,
+        service_cidr: Optional[str] = None,
+        dns_service_ip: Optional[str] = None,
+        docker_bridge_cidr: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword subnet_id: Virtual network subnet resource ID the compute nodes belong to.
+        :paramtype subnet_id: str
+        :keyword service_cidr: A CIDR notation IP range from which to assign service cluster IPs. It
+         must not overlap with any Subnet IP ranges.
+        :paramtype service_cidr: str
+        :keyword dns_service_ip: An IP address assigned to the Kubernetes DNS service. It must be
+         within the Kubernetes service address range specified in serviceCidr.
+        :paramtype dns_service_ip: str
+        :keyword docker_bridge_cidr: A CIDR notation IP range assigned to the Docker bridge network. It
+         must not overlap with any Subnet IP ranges or the Kubernetes service address range.
+        :paramtype docker_bridge_cidr: str
+        """
+        super(AksNetworkingConfiguration, self).__init__(**kwargs)
+        self.subnet_id = subnet_id
+        self.service_cidr = service_cidr
+        self.dns_service_ip = dns_service_ip
+        self.docker_bridge_cidr = docker_bridge_cidr
+
+
+class AKSSchemaProperties(msrest.serialization.Model):
+    """AKS properties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar cluster_fqdn: Cluster full qualified domain name.
+    :vartype cluster_fqdn: str
+    :ivar system_services: System services.
+    :vartype system_services: list[~azure.mgmt.machinelearningservices.models.SystemService]
+    :ivar agent_count: Number of agents.
+    :vartype agent_count: int
+    :ivar agent_vm_size: Agent virtual machine size.
+    :vartype agent_vm_size: str
+    :ivar cluster_purpose: Intended usage of the cluster. Possible values include: "FastProd",
+     "DenseProd", "DevTest". Default value: "FastProd".
+    :vartype cluster_purpose: str or ~azure.mgmt.machinelearningservices.models.ClusterPurpose
+    :ivar ssl_configuration: SSL configuration.
+    :vartype ssl_configuration: ~azure.mgmt.machinelearningservices.models.SslConfiguration
+    :ivar aks_networking_configuration: AKS networking configuration for vnet.
+    :vartype aks_networking_configuration:
+     ~azure.mgmt.machinelearningservices.models.AksNetworkingConfiguration
+    :ivar load_balancer_type: Load Balancer Type. Possible values include: "PublicIp",
+     "InternalLoadBalancer". Default value: "PublicIp".
+    :vartype load_balancer_type: str or ~azure.mgmt.machinelearningservices.models.LoadBalancerType
+    :ivar load_balancer_subnet: Load Balancer Subnet.
+    :vartype load_balancer_subnet: str
+    """
+
+    _validation = {
+        'system_services': {'readonly': True},
+        'agent_count': {'minimum': 0},
+    }
+
+    _attribute_map = {
+        'cluster_fqdn': {'key': 'clusterFqdn', 'type': 'str'},
+        'system_services': {'key': 'systemServices', 'type': '[SystemService]'},
+        'agent_count': {'key': 'agentCount', 'type': 'int'},
+        'agent_vm_size': {'key': 'agentVmSize', 'type': 'str'},
+        'cluster_purpose': {'key': 'clusterPurpose', 'type': 'str'},
+        'ssl_configuration': {'key': 'sslConfiguration', 'type': 'SslConfiguration'},
+        'aks_networking_configuration': {'key': 'aksNetworkingConfiguration', 'type': 'AksNetworkingConfiguration'},
+        'load_balancer_type': {'key': 'loadBalancerType', 'type': 'str'},
+        'load_balancer_subnet': {'key': 'loadBalancerSubnet', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        cluster_fqdn: Optional[str] = None,
+        agent_count: Optional[int] = None,
+        agent_vm_size: Optional[str] = None,
+        cluster_purpose: Optional[Union[str, "ClusterPurpose"]] = "FastProd",
+        ssl_configuration: Optional["SslConfiguration"] = None,
+        aks_networking_configuration: Optional["AksNetworkingConfiguration"] = None,
+        load_balancer_type: Optional[Union[str, "LoadBalancerType"]] = "PublicIp",
+        load_balancer_subnet: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword cluster_fqdn: Cluster full qualified domain name.
+        :paramtype cluster_fqdn: str
+        :keyword agent_count: Number of agents.
+        :paramtype agent_count: int
+        :keyword agent_vm_size: Agent virtual machine size.
+        :paramtype agent_vm_size: str
+        :keyword cluster_purpose: Intended usage of the cluster. Possible values include: "FastProd",
+         "DenseProd", "DevTest". Default value: "FastProd".
+        :paramtype cluster_purpose: str or ~azure.mgmt.machinelearningservices.models.ClusterPurpose
+        :keyword ssl_configuration: SSL configuration.
+        :paramtype ssl_configuration: ~azure.mgmt.machinelearningservices.models.SslConfiguration
+        :keyword aks_networking_configuration: AKS networking configuration for vnet.
+        :paramtype aks_networking_configuration:
+         ~azure.mgmt.machinelearningservices.models.AksNetworkingConfiguration
+        :keyword load_balancer_type: Load Balancer Type. Possible values include: "PublicIp",
+         "InternalLoadBalancer". Default value: "PublicIp".
+        :paramtype load_balancer_type: str or
+         ~azure.mgmt.machinelearningservices.models.LoadBalancerType
+        :keyword load_balancer_subnet: Load Balancer Subnet.
+        :paramtype load_balancer_subnet: str
+        """
+        super(AKSSchemaProperties, self).__init__(**kwargs)
+        self.cluster_fqdn = cluster_fqdn
+        self.system_services = None
+        self.agent_count = agent_count
+        self.agent_vm_size = agent_vm_size
+        self.cluster_purpose = cluster_purpose
+        self.ssl_configuration = ssl_configuration
+        self.aks_networking_configuration = aks_networking_configuration
+        self.load_balancer_type = load_balancer_type
+        self.load_balancer_subnet = load_balancer_subnet
+
+
+class AmlComputeSchema(msrest.serialization.Model):
+    """Properties(top level) of AmlCompute.
+
+    :ivar properties: Properties of AmlCompute.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'AmlComputeProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["AmlComputeProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of AmlCompute.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
+        """
+        super(AmlComputeSchema, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class AmlCompute(Compute, AmlComputeSchema):
+    """An Azure Machine Learning compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: Properties of AmlCompute.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'AmlComputeProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["AmlComputeProperties"] = None,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of AmlCompute.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(AmlCompute, self).__init__(description=description, resource_id=resource_id, properties=properties, **kwargs)
+        self.properties = properties
+        self.compute_type = 'AmlCompute'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = description
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = resource_id
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class AmlComputeNodeInformation(msrest.serialization.Model):
+    """Compute node information related to a AmlCompute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar node_id: ID of the compute node.
+    :vartype node_id: str
+    :ivar private_ip_address: Private IP address of the compute node.
+    :vartype private_ip_address: str
+    :ivar public_ip_address: Public IP address of the compute node.
+    :vartype public_ip_address: str
+    :ivar port: SSH port number of the node.
+    :vartype port: int
+    :ivar node_state: State of the compute node. Values are idle, running, preparing, unusable,
+     leaving and preempted. Possible values include: "idle", "running", "preparing", "unusable",
+     "leaving", "preempted".
+    :vartype node_state: str or ~azure.mgmt.machinelearningservices.models.NodeState
+    :ivar run_id: ID of the Experiment running on the node, if any else null.
+    :vartype run_id: str
+    """
+
+    _validation = {
+        'node_id': {'readonly': True},
+        'private_ip_address': {'readonly': True},
+        'public_ip_address': {'readonly': True},
+        'port': {'readonly': True},
+        'node_state': {'readonly': True},
+        'run_id': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'node_id': {'key': 'nodeId', 'type': 'str'},
+        'private_ip_address': {'key': 'privateIpAddress', 'type': 'str'},
+        'public_ip_address': {'key': 'publicIpAddress', 'type': 'str'},
+        'port': {'key': 'port', 'type': 'int'},
+        'node_state': {'key': 'nodeState', 'type': 'str'},
+        'run_id': {'key': 'runId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AmlComputeNodeInformation, self).__init__(**kwargs)
+        self.node_id = None
+        self.private_ip_address = None
+        self.public_ip_address = None
+        self.port = None
+        self.node_state = None
+        self.run_id = None
+
+
+class AmlComputeNodesInformation(msrest.serialization.Model):
+    """Result of AmlCompute Nodes.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar nodes: The collection of returned AmlCompute nodes details.
+    :vartype nodes: list[~azure.mgmt.machinelearningservices.models.AmlComputeNodeInformation]
+    :ivar next_link: The continuation token.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'nodes': {'readonly': True},
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'nodes': {'key': 'nodes', 'type': '[AmlComputeNodeInformation]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AmlComputeNodesInformation, self).__init__(**kwargs)
+        self.nodes = None
+        self.next_link = None
+
+
+class AmlComputeProperties(msrest.serialization.Model):
+    """AML Compute properties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar os_type: Compute OS Type. Possible values include: "Linux", "Windows". Default value:
+     "Linux".
+    :vartype os_type: str or ~azure.mgmt.machinelearningservices.models.OsType
+    :ivar vm_size: Virtual Machine Size.
+    :vartype vm_size: str
+    :ivar vm_priority: Virtual Machine priority. Possible values include: "Dedicated",
+     "LowPriority".
+    :vartype vm_priority: str or ~azure.mgmt.machinelearningservices.models.VmPriority
+    :ivar virtual_machine_image: Virtual Machine image for AML Compute - windows only.
+    :vartype virtual_machine_image: ~azure.mgmt.machinelearningservices.models.VirtualMachineImage
+    :ivar isolated_network: Network is isolated or not.
+    :vartype isolated_network: bool
+    :ivar scale_settings: Scale settings for AML Compute.
+    :vartype scale_settings: ~azure.mgmt.machinelearningservices.models.ScaleSettings
+    :ivar user_account_credentials: Credentials for an administrator user account that will be
+     created on each compute node.
+    :vartype user_account_credentials:
+     ~azure.mgmt.machinelearningservices.models.UserAccountCredentials
+    :ivar subnet: Virtual network subnet resource ID the compute nodes belong to.
+    :vartype subnet: ~azure.mgmt.machinelearningservices.models.ResourceId
+    :ivar remote_login_port_public_access: State of the public SSH port. Possible values are:
+     Disabled - Indicates that the public ssh port is closed on all nodes of the cluster. Enabled -
+     Indicates that the public ssh port is open on all nodes of the cluster. NotSpecified -
+     Indicates that the public ssh port is closed on all nodes of the cluster if VNet is defined,
+     else is open all public nodes. It can be default only during cluster creation time, after
+     creation it will be either enabled or disabled. Possible values include: "Enabled", "Disabled",
+     "NotSpecified". Default value: "NotSpecified".
+    :vartype remote_login_port_public_access: str or
+     ~azure.mgmt.machinelearningservices.models.RemoteLoginPortPublicAccess
+    :ivar allocation_state: Allocation state of the compute. Possible values are: steady -
+     Indicates that the compute is not resizing. There are no changes to the number of compute nodes
+     in the compute in progress. A compute enters this state when it is created and when no
+     operations are being performed on the compute to change the number of compute nodes. resizing -
+     Indicates that the compute is resizing; that is, compute nodes are being added to or removed
+     from the compute. Possible values include: "Steady", "Resizing".
+    :vartype allocation_state: str or ~azure.mgmt.machinelearningservices.models.AllocationState
+    :ivar allocation_state_transition_time: The time at which the compute entered its current
+     allocation state.
+    :vartype allocation_state_transition_time: ~datetime.datetime
+    :ivar errors: Collection of errors encountered by various compute nodes during node setup.
+    :vartype errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar current_node_count: The number of compute nodes currently assigned to the compute.
+    :vartype current_node_count: int
+    :ivar target_node_count: The target number of compute nodes for the compute. If the
+     allocationState is resizing, this property denotes the target node count for the ongoing resize
+     operation. If the allocationState is steady, this property denotes the target node count for
+     the previous resize operation.
+    :vartype target_node_count: int
+    :ivar node_state_counts: Counts of various node states on the compute.
+    :vartype node_state_counts: ~azure.mgmt.machinelearningservices.models.NodeStateCounts
+    :ivar enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
+     values are: Possible values are: true - Indicates that the compute nodes will have public IPs
+     provisioned. false - Indicates that the compute nodes will have a private endpoint and no
+     public IPs.
+    :vartype enable_node_public_ip: bool
+    :ivar property_bag: A property bag containing additional properties.
+    :vartype property_bag: any
+    """
+
+    _validation = {
+        'allocation_state': {'readonly': True},
+        'allocation_state_transition_time': {'readonly': True},
+        'errors': {'readonly': True},
+        'current_node_count': {'readonly': True},
+        'target_node_count': {'readonly': True},
+        'node_state_counts': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'os_type': {'key': 'osType', 'type': 'str'},
+        'vm_size': {'key': 'vmSize', 'type': 'str'},
+        'vm_priority': {'key': 'vmPriority', 'type': 'str'},
+        'virtual_machine_image': {'key': 'virtualMachineImage', 'type': 'VirtualMachineImage'},
+        'isolated_network': {'key': 'isolatedNetwork', 'type': 'bool'},
+        'scale_settings': {'key': 'scaleSettings', 'type': 'ScaleSettings'},
+        'user_account_credentials': {'key': 'userAccountCredentials', 'type': 'UserAccountCredentials'},
+        'subnet': {'key': 'subnet', 'type': 'ResourceId'},
+        'remote_login_port_public_access': {'key': 'remoteLoginPortPublicAccess', 'type': 'str'},
+        'allocation_state': {'key': 'allocationState', 'type': 'str'},
+        'allocation_state_transition_time': {'key': 'allocationStateTransitionTime', 'type': 'iso-8601'},
+        'errors': {'key': 'errors', 'type': '[ErrorResponse]'},
+        'current_node_count': {'key': 'currentNodeCount', 'type': 'int'},
+        'target_node_count': {'key': 'targetNodeCount', 'type': 'int'},
+        'node_state_counts': {'key': 'nodeStateCounts', 'type': 'NodeStateCounts'},
+        'enable_node_public_ip': {'key': 'enableNodePublicIp', 'type': 'bool'},
+        'property_bag': {'key': 'propertyBag', 'type': 'object'},
+    }
+
+    def __init__(
+        self,
+        *,
+        os_type: Optional[Union[str, "OsType"]] = "Linux",
+        vm_size: Optional[str] = None,
+        vm_priority: Optional[Union[str, "VmPriority"]] = None,
+        virtual_machine_image: Optional["VirtualMachineImage"] = None,
+        isolated_network: Optional[bool] = None,
+        scale_settings: Optional["ScaleSettings"] = None,
+        user_account_credentials: Optional["UserAccountCredentials"] = None,
+        subnet: Optional["ResourceId"] = None,
+        remote_login_port_public_access: Optional[Union[str, "RemoteLoginPortPublicAccess"]] = "NotSpecified",
+        enable_node_public_ip: Optional[bool] = True,
+        property_bag: Optional[Any] = None,
+        **kwargs
+    ):
+        """
+        :keyword os_type: Compute OS Type. Possible values include: "Linux", "Windows". Default value:
+         "Linux".
+        :paramtype os_type: str or ~azure.mgmt.machinelearningservices.models.OsType
+        :keyword vm_size: Virtual Machine Size.
+        :paramtype vm_size: str
+        :keyword vm_priority: Virtual Machine priority. Possible values include: "Dedicated",
+         "LowPriority".
+        :paramtype vm_priority: str or ~azure.mgmt.machinelearningservices.models.VmPriority
+        :keyword virtual_machine_image: Virtual Machine image for AML Compute - windows only.
+        :paramtype virtual_machine_image:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineImage
+        :keyword isolated_network: Network is isolated or not.
+        :paramtype isolated_network: bool
+        :keyword scale_settings: Scale settings for AML Compute.
+        :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.ScaleSettings
+        :keyword user_account_credentials: Credentials for an administrator user account that will be
+         created on each compute node.
+        :paramtype user_account_credentials:
+         ~azure.mgmt.machinelearningservices.models.UserAccountCredentials
+        :keyword subnet: Virtual network subnet resource ID the compute nodes belong to.
+        :paramtype subnet: ~azure.mgmt.machinelearningservices.models.ResourceId
+        :keyword remote_login_port_public_access: State of the public SSH port. Possible values are:
+         Disabled - Indicates that the public ssh port is closed on all nodes of the cluster. Enabled -
+         Indicates that the public ssh port is open on all nodes of the cluster. NotSpecified -
+         Indicates that the public ssh port is closed on all nodes of the cluster if VNet is defined,
+         else is open all public nodes. It can be default only during cluster creation time, after
+         creation it will be either enabled or disabled. Possible values include: "Enabled", "Disabled",
+         "NotSpecified". Default value: "NotSpecified".
+        :paramtype remote_login_port_public_access: str or
+         ~azure.mgmt.machinelearningservices.models.RemoteLoginPortPublicAccess
+        :keyword enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
+         values are: Possible values are: true - Indicates that the compute nodes will have public IPs
+         provisioned. false - Indicates that the compute nodes will have a private endpoint and no
+         public IPs.
+        :paramtype enable_node_public_ip: bool
+        :keyword property_bag: A property bag containing additional properties.
+        :paramtype property_bag: any
+        """
+        super(AmlComputeProperties, self).__init__(**kwargs)
+        self.os_type = os_type
+        self.vm_size = vm_size
+        self.vm_priority = vm_priority
+        self.virtual_machine_image = virtual_machine_image
+        self.isolated_network = isolated_network
+        self.scale_settings = scale_settings
+        self.user_account_credentials = user_account_credentials
+        self.subnet = subnet
+        self.remote_login_port_public_access = remote_login_port_public_access
+        self.allocation_state = None
+        self.allocation_state_transition_time = None
+        self.errors = None
+        self.current_node_count = None
+        self.target_node_count = None
+        self.node_state_counts = None
+        self.enable_node_public_ip = enable_node_public_ip
+        self.property_bag = property_bag
+
+
+class AmlOperation(msrest.serialization.Model):
+    """Azure Machine Learning workspace REST API operation.
+
+    :ivar name: Operation name: {provider}/{resource}/{operation}.
+    :vartype name: str
+    :ivar display: Display name of operation.
+    :vartype display: ~azure.mgmt.machinelearningservices.models.AmlOperationDisplay
+    :ivar is_data_action: Indicates whether the operation applies to data-plane.
+    :vartype is_data_action: bool
+    """
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'display': {'key': 'display', 'type': 'AmlOperationDisplay'},
+        'is_data_action': {'key': 'isDataAction', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        name: Optional[str] = None,
+        display: Optional["AmlOperationDisplay"] = None,
+        is_data_action: Optional[bool] = None,
+        **kwargs
+    ):
+        """
+        :keyword name: Operation name: {provider}/{resource}/{operation}.
+        :paramtype name: str
+        :keyword display: Display name of operation.
+        :paramtype display: ~azure.mgmt.machinelearningservices.models.AmlOperationDisplay
+        :keyword is_data_action: Indicates whether the operation applies to data-plane.
+        :paramtype is_data_action: bool
+        """
+        super(AmlOperation, self).__init__(**kwargs)
+        self.name = name
+        self.display = display
+        self.is_data_action = is_data_action
+
+
+class AmlOperationDisplay(msrest.serialization.Model):
+    """Display name of operation.
+
+    :ivar provider: The resource provider name: Microsoft.MachineLearningExperimentation.
+    :vartype provider: str
+    :ivar resource: The resource on which the operation is performed.
+    :vartype resource: str
+    :ivar operation: The operation that users can perform.
+    :vartype operation: str
+    :ivar description: The description for the operation.
+    :vartype description: str
+    """
+
+    _attribute_map = {
+        'provider': {'key': 'provider', 'type': 'str'},
+        'resource': {'key': 'resource', 'type': 'str'},
+        'operation': {'key': 'operation', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        provider: Optional[str] = None,
+        resource: Optional[str] = None,
+        operation: Optional[str] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword provider: The resource provider name: Microsoft.MachineLearningExperimentation.
+        :paramtype provider: str
+        :keyword resource: The resource on which the operation is performed.
+        :paramtype resource: str
+        :keyword operation: The operation that users can perform.
+        :paramtype operation: str
+        :keyword description: The description for the operation.
+        :paramtype description: str
+        """
+        super(AmlOperationDisplay, self).__init__(**kwargs)
+        self.provider = provider
+        self.resource = resource
+        self.operation = operation
+        self.description = description
+
+
+class AmlOperationListResult(msrest.serialization.Model):
+    """An array of operations supported by the resource provider.
+
+    :ivar value: List of AML workspace operations supported by the AML workspace resource provider.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.AmlOperation]
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[AmlOperation]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional[List["AmlOperation"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword value: List of AML workspace operations supported by the AML workspace resource
+         provider.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.AmlOperation]
+        """
+        super(AmlOperationListResult, self).__init__(**kwargs)
+        self.value = value
+
+
+class IdentityConfiguration(msrest.serialization.Model):
+    """Base definition for identity configuration.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AmlToken, ManagedIdentity, UserIdentity.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar identity_type: Required. [Required] Specifies the type of identity framework.Constant
+     filled by server. Possible values include: "Managed", "AMLToken", "UserIdentity".
+    :vartype identity_type: str or
+     ~azure.mgmt.machinelearningservices.models.IdentityConfigurationType
+    """
+
+    _validation = {
+        'identity_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'identity_type': {'key': 'identityType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'identity_type': {'AMLToken': 'AmlToken', 'Managed': 'ManagedIdentity', 'UserIdentity': 'UserIdentity'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(IdentityConfiguration, self).__init__(**kwargs)
+        self.identity_type = None  # type: Optional[str]
+
+
+class AmlToken(IdentityConfiguration):
+    """AML Token identity configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar identity_type: Required. [Required] Specifies the type of identity framework.Constant
+     filled by server. Possible values include: "Managed", "AMLToken", "UserIdentity".
+    :vartype identity_type: str or
+     ~azure.mgmt.machinelearningservices.models.IdentityConfigurationType
+    """
+
+    _validation = {
+        'identity_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'identity_type': {'key': 'identityType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AmlToken, self).__init__(**kwargs)
+        self.identity_type = 'AMLToken'  # type: str
+
+
+class AmlUserFeature(msrest.serialization.Model):
+    """Features enabled for a workspace.
+
+    :ivar id: Specifies the feature ID.
+    :vartype id: str
+    :ivar display_name: Specifies the feature name.
+    :vartype display_name: str
+    :ivar description: Describes the feature for user experience.
+    :vartype description: str
+    """
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        id: Optional[str] = None,
+        display_name: Optional[str] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword id: Specifies the feature ID.
+        :paramtype id: str
+        :keyword display_name: Specifies the feature name.
+        :paramtype display_name: str
+        :keyword description: Describes the feature for user experience.
+        :paramtype description: str
+        """
+        super(AmlUserFeature, self).__init__(**kwargs)
+        self.id = id
+        self.display_name = display_name
+        self.description = description
+
+
+class ResourceBase(msrest.serialization.Model):
+    """ResourceBase.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        """
+        super(ResourceBase, self).__init__(**kwargs)
+        self.description = description
+        self.properties = properties
+        self.tags = tags
+
+
+class AssetBase(ResourceBase):
+    """AssetBase.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_anonymous: Optional[bool] = False,
+        is_archived: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(AssetBase, self).__init__(description=description, properties=properties, tags=tags, **kwargs)
+        self.is_anonymous = is_anonymous
+        self.is_archived = is_archived
+
+
+class AssetContainer(ResourceBase):
+    """AssetContainer.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_archived: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(AssetContainer, self).__init__(description=description, properties=properties, tags=tags, **kwargs)
+        self.is_archived = is_archived
+        self.latest_version = None
+        self.next_version = None
+
+
+class AssetJobInput(msrest.serialization.Model):
+    """Asset input type.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        uri: str,
+        mode: Optional[Union[str, "InputDeliveryMode"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        """
+        super(AssetJobInput, self).__init__(**kwargs)
+        self.mode = mode
+        self.uri = uri
+
+
+class AssetJobOutput(msrest.serialization.Model):
+    """Asset output type.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    """
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        mode: Optional[Union[str, "OutputDeliveryMode"]] = None,
+        uri: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        """
+        super(AssetJobOutput, self).__init__(**kwargs)
+        self.mode = mode
+        self.uri = uri
+
+
+class AssetReferenceBase(msrest.serialization.Model):
+    """Base definition for asset references.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: DataPathAssetReference, IdAssetReference, OutputPathAssetReference.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar reference_type: Required. [Required] Specifies the type of asset reference.Constant
+     filled by server. Possible values include: "Id", "DataPath", "OutputPath".
+    :vartype reference_type: str or ~azure.mgmt.machinelearningservices.models.ReferenceType
+    """
+
+    _validation = {
+        'reference_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'reference_type': {'key': 'referenceType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'reference_type': {'DataPath': 'DataPathAssetReference', 'Id': 'IdAssetReference', 'OutputPath': 'OutputPathAssetReference'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AssetReferenceBase, self).__init__(**kwargs)
+        self.reference_type = None  # type: Optional[str]
+
+
+class AssignedUser(msrest.serialization.Model):
+    """A user that can be assigned to a compute instance.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar object_id: Required. User�s AAD Object Id.
+    :vartype object_id: str
+    :ivar tenant_id: Required. User�s AAD Tenant Id.
+    :vartype tenant_id: str
+    """
+
+    _validation = {
+        'object_id': {'required': True},
+        'tenant_id': {'required': True},
+    }
+
+    _attribute_map = {
+        'object_id': {'key': 'objectId', 'type': 'str'},
+        'tenant_id': {'key': 'tenantId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        object_id: str,
+        tenant_id: str,
+        **kwargs
+    ):
+        """
+        :keyword object_id: Required. User�s AAD Object Id.
+        :paramtype object_id: str
+        :keyword tenant_id: Required. User�s AAD Tenant Id.
+        :paramtype tenant_id: str
+        """
+        super(AssignedUser, self).__init__(**kwargs)
+        self.object_id = object_id
+        self.tenant_id = tenant_id
+
+
+class ForecastHorizon(msrest.serialization.Model):
+    """The desired maximum forecast horizon in units of time-series frequency.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoForecastHorizon, CustomForecastHorizon.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set forecast horizon value selection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.ForecastHorizonMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'mode': {'Auto': 'AutoForecastHorizon', 'Custom': 'CustomForecastHorizon'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ForecastHorizon, self).__init__(**kwargs)
+        self.mode = None  # type: Optional[str]
+
+
+class AutoForecastHorizon(ForecastHorizon):
+    """Forecast horizon determined automatically by system.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set forecast horizon value selection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.ForecastHorizonMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AutoForecastHorizon, self).__init__(**kwargs)
+        self.mode = 'Auto'  # type: str
+
+
+class JobBaseProperties(ResourceBase):
+    """Base definition for a job.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoMLJob, CommandJob, PipelineJob, SweepJob.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "AutoML", "Command", "Sweep", "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Possible values include: "NotStarted", "Starting",
+     "Provisioning", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
+     "Failed", "Canceled", "NotResponding", "Paused", "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    """
+
+    _validation = {
+        'job_type': {'required': True},
+        'status': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'component_id': {'key': 'componentId', 'type': 'str'},
+        'compute_id': {'key': 'computeId', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'experiment_name': {'key': 'experimentName', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'job_type': {'key': 'jobType', 'type': 'str'},
+        'services': {'key': 'services', 'type': '{JobService}'},
+        'status': {'key': 'status', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'job_type': {'AutoML': 'AutoMLJob', 'Command': 'CommandJob', 'Pipeline': 'PipelineJob', 'Sweep': 'SweepJob'}
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        component_id: Optional[str] = None,
+        compute_id: Optional[str] = None,
+        display_name: Optional[str] = None,
+        experiment_name: Optional[str] = "Default",
+        identity: Optional["IdentityConfiguration"] = None,
+        is_archived: Optional[bool] = False,
+        services: Optional[Dict[str, "JobService"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        """
+        super(JobBaseProperties, self).__init__(description=description, properties=properties, tags=tags, **kwargs)
+        self.component_id = component_id
+        self.compute_id = compute_id
+        self.display_name = display_name
+        self.experiment_name = experiment_name
+        self.identity = identity
+        self.is_archived = is_archived
+        self.job_type = 'JobBaseProperties'  # type: str
+        self.services = services
+        self.status = None
+
+
+class AutoMLJob(JobBaseProperties):
+    """AutoMLJob class.
+Use this class for executing AutoML tasks like Classification/Regression etc.
+See TaskType enum for all the tasks supported.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "AutoML", "Command", "Sweep", "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Possible values include: "NotStarted", "Starting",
+     "Provisioning", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
+     "Failed", "Canceled", "NotResponding", "Paused", "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar environment_id: The ARM resource ID of the Environment specification for the job.
+     This is optional value to provide, if not provided, AutoML will default this to Production
+     AutoML curated environment version when running the job.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables included in the job.
+    :vartype environment_variables: dict[str, str]
+    :ivar outputs: Mapping of output data bindings used in the job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar resources: Compute Resource configuration for the job.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+    :ivar task_details: Required. [Required] This represents scenario which can be one of
+     Tables/NLP/Image.
+    :vartype task_details: ~azure.mgmt.machinelearningservices.models.AutoMLVertical
+    """
+
+    _validation = {
+        'job_type': {'required': True},
+        'status': {'readonly': True},
+        'task_details': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'component_id': {'key': 'componentId', 'type': 'str'},
+        'compute_id': {'key': 'computeId', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'experiment_name': {'key': 'experimentName', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'job_type': {'key': 'jobType', 'type': 'str'},
+        'services': {'key': 'services', 'type': '{JobService}'},
+        'status': {'key': 'status', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
+        'resources': {'key': 'resources', 'type': 'JobResourceConfiguration'},
+        'task_details': {'key': 'taskDetails', 'type': 'AutoMLVertical'},
+    }
+
+    def __init__(
+        self,
+        *,
+        task_details: "AutoMLVertical",
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        component_id: Optional[str] = None,
+        compute_id: Optional[str] = None,
+        display_name: Optional[str] = None,
+        experiment_name: Optional[str] = "Default",
+        identity: Optional["IdentityConfiguration"] = None,
+        is_archived: Optional[bool] = False,
+        services: Optional[Dict[str, "JobService"]] = None,
+        environment_id: Optional[str] = None,
+        environment_variables: Optional[Dict[str, str]] = None,
+        outputs: Optional[Dict[str, "JobOutput"]] = None,
+        resources: Optional["JobResourceConfiguration"] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword environment_id: The ARM resource ID of the Environment specification for the job.
+         This is optional value to provide, if not provided, AutoML will default this to Production
+         AutoML curated environment version when running the job.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables included in the job.
+        :paramtype environment_variables: dict[str, str]
+        :keyword outputs: Mapping of output data bindings used in the job.
+        :paramtype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+        :keyword resources: Compute Resource configuration for the job.
+        :paramtype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+        :keyword task_details: Required. [Required] This represents scenario which can be one of
+         Tables/NLP/Image.
+        :paramtype task_details: ~azure.mgmt.machinelearningservices.models.AutoMLVertical
+        """
+        super(AutoMLJob, self).__init__(description=description, properties=properties, tags=tags, component_id=component_id, compute_id=compute_id, display_name=display_name, experiment_name=experiment_name, identity=identity, is_archived=is_archived, services=services, **kwargs)
+        self.job_type = 'AutoML'  # type: str
+        self.environment_id = environment_id
+        self.environment_variables = environment_variables
+        self.outputs = outputs
+        self.resources = resources
+        self.task_details = task_details
+
+
+class AutoMLVertical(msrest.serialization.Model):
+    """AutoML vertical class.
+Base class for AutoML verticals - TableVertical/ImageVertical/NLPVertical.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: Classification, Forecasting, ImageClassification, ImageClassificationMultilabel, ImageInstanceSegmentation, ImageObjectDetection, Regression, TextClassification, TextClassificationMultilabel, TextNer.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+    }
+
+    _subtype_map = {
+        'task_type': {'Classification': 'Classification', 'Forecasting': 'Forecasting', 'ImageClassification': 'ImageClassification', 'ImageClassificationMultilabel': 'ImageClassificationMultilabel', 'ImageInstanceSegmentation': 'ImageInstanceSegmentation', 'ImageObjectDetection': 'ImageObjectDetection', 'Regression': 'Regression', 'TextClassification': 'TextClassification', 'TextClassificationMultilabel': 'TextClassificationMultilabel', 'TextNER': 'TextNer'}
+    }
+
+    def __init__(
+        self,
+        *,
+        training_data: "MLTableJobInput",
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        """
+        super(AutoMLVertical, self).__init__(**kwargs)
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.task_type = None  # type: Optional[str]
+        self.training_data = training_data
+
+
+class NCrossValidations(msrest.serialization.Model):
+    """N-Cross validations value.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoNCrossValidations, CustomNCrossValidations.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Mode for determining N-Cross validations.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.NCrossValidationsMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'mode': {'Auto': 'AutoNCrossValidations', 'Custom': 'CustomNCrossValidations'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(NCrossValidations, self).__init__(**kwargs)
+        self.mode = None  # type: Optional[str]
+
+
+class AutoNCrossValidations(NCrossValidations):
+    """N-Cross validations determined automatically.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Mode for determining N-Cross validations.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.NCrossValidationsMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AutoNCrossValidations, self).__init__(**kwargs)
+        self.mode = 'Auto'  # type: str
+
+
+class AutoPauseProperties(msrest.serialization.Model):
+    """Auto pause properties.
+
+    :ivar delay_in_minutes:
+    :vartype delay_in_minutes: int
+    :ivar enabled:
+    :vartype enabled: bool
+    """
+
+    _attribute_map = {
+        'delay_in_minutes': {'key': 'delayInMinutes', 'type': 'int'},
+        'enabled': {'key': 'enabled', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        delay_in_minutes: Optional[int] = None,
+        enabled: Optional[bool] = None,
+        **kwargs
+    ):
+        """
+        :keyword delay_in_minutes:
+        :paramtype delay_in_minutes: int
+        :keyword enabled:
+        :paramtype enabled: bool
+        """
+        super(AutoPauseProperties, self).__init__(**kwargs)
+        self.delay_in_minutes = delay_in_minutes
+        self.enabled = enabled
+
+
+class AutoScaleProperties(msrest.serialization.Model):
+    """Auto scale properties.
+
+    :ivar min_node_count:
+    :vartype min_node_count: int
+    :ivar enabled:
+    :vartype enabled: bool
+    :ivar max_node_count:
+    :vartype max_node_count: int
+    """
+
+    _attribute_map = {
+        'min_node_count': {'key': 'minNodeCount', 'type': 'int'},
+        'enabled': {'key': 'enabled', 'type': 'bool'},
+        'max_node_count': {'key': 'maxNodeCount', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        min_node_count: Optional[int] = None,
+        enabled: Optional[bool] = None,
+        max_node_count: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword min_node_count:
+        :paramtype min_node_count: int
+        :keyword enabled:
+        :paramtype enabled: bool
+        :keyword max_node_count:
+        :paramtype max_node_count: int
+        """
+        super(AutoScaleProperties, self).__init__(**kwargs)
+        self.min_node_count = min_node_count
+        self.enabled = enabled
+        self.max_node_count = max_node_count
+
+
+class Seasonality(msrest.serialization.Model):
+    """Forecasting seasonality.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoSeasonality, CustomSeasonality.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Seasonality mode.Constant filled by server. Possible values
+     include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.SeasonalityMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'mode': {'Auto': 'AutoSeasonality', 'Custom': 'CustomSeasonality'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(Seasonality, self).__init__(**kwargs)
+        self.mode = None  # type: Optional[str]
+
+
+class AutoSeasonality(Seasonality):
+    """AutoSeasonality.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Seasonality mode.Constant filled by server. Possible values
+     include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.SeasonalityMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AutoSeasonality, self).__init__(**kwargs)
+        self.mode = 'Auto'  # type: str
+
+
+class TargetLags(msrest.serialization.Model):
+    """The number of past periods to lag from the target column.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoTargetLags, CustomTargetLags.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set target lags mode - Auto/Custom.Constant filled by server.
+     Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetLagsMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'mode': {'Auto': 'AutoTargetLags', 'Custom': 'CustomTargetLags'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(TargetLags, self).__init__(**kwargs)
+        self.mode = None  # type: Optional[str]
+
+
+class AutoTargetLags(TargetLags):
+    """AutoTargetLags.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set target lags mode - Auto/Custom.Constant filled by server.
+     Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetLagsMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AutoTargetLags, self).__init__(**kwargs)
+        self.mode = 'Auto'  # type: str
+
+
+class TargetRollingWindowSize(msrest.serialization.Model):
+    """Forecasting target rolling window size.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AutoTargetRollingWindowSize, CustomTargetRollingWindowSize.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] TargetRollingWindowSiz detection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetRollingWindowSizeMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'mode': {'Auto': 'AutoTargetRollingWindowSize', 'Custom': 'CustomTargetRollingWindowSize'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(TargetRollingWindowSize, self).__init__(**kwargs)
+        self.mode = None  # type: Optional[str]
+
+
+class AutoTargetRollingWindowSize(TargetRollingWindowSize):
+    """Target lags rolling window determined automatically.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] TargetRollingWindowSiz detection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetRollingWindowSizeMode
+    """
+
+    _validation = {
+        'mode': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(AutoTargetRollingWindowSize, self).__init__(**kwargs)
+        self.mode = 'Auto'  # type: str
+
+
+class DatastoreProperties(ResourceBase):
+    """Base definition for datastore contents configuration.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: AzureBlobDatastore, AzureDataLakeGen1Datastore, AzureDataLakeGen2Datastore, AzureFileDatastore.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: Required. [Required] Account credentials.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: Required. [Required] Storage type backing the datastore.Constant filled
+     by server. Possible values include: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2",
+     "AzureFile".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    """
+
+    _validation = {
+        'credentials': {'required': True},
+        'datastore_type': {'required': True},
+        'is_default': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'credentials': {'key': 'credentials', 'type': 'DatastoreCredentials'},
+        'datastore_type': {'key': 'datastoreType', 'type': 'str'},
+        'is_default': {'key': 'isDefault', 'type': 'bool'},
+    }
+
+    _subtype_map = {
+        'datastore_type': {'AzureBlob': 'AzureBlobDatastore', 'AzureDataLakeGen1': 'AzureDataLakeGen1Datastore', 'AzureDataLakeGen2': 'AzureDataLakeGen2Datastore', 'AzureFile': 'AzureFileDatastore'}
+    }
+
+    def __init__(
+        self,
+        *,
+        credentials: "DatastoreCredentials",
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: Required. [Required] Account credentials.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        """
+        super(DatastoreProperties, self).__init__(description=description, properties=properties, tags=tags, **kwargs)
+        self.credentials = credentials
+        self.datastore_type = 'DatastoreProperties'  # type: str
+        self.is_default = None
+
+
+class AzureBlobDatastore(DatastoreProperties):
+    """Azure Blob datastore configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: Required. [Required] Account credentials.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: Required. [Required] Storage type backing the datastore.Constant filled
+     by server. Possible values include: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2",
+     "AzureFile".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    :ivar account_name: Storage account name.
+    :vartype account_name: str
+    :ivar container_name: Storage account container name.
+    :vartype container_name: str
+    :ivar endpoint: Azure cloud endpoint for the storage account.
+    :vartype endpoint: str
+    :ivar protocol: Protocol used to communicate with the storage account.
+    :vartype protocol: str
+    :ivar service_data_access_auth_identity: Indicates which identity to use to authenticate
+     service data access to customer's storage. Possible values include: "None",
+     "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+    :vartype service_data_access_auth_identity: str or
+     ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+    """
+
+    _validation = {
+        'credentials': {'required': True},
+        'datastore_type': {'required': True},
+        'is_default': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'credentials': {'key': 'credentials', 'type': 'DatastoreCredentials'},
+        'datastore_type': {'key': 'datastoreType', 'type': 'str'},
+        'is_default': {'key': 'isDefault', 'type': 'bool'},
+        'account_name': {'key': 'accountName', 'type': 'str'},
+        'container_name': {'key': 'containerName', 'type': 'str'},
+        'endpoint': {'key': 'endpoint', 'type': 'str'},
+        'protocol': {'key': 'protocol', 'type': 'str'},
+        'service_data_access_auth_identity': {'key': 'serviceDataAccessAuthIdentity', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        credentials: "DatastoreCredentials",
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        account_name: Optional[str] = None,
+        container_name: Optional[str] = None,
+        endpoint: Optional[str] = None,
+        protocol: Optional[str] = None,
+        service_data_access_auth_identity: Optional[Union[str, "ServiceDataAccessAuthIdentity"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: Required. [Required] Account credentials.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword account_name: Storage account name.
+        :paramtype account_name: str
+        :keyword container_name: Storage account container name.
+        :paramtype container_name: str
+        :keyword endpoint: Azure cloud endpoint for the storage account.
+        :paramtype endpoint: str
+        :keyword protocol: Protocol used to communicate with the storage account.
+        :paramtype protocol: str
+        :keyword service_data_access_auth_identity: Indicates which identity to use to authenticate
+         service data access to customer's storage. Possible values include: "None",
+         "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+        :paramtype service_data_access_auth_identity: str or
+         ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+        """
+        super(AzureBlobDatastore, self).__init__(description=description, properties=properties, tags=tags, credentials=credentials, **kwargs)
+        self.datastore_type = 'AzureBlob'  # type: str
+        self.account_name = account_name
+        self.container_name = container_name
+        self.endpoint = endpoint
+        self.protocol = protocol
+        self.service_data_access_auth_identity = service_data_access_auth_identity
+
+
+class AzureDataLakeGen1Datastore(DatastoreProperties):
+    """Azure Data Lake Gen1 datastore configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: Required. [Required] Account credentials.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: Required. [Required] Storage type backing the datastore.Constant filled
+     by server. Possible values include: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2",
+     "AzureFile".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    :ivar service_data_access_auth_identity: Indicates which identity to use to authenticate
+     service data access to customer's storage. Possible values include: "None",
+     "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+    :vartype service_data_access_auth_identity: str or
+     ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+    :ivar store_name: Required. [Required] Azure Data Lake store name.
+    :vartype store_name: str
+    """
+
+    _validation = {
+        'credentials': {'required': True},
+        'datastore_type': {'required': True},
+        'is_default': {'readonly': True},
+        'store_name': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'credentials': {'key': 'credentials', 'type': 'DatastoreCredentials'},
+        'datastore_type': {'key': 'datastoreType', 'type': 'str'},
+        'is_default': {'key': 'isDefault', 'type': 'bool'},
+        'service_data_access_auth_identity': {'key': 'serviceDataAccessAuthIdentity', 'type': 'str'},
+        'store_name': {'key': 'storeName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        credentials: "DatastoreCredentials",
+        store_name: str,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        service_data_access_auth_identity: Optional[Union[str, "ServiceDataAccessAuthIdentity"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: Required. [Required] Account credentials.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword service_data_access_auth_identity: Indicates which identity to use to authenticate
+         service data access to customer's storage. Possible values include: "None",
+         "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+        :paramtype service_data_access_auth_identity: str or
+         ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+        :keyword store_name: Required. [Required] Azure Data Lake store name.
+        :paramtype store_name: str
+        """
+        super(AzureDataLakeGen1Datastore, self).__init__(description=description, properties=properties, tags=tags, credentials=credentials, **kwargs)
+        self.datastore_type = 'AzureDataLakeGen1'  # type: str
+        self.service_data_access_auth_identity = service_data_access_auth_identity
+        self.store_name = store_name
+
+
+class AzureDataLakeGen2Datastore(DatastoreProperties):
+    """Azure Data Lake Gen2 datastore configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: Required. [Required] Account credentials.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: Required. [Required] Storage type backing the datastore.Constant filled
+     by server. Possible values include: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2",
+     "AzureFile".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    :ivar account_name: Required. [Required] Storage account name.
+    :vartype account_name: str
+    :ivar endpoint: Azure cloud endpoint for the storage account.
+    :vartype endpoint: str
+    :ivar filesystem: Required. [Required] The name of the Data Lake Gen2 filesystem.
+    :vartype filesystem: str
+    :ivar protocol: Protocol used to communicate with the storage account.
+    :vartype protocol: str
+    :ivar service_data_access_auth_identity: Indicates which identity to use to authenticate
+     service data access to customer's storage. Possible values include: "None",
+     "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+    :vartype service_data_access_auth_identity: str or
+     ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+    """
+
+    _validation = {
+        'credentials': {'required': True},
+        'datastore_type': {'required': True},
+        'is_default': {'readonly': True},
+        'account_name': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'filesystem': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'credentials': {'key': 'credentials', 'type': 'DatastoreCredentials'},
+        'datastore_type': {'key': 'datastoreType', 'type': 'str'},
+        'is_default': {'key': 'isDefault', 'type': 'bool'},
+        'account_name': {'key': 'accountName', 'type': 'str'},
+        'endpoint': {'key': 'endpoint', 'type': 'str'},
+        'filesystem': {'key': 'filesystem', 'type': 'str'},
+        'protocol': {'key': 'protocol', 'type': 'str'},
+        'service_data_access_auth_identity': {'key': 'serviceDataAccessAuthIdentity', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        credentials: "DatastoreCredentials",
+        account_name: str,
+        filesystem: str,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        endpoint: Optional[str] = None,
+        protocol: Optional[str] = None,
+        service_data_access_auth_identity: Optional[Union[str, "ServiceDataAccessAuthIdentity"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: Required. [Required] Account credentials.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword account_name: Required. [Required] Storage account name.
+        :paramtype account_name: str
+        :keyword endpoint: Azure cloud endpoint for the storage account.
+        :paramtype endpoint: str
+        :keyword filesystem: Required. [Required] The name of the Data Lake Gen2 filesystem.
+        :paramtype filesystem: str
+        :keyword protocol: Protocol used to communicate with the storage account.
+        :paramtype protocol: str
+        :keyword service_data_access_auth_identity: Indicates which identity to use to authenticate
+         service data access to customer's storage. Possible values include: "None",
+         "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+        :paramtype service_data_access_auth_identity: str or
+         ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+        """
+        super(AzureDataLakeGen2Datastore, self).__init__(description=description, properties=properties, tags=tags, credentials=credentials, **kwargs)
+        self.datastore_type = 'AzureDataLakeGen2'  # type: str
+        self.account_name = account_name
+        self.endpoint = endpoint
+        self.filesystem = filesystem
+        self.protocol = protocol
+        self.service_data_access_auth_identity = service_data_access_auth_identity
+
+
+class AzureFileDatastore(DatastoreProperties):
+    """Azure File datastore configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: Required. [Required] Account credentials.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: Required. [Required] Storage type backing the datastore.Constant filled
+     by server. Possible values include: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2",
+     "AzureFile".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    :ivar account_name: Required. [Required] Storage account name.
+    :vartype account_name: str
+    :ivar endpoint: Azure cloud endpoint for the storage account.
+    :vartype endpoint: str
+    :ivar file_share_name: Required. [Required] The name of the Azure file share that the datastore
+     points to.
+    :vartype file_share_name: str
+    :ivar protocol: Protocol used to communicate with the storage account.
+    :vartype protocol: str
+    :ivar service_data_access_auth_identity: Indicates which identity to use to authenticate
+     service data access to customer's storage. Possible values include: "None",
+     "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+    :vartype service_data_access_auth_identity: str or
+     ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+    """
+
+    _validation = {
+        'credentials': {'required': True},
+        'datastore_type': {'required': True},
+        'is_default': {'readonly': True},
+        'account_name': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'file_share_name': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'credentials': {'key': 'credentials', 'type': 'DatastoreCredentials'},
+        'datastore_type': {'key': 'datastoreType', 'type': 'str'},
+        'is_default': {'key': 'isDefault', 'type': 'bool'},
+        'account_name': {'key': 'accountName', 'type': 'str'},
+        'endpoint': {'key': 'endpoint', 'type': 'str'},
+        'file_share_name': {'key': 'fileShareName', 'type': 'str'},
+        'protocol': {'key': 'protocol', 'type': 'str'},
+        'service_data_access_auth_identity': {'key': 'serviceDataAccessAuthIdentity', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        credentials: "DatastoreCredentials",
+        account_name: str,
+        file_share_name: str,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        endpoint: Optional[str] = None,
+        protocol: Optional[str] = None,
+        service_data_access_auth_identity: Optional[Union[str, "ServiceDataAccessAuthIdentity"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: Required. [Required] Account credentials.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword account_name: Required. [Required] Storage account name.
+        :paramtype account_name: str
+        :keyword endpoint: Azure cloud endpoint for the storage account.
+        :paramtype endpoint: str
+        :keyword file_share_name: Required. [Required] The name of the Azure file share that the
+         datastore points to.
+        :paramtype file_share_name: str
+        :keyword protocol: Protocol used to communicate with the storage account.
+        :paramtype protocol: str
+        :keyword service_data_access_auth_identity: Indicates which identity to use to authenticate
+         service data access to customer's storage. Possible values include: "None",
+         "WorkspaceSystemAssignedIdentity", "WorkspaceUserAssignedIdentity".
+        :paramtype service_data_access_auth_identity: str or
+         ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
+        """
+        super(AzureFileDatastore, self).__init__(description=description, properties=properties, tags=tags, credentials=credentials, **kwargs)
+        self.datastore_type = 'AzureFile'  # type: str
+        self.account_name = account_name
+        self.endpoint = endpoint
+        self.file_share_name = file_share_name
+        self.protocol = protocol
+        self.service_data_access_auth_identity = service_data_access_auth_identity
+
+
+class EarlyTerminationPolicy(msrest.serialization.Model):
+    """Early termination policies enable canceling poor-performing runs before they complete.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: BanditPolicy, MedianStoppingPolicy, TruncationSelectionPolicy.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar delay_evaluation: Number of intervals by which to delay the first evaluation.
+    :vartype delay_evaluation: int
+    :ivar evaluation_interval: Interval (number of runs) between policy evaluations.
+    :vartype evaluation_interval: int
+    :ivar policy_type: Required. [Required] Name of policy configuration.Constant filled by server.
+     Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
+    :vartype policy_type: str or
+     ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicyType
+    """
+
+    _validation = {
+        'policy_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
+        'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
+        'policy_type': {'key': 'policyType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'policy_type': {'Bandit': 'BanditPolicy', 'MedianStopping': 'MedianStoppingPolicy', 'TruncationSelection': 'TruncationSelectionPolicy'}
+    }
+
+    def __init__(
+        self,
+        *,
+        delay_evaluation: Optional[int] = 0,
+        evaluation_interval: Optional[int] = 0,
+        **kwargs
+    ):
+        """
+        :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
+        :paramtype delay_evaluation: int
+        :keyword evaluation_interval: Interval (number of runs) between policy evaluations.
+        :paramtype evaluation_interval: int
+        """
+        super(EarlyTerminationPolicy, self).__init__(**kwargs)
+        self.delay_evaluation = delay_evaluation
+        self.evaluation_interval = evaluation_interval
+        self.policy_type = None  # type: Optional[str]
+
+
+class BanditPolicy(EarlyTerminationPolicy):
+    """Defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar delay_evaluation: Number of intervals by which to delay the first evaluation.
+    :vartype delay_evaluation: int
+    :ivar evaluation_interval: Interval (number of runs) between policy evaluations.
+    :vartype evaluation_interval: int
+    :ivar policy_type: Required. [Required] Name of policy configuration.Constant filled by server.
+     Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
+    :vartype policy_type: str or
+     ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicyType
+    :ivar slack_amount: Absolute distance allowed from the best performing run.
+    :vartype slack_amount: float
+    :ivar slack_factor: Ratio of the allowed distance from the best performing run.
+    :vartype slack_factor: float
+    """
+
+    _validation = {
+        'policy_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
+        'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
+        'policy_type': {'key': 'policyType', 'type': 'str'},
+        'slack_amount': {'key': 'slackAmount', 'type': 'float'},
+        'slack_factor': {'key': 'slackFactor', 'type': 'float'},
+    }
+
+    def __init__(
+        self,
+        *,
+        delay_evaluation: Optional[int] = 0,
+        evaluation_interval: Optional[int] = 0,
+        slack_amount: Optional[float] = 0,
+        slack_factor: Optional[float] = 0,
+        **kwargs
+    ):
+        """
+        :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
+        :paramtype delay_evaluation: int
+        :keyword evaluation_interval: Interval (number of runs) between policy evaluations.
+        :paramtype evaluation_interval: int
+        :keyword slack_amount: Absolute distance allowed from the best performing run.
+        :paramtype slack_amount: float
+        :keyword slack_factor: Ratio of the allowed distance from the best performing run.
+        :paramtype slack_factor: float
+        """
+        super(BanditPolicy, self).__init__(delay_evaluation=delay_evaluation, evaluation_interval=evaluation_interval, **kwargs)
+        self.policy_type = 'Bandit'  # type: str
+        self.slack_amount = slack_amount
+        self.slack_factor = slack_factor
+
+
+class Resource(msrest.serialization.Model):
+    """Common fields that are returned in the response for all Azure Resource Manager resources.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(Resource, self).__init__(**kwargs)
+        self.id = None
+        self.name = None
+        self.type = None
+        self.system_data = None
+
+
+class TrackedResource(Resource):
+    """The resource model definition for an Azure Resource Manager tracked top level resource which has 'tags' and a 'location'.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: Required. The geo-location where the resource lives.
+    :vartype location: str
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'location': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'location': {'key': 'location', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        location: str,
+        tags: Optional[Dict[str, str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: Required. The geo-location where the resource lives.
+        :paramtype location: str
+        """
+        super(TrackedResource, self).__init__(**kwargs)
+        self.tags = tags
+        self.location = location
+
+
+class BatchDeployment(TrackedResource):
+    """BatchDeployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: Required. The geo-location where the resource lives.
+    :vartype location: str
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.BatchDeploymentProperties
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'location': {'required': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'location': {'key': 'location', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'kind': {'key': 'kind', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': 'BatchDeploymentProperties'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+    }
+
+    def __init__(
+        self,
+        *,
+        location: str,
+        properties: "BatchDeploymentProperties",
+        tags: Optional[Dict[str, str]] = None,
+        identity: Optional["ManagedServiceIdentity"] = None,
+        kind: Optional[str] = None,
+        sku: Optional["Sku"] = None,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: Required. The geo-location where the resource lives.
+        :paramtype location: str
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.BatchDeploymentProperties
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super(BatchDeployment, self).__init__(tags=tags, location=location, **kwargs)
+        self.identity = identity
+        self.kind = kind
+        self.properties = properties
+        self.sku = sku
+
+
+class EndpointDeploymentPropertiesBase(msrest.serialization.Model):
+    """Base definition for endpoint deployment.
+
+    :ivar code_configuration: Code configuration for the endpoint deployment.
+    :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
+     endpoint deployment.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables configuration for the deployment.
+    :vartype environment_variables: dict[str, str]
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    """
+
+    _attribute_map = {
+        'code_configuration': {'key': 'codeConfiguration', 'type': 'CodeConfiguration'},
+        'description': {'key': 'description', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        code_configuration: Optional["CodeConfiguration"] = None,
+        description: Optional[str] = None,
+        environment_id: Optional[str] = None,
+        environment_variables: Optional[Dict[str, str]] = None,
+        properties: Optional[Dict[str, str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword code_configuration: Code configuration for the endpoint deployment.
+        :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
+         endpoint deployment.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables configuration for the deployment.
+        :paramtype environment_variables: dict[str, str]
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        """
+        super(EndpointDeploymentPropertiesBase, self).__init__(**kwargs)
+        self.code_configuration = code_configuration
+        self.description = description
+        self.environment_id = environment_id
+        self.environment_variables = environment_variables
+        self.properties = properties
+
+
+class BatchDeploymentProperties(EndpointDeploymentPropertiesBase):
+    """Batch inference settings per deployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar code_configuration: Code configuration for the endpoint deployment.
+    :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
+     endpoint deployment.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables configuration for the deployment.
+    :vartype environment_variables: dict[str, str]
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar compute: Compute target for batch inference operation.
+    :vartype compute: str
+    :ivar error_threshold: Error threshold, if the error count for the entire input goes above this
+     value,
+     the batch inference will be aborted. Range is [-1, int.MaxValue].
+     For FileDataset, this value is the count of file failures.
+     For TabularDataset, this value is the count of record failures.
+     If set to -1 (the lower bound), all failures during batch inference will be ignored.
+    :vartype error_threshold: int
+    :ivar logging_level: Logging level for batch inference operation. Possible values include:
+     "Info", "Warning", "Debug".
+    :vartype logging_level: str or ~azure.mgmt.machinelearningservices.models.BatchLoggingLevel
+    :ivar max_concurrency_per_instance: Indicates maximum number of parallelism per instance.
+    :vartype max_concurrency_per_instance: int
+    :ivar mini_batch_size: Size of the mini-batch passed to each batch invocation.
+     For FileDataset, this is the number of files per mini-batch.
+     For TabularDataset, this is the size of the records in bytes, per mini-batch.
+    :vartype mini_batch_size: long
+    :ivar model: Reference to the model asset for the endpoint deployment.
+    :vartype model: ~azure.mgmt.machinelearningservices.models.AssetReferenceBase
+    :ivar output_action: Indicates how the output will be organized. Possible values include:
+     "SummaryOnly", "AppendRow".
+    :vartype output_action: str or ~azure.mgmt.machinelearningservices.models.BatchOutputAction
+    :ivar output_file_name: Customized output file name for append_row output action.
+    :vartype output_file_name: str
+    :ivar provisioning_state: Provisioning state for the endpoint deployment. Possible values
+     include: "Creating", "Deleting", "Scaling", "Updating", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.DeploymentProvisioningState
+    :ivar resources: Indicates compute configuration for the job.
+     If not provided, will default to the defaults defined in ResourceConfiguration.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.DeploymentResourceConfiguration
+    :ivar retry_settings: Retry Settings for the batch inference operation.
+     If not provided, will default to the defaults defined in BatchRetrySettings.
+    :vartype retry_settings: ~azure.mgmt.machinelearningservices.models.BatchRetrySettings
+    """
+
+    _validation = {
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code_configuration': {'key': 'codeConfiguration', 'type': 'CodeConfiguration'},
+        'description': {'key': 'description', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'compute': {'key': 'compute', 'type': 'str'},
+        'error_threshold': {'key': 'errorThreshold', 'type': 'int'},
+        'logging_level': {'key': 'loggingLevel', 'type': 'str'},
+        'max_concurrency_per_instance': {'key': 'maxConcurrencyPerInstance', 'type': 'int'},
+        'mini_batch_size': {'key': 'miniBatchSize', 'type': 'long'},
+        'model': {'key': 'model', 'type': 'AssetReferenceBase'},
+        'output_action': {'key': 'outputAction', 'type': 'str'},
+        'output_file_name': {'key': 'outputFileName', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'resources': {'key': 'resources', 'type': 'DeploymentResourceConfiguration'},
+        'retry_settings': {'key': 'retrySettings', 'type': 'BatchRetrySettings'},
+    }
+
+    def __init__(
+        self,
+        *,
+        code_configuration: Optional["CodeConfiguration"] = None,
+        description: Optional[str] = None,
+        environment_id: Optional[str] = None,
+        environment_variables: Optional[Dict[str, str]] = None,
+        properties: Optional[Dict[str, str]] = None,
+        compute: Optional[str] = None,
+        error_threshold: Optional[int] = -1,
+        logging_level: Optional[Union[str, "BatchLoggingLevel"]] = None,
+        max_concurrency_per_instance: Optional[int] = 1,
+        mini_batch_size: Optional[int] = 10,
+        model: Optional["AssetReferenceBase"] = None,
+        output_action: Optional[Union[str, "BatchOutputAction"]] = None,
+        output_file_name: Optional[str] = "predictions.csv",
+        resources: Optional["DeploymentResourceConfiguration"] = None,
+        retry_settings: Optional["BatchRetrySettings"] = None,
+        **kwargs
+    ):
+        """
+        :keyword code_configuration: Code configuration for the endpoint deployment.
+        :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
+         endpoint deployment.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables configuration for the deployment.
+        :paramtype environment_variables: dict[str, str]
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword compute: Compute target for batch inference operation.
+        :paramtype compute: str
+        :keyword error_threshold: Error threshold, if the error count for the entire input goes above
+         this value,
+         the batch inference will be aborted. Range is [-1, int.MaxValue].
+         For FileDataset, this value is the count of file failures.
+         For TabularDataset, this value is the count of record failures.
+         If set to -1 (the lower bound), all failures during batch inference will be ignored.
+        :paramtype error_threshold: int
+        :keyword logging_level: Logging level for batch inference operation. Possible values include:
+         "Info", "Warning", "Debug".
+        :paramtype logging_level: str or ~azure.mgmt.machinelearningservices.models.BatchLoggingLevel
+        :keyword max_concurrency_per_instance: Indicates maximum number of parallelism per instance.
+        :paramtype max_concurrency_per_instance: int
+        :keyword mini_batch_size: Size of the mini-batch passed to each batch invocation.
+         For FileDataset, this is the number of files per mini-batch.
+         For TabularDataset, this is the size of the records in bytes, per mini-batch.
+        :paramtype mini_batch_size: long
+        :keyword model: Reference to the model asset for the endpoint deployment.
+        :paramtype model: ~azure.mgmt.machinelearningservices.models.AssetReferenceBase
+        :keyword output_action: Indicates how the output will be organized. Possible values include:
+         "SummaryOnly", "AppendRow".
+        :paramtype output_action: str or ~azure.mgmt.machinelearningservices.models.BatchOutputAction
+        :keyword output_file_name: Customized output file name for append_row output action.
+        :paramtype output_file_name: str
+        :keyword resources: Indicates compute configuration for the job.
+         If not provided, will default to the defaults defined in ResourceConfiguration.
+        :paramtype resources:
+         ~azure.mgmt.machinelearningservices.models.DeploymentResourceConfiguration
+        :keyword retry_settings: Retry Settings for the batch inference operation.
+         If not provided, will default to the defaults defined in BatchRetrySettings.
+        :paramtype retry_settings: ~azure.mgmt.machinelearningservices.models.BatchRetrySettings
+        """
+        super(BatchDeploymentProperties, self).__init__(code_configuration=code_configuration, description=description, environment_id=environment_id, environment_variables=environment_variables, properties=properties, **kwargs)
+        self.compute = compute
+        self.error_threshold = error_threshold
+        self.logging_level = logging_level
+        self.max_concurrency_per_instance = max_concurrency_per_instance
+        self.mini_batch_size = mini_batch_size
+        self.model = model
+        self.output_action = output_action
+        self.output_file_name = output_file_name
+        self.provisioning_state = None
+        self.resources = resources
+        self.retry_settings = retry_settings
+
+
+class BatchDeploymentTrackedResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of BatchDeployment entities.
+
+    :ivar next_link: The link to the next page of BatchDeployment objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type BatchDeployment.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.BatchDeployment]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[BatchDeployment]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["BatchDeployment"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of BatchDeployment objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type BatchDeployment.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.BatchDeployment]
+        """
+        super(BatchDeploymentTrackedResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class BatchEndpoint(TrackedResource):
+    """BatchEndpoint.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: Required. The geo-location where the resource lives.
+    :vartype location: str
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.BatchEndpointProperties
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'location': {'required': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'location': {'key': 'location', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'kind': {'key': 'kind', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': 'BatchEndpointProperties'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+    }
+
+    def __init__(
+        self,
+        *,
+        location: str,
+        properties: "BatchEndpointProperties",
+        tags: Optional[Dict[str, str]] = None,
+        identity: Optional["ManagedServiceIdentity"] = None,
+        kind: Optional[str] = None,
+        sku: Optional["Sku"] = None,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: Required. The geo-location where the resource lives.
+        :paramtype location: str
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.BatchEndpointProperties
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super(BatchEndpoint, self).__init__(tags=tags, location=location, **kwargs)
+        self.identity = identity
+        self.kind = kind
+        self.properties = properties
+        self.sku = sku
+
+
+class BatchEndpointDefaults(msrest.serialization.Model):
+    """Batch endpoint default values.
+
+    :ivar deployment_name: Name of the deployment that will be default for the endpoint.
+     This deployment will end up getting 100% traffic when the endpoint scoring URL is invoked.
+    :vartype deployment_name: str
+    """
+
+    _attribute_map = {
+        'deployment_name': {'key': 'deploymentName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        deployment_name: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword deployment_name: Name of the deployment that will be default for the endpoint.
+         This deployment will end up getting 100% traffic when the endpoint scoring URL is invoked.
+        :paramtype deployment_name: str
+        """
+        super(BatchEndpointDefaults, self).__init__(**kwargs)
+        self.deployment_name = deployment_name
+
+
+class EndpointPropertiesBase(msrest.serialization.Model):
+    """Inference Endpoint base definition.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken' for
+     Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
+     Possible values include: "AMLToken", "Key", "AADToken".
+    :vartype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+    :ivar description: Description of the inference endpoint.
+    :vartype description: str
+    :ivar keys: EndpointAuthKeys to set initially on an Endpoint.
+     This property will always be returned as null. AuthKey values must be retrieved using the
+     ListKeys API.
+    :vartype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar scoring_uri: Endpoint URI.
+    :vartype scoring_uri: str
+    :ivar swagger_uri: Endpoint Swagger URI.
+    :vartype swagger_uri: str
+    """
+
+    _validation = {
+        'auth_mode': {'required': True},
+        'scoring_uri': {'readonly': True},
+        'swagger_uri': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'auth_mode': {'key': 'authMode', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'keys': {'key': 'keys', 'type': 'EndpointAuthKeys'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'scoring_uri': {'key': 'scoringUri', 'type': 'str'},
+        'swagger_uri': {'key': 'swaggerUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        auth_mode: Union[str, "EndpointAuthMode"],
+        description: Optional[str] = None,
+        keys: Optional["EndpointAuthKeys"] = None,
+        properties: Optional[Dict[str, str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken'
+         for Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken'
+         does. Possible values include: "AMLToken", "Key", "AADToken".
+        :paramtype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+        :keyword description: Description of the inference endpoint.
+        :paramtype description: str
+        :keyword keys: EndpointAuthKeys to set initially on an Endpoint.
+         This property will always be returned as null. AuthKey values must be retrieved using the
+         ListKeys API.
+        :paramtype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        """
+        super(EndpointPropertiesBase, self).__init__(**kwargs)
+        self.auth_mode = auth_mode
+        self.description = description
+        self.keys = keys
+        self.properties = properties
+        self.scoring_uri = None
+        self.swagger_uri = None
+
+
+class BatchEndpointProperties(EndpointPropertiesBase):
+    """Batch endpoint configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken' for
+     Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
+     Possible values include: "AMLToken", "Key", "AADToken".
+    :vartype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+    :ivar description: Description of the inference endpoint.
+    :vartype description: str
+    :ivar keys: EndpointAuthKeys to set initially on an Endpoint.
+     This property will always be returned as null. AuthKey values must be retrieved using the
+     ListKeys API.
+    :vartype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar scoring_uri: Endpoint URI.
+    :vartype scoring_uri: str
+    :ivar swagger_uri: Endpoint Swagger URI.
+    :vartype swagger_uri: str
+    :ivar defaults: Default values for Batch Endpoint.
+    :vartype defaults: ~azure.mgmt.machinelearningservices.models.BatchEndpointDefaults
+    :ivar provisioning_state: Provisioning state for the endpoint. Possible values include:
+     "Creating", "Deleting", "Succeeded", "Failed", "Updating", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.EndpointProvisioningState
+    """
+
+    _validation = {
+        'auth_mode': {'required': True},
+        'scoring_uri': {'readonly': True},
+        'swagger_uri': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'auth_mode': {'key': 'authMode', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'keys': {'key': 'keys', 'type': 'EndpointAuthKeys'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'scoring_uri': {'key': 'scoringUri', 'type': 'str'},
+        'swagger_uri': {'key': 'swaggerUri', 'type': 'str'},
+        'defaults': {'key': 'defaults', 'type': 'BatchEndpointDefaults'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        auth_mode: Union[str, "EndpointAuthMode"],
+        description: Optional[str] = None,
+        keys: Optional["EndpointAuthKeys"] = None,
+        properties: Optional[Dict[str, str]] = None,
+        defaults: Optional["BatchEndpointDefaults"] = None,
+        **kwargs
+    ):
+        """
+        :keyword auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken'
+         for Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken'
+         does. Possible values include: "AMLToken", "Key", "AADToken".
+        :paramtype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+        :keyword description: Description of the inference endpoint.
+        :paramtype description: str
+        :keyword keys: EndpointAuthKeys to set initially on an Endpoint.
+         This property will always be returned as null. AuthKey values must be retrieved using the
+         ListKeys API.
+        :paramtype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword defaults: Default values for Batch Endpoint.
+        :paramtype defaults: ~azure.mgmt.machinelearningservices.models.BatchEndpointDefaults
+        """
+        super(BatchEndpointProperties, self).__init__(auth_mode=auth_mode, description=description, keys=keys, properties=properties, **kwargs)
+        self.defaults = defaults
+        self.provisioning_state = None
+
+
+class BatchEndpointTrackedResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of BatchEndpoint entities.
+
+    :ivar next_link: The link to the next page of BatchEndpoint objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type BatchEndpoint.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.BatchEndpoint]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[BatchEndpoint]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["BatchEndpoint"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of BatchEndpoint objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type BatchEndpoint.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.BatchEndpoint]
+        """
+        super(BatchEndpointTrackedResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class BatchRetrySettings(msrest.serialization.Model):
+    """Retry settings for a batch inference operation.
+
+    :ivar max_retries: Maximum retry count for a mini-batch.
+    :vartype max_retries: int
+    :ivar timeout: Invocation timeout for a mini-batch, in ISO 8601 format.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'max_retries': {'key': 'maxRetries', 'type': 'int'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        max_retries: Optional[int] = 3,
+        timeout: Optional[datetime.timedelta] = "PT30S",
+        **kwargs
+    ):
+        """
+        :keyword max_retries: Maximum retry count for a mini-batch.
+        :paramtype max_retries: int
+        :keyword timeout: Invocation timeout for a mini-batch, in ISO 8601 format.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(BatchRetrySettings, self).__init__(**kwargs)
+        self.max_retries = max_retries
+        self.timeout = timeout
+
+
+class SamplingAlgorithm(msrest.serialization.Model):
+    """The Sampling Algorithm used to generate hyperparameter values, along with properties to
+configure the algorithm.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: BayesianSamplingAlgorithm, GridSamplingAlgorithm, RandomSamplingAlgorithm.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar sampling_algorithm_type: Required. [Required] The algorithm used for generating
+     hyperparameter values, along with configuration properties.Constant filled by server. Possible
+     values include: "Grid", "Random", "Bayesian".
+    :vartype sampling_algorithm_type: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    """
+
+    _validation = {
+        'sampling_algorithm_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'sampling_algorithm_type': {'key': 'samplingAlgorithmType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'sampling_algorithm_type': {'Bayesian': 'BayesianSamplingAlgorithm', 'Grid': 'GridSamplingAlgorithm', 'Random': 'RandomSamplingAlgorithm'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(SamplingAlgorithm, self).__init__(**kwargs)
+        self.sampling_algorithm_type = None  # type: Optional[str]
+
+
+class BayesianSamplingAlgorithm(SamplingAlgorithm):
+    """Defines a Sampling Algorithm that generates values based on previous values.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar sampling_algorithm_type: Required. [Required] The algorithm used for generating
+     hyperparameter values, along with configuration properties.Constant filled by server. Possible
+     values include: "Grid", "Random", "Bayesian".
+    :vartype sampling_algorithm_type: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    """
+
+    _validation = {
+        'sampling_algorithm_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'sampling_algorithm_type': {'key': 'samplingAlgorithmType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(BayesianSamplingAlgorithm, self).__init__(**kwargs)
+        self.sampling_algorithm_type = 'Bayesian'  # type: str
+
+
+class BuildContext(msrest.serialization.Model):
+    """Configuration settings for Docker build context.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar context_uri: Required. [Required] URI of the Docker build context used to build the
+     image. Supports blob URIs on environment creation and may return blob or Git URIs.
+    
+    
+     .. raw:: html
+    
+        <seealso
+     href="https://docs.docker.com/engine/reference/commandline/build/#extended-description" />.
+    :vartype context_uri: str
+    :ivar dockerfile_path: Path to the Dockerfile in the build context.
+    
+    
+     .. raw:: html
+    
+        <seealso href="https://docs.docker.com/engine/reference/builder/" />.
+    :vartype dockerfile_path: str
+    """
+
+    _validation = {
+        'context_uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'context_uri': {'key': 'contextUri', 'type': 'str'},
+        'dockerfile_path': {'key': 'dockerfilePath', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        context_uri: str,
+        dockerfile_path: Optional[str] = "Dockerfile",
+        **kwargs
+    ):
+        """
+        :keyword context_uri: Required. [Required] URI of the Docker build context used to build the
+         image. Supports blob URIs on environment creation and may return blob or Git URIs.
+        
+        
+         .. raw:: html
+        
+            <seealso
+         href="https://docs.docker.com/engine/reference/commandline/build/#extended-description" />.
+        :paramtype context_uri: str
+        :keyword dockerfile_path: Path to the Dockerfile in the build context.
+        
+        
+         .. raw:: html
+        
+            <seealso href="https://docs.docker.com/engine/reference/builder/" />.
+        :paramtype dockerfile_path: str
+        """
+        super(BuildContext, self).__init__(**kwargs)
+        self.context_uri = context_uri
+        self.dockerfile_path = dockerfile_path
+
+
+class CertificateDatastoreCredentials(DatastoreCredentials):
+    """Certificate datastore credentials configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    :ivar authority_url: Authority URL used for authentication.
+    :vartype authority_url: str
+    :ivar client_id: Required. [Required] Service principal client ID.
+    :vartype client_id: str
+    :ivar resource_url: Resource the service principal has access to.
+    :vartype resource_url: str
+    :ivar secrets: Required. [Required] Service principal secrets.
+    :vartype secrets: ~azure.mgmt.machinelearningservices.models.CertificateDatastoreSecrets
+    :ivar tenant_id: Required. [Required] ID of the tenant to which the service principal belongs.
+    :vartype tenant_id: str
+    :ivar thumbprint: Required. [Required] Thumbprint of the certificate used for authentication.
+    :vartype thumbprint: str
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+        'client_id': {'required': True},
+        'secrets': {'required': True},
+        'tenant_id': {'required': True},
+        'thumbprint': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+        'authority_url': {'key': 'authorityUrl', 'type': 'str'},
+        'client_id': {'key': 'clientId', 'type': 'str'},
+        'resource_url': {'key': 'resourceUrl', 'type': 'str'},
+        'secrets': {'key': 'secrets', 'type': 'CertificateDatastoreSecrets'},
+        'tenant_id': {'key': 'tenantId', 'type': 'str'},
+        'thumbprint': {'key': 'thumbprint', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        client_id: str,
+        secrets: "CertificateDatastoreSecrets",
+        tenant_id: str,
+        thumbprint: str,
+        authority_url: Optional[str] = None,
+        resource_url: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword authority_url: Authority URL used for authentication.
+        :paramtype authority_url: str
+        :keyword client_id: Required. [Required] Service principal client ID.
+        :paramtype client_id: str
+        :keyword resource_url: Resource the service principal has access to.
+        :paramtype resource_url: str
+        :keyword secrets: Required. [Required] Service principal secrets.
+        :paramtype secrets: ~azure.mgmt.machinelearningservices.models.CertificateDatastoreSecrets
+        :keyword tenant_id: Required. [Required] ID of the tenant to which the service principal
+         belongs.
+        :paramtype tenant_id: str
+        :keyword thumbprint: Required. [Required] Thumbprint of the certificate used for
+         authentication.
+        :paramtype thumbprint: str
+        """
+        super(CertificateDatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = 'Certificate'  # type: str
+        self.authority_url = authority_url
+        self.client_id = client_id
+        self.resource_url = resource_url
+        self.secrets = secrets
+        self.tenant_id = tenant_id
+        self.thumbprint = thumbprint
+
+
+class CertificateDatastoreSecrets(DatastoreSecrets):
+    """Datastore certificate secrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate", "Sas",
+     "ServicePrincipal".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    :ivar certificate: Service principal certificate.
+    :vartype certificate: str
+    """
+
+    _validation = {
+        'secrets_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'secrets_type': {'key': 'secretsType', 'type': 'str'},
+        'certificate': {'key': 'certificate', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        certificate: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword certificate: Service principal certificate.
+        :paramtype certificate: str
+        """
+        super(CertificateDatastoreSecrets, self).__init__(**kwargs)
+        self.secrets_type = 'Certificate'  # type: str
+        self.certificate = certificate
+
+
+class TableVertical(msrest.serialization.Model):
+    """Abstract class for AutoML tasks that use table dataset as input - such as Classification/Regression/Forecasting.
+
+    :ivar cv_split_column_names: Columns to use for CVSplit data.
+    :vartype cv_split_column_names: list[str]
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+    :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
+     when validation dataset is not provided.
+    :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar test_data: Test data input.
+    :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
+     purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype test_data_size: float
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar weight_column_name: The name of the sample weight column. Automated ML supports a
+     weighted column as an input, causing rows in the data to be weighted up or down.
+    :vartype weight_column_name: str
+    """
+
+    _attribute_map = {
+        'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'TableVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'TableVerticalLimitSettings'},
+        'n_cross_validations': {'key': 'nCrossValidations', 'type': 'NCrossValidations'},
+        'test_data': {'key': 'testData', 'type': 'MLTableJobInput'},
+        'test_data_size': {'key': 'testDataSize', 'type': 'float'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        cv_split_column_names: Optional[List[str]] = None,
+        featurization_settings: Optional["TableVerticalFeaturizationSettings"] = None,
+        limit_settings: Optional["TableVerticalLimitSettings"] = None,
+        n_cross_validations: Optional["NCrossValidations"] = None,
+        test_data: Optional["MLTableJobInput"] = None,
+        test_data_size: Optional[float] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        weight_column_name: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword cv_split_column_names: Columns to use for CVSplit data.
+        :paramtype cv_split_column_names: list[str]
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+        :keyword n_cross_validations: Number of cross validation folds to be applied on training
+         dataset
+         when validation dataset is not provided.
+        :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword test_data: Test data input.
+        :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
+         purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype test_data_size: float
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword weight_column_name: The name of the sample weight column. Automated ML supports a
+         weighted column as an input, causing rows in the data to be weighted up or down.
+        :paramtype weight_column_name: str
+        """
+        super(TableVertical, self).__init__(**kwargs)
+        self.cv_split_column_names = cv_split_column_names
+        self.featurization_settings = featurization_settings
+        self.limit_settings = limit_settings
+        self.n_cross_validations = n_cross_validations
+        self.test_data = test_data
+        self.test_data_size = test_data_size
+        self.validation_data = validation_data
+        self.validation_data_size = validation_data_size
+        self.weight_column_name = weight_column_name
+
+
+class Classification(AutoMLVertical, TableVertical):
+    """Classification task in AutoML Table vertical.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar cv_split_column_names: Columns to use for CVSplit data.
+    :vartype cv_split_column_names: list[str]
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+    :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
+     when validation dataset is not provided.
+    :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar test_data: Test data input.
+    :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
+     purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype test_data_size: float
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar weight_column_name: The name of the sample weight column. Automated ML supports a
+     weighted column as an input, causing rows in the data to be weighted up or down.
+    :vartype weight_column_name: str
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar positive_label: Positive label for binary metrics calculation.
+    :vartype positive_label: str
+    :ivar primary_metric: Primary metric for the task. Possible values include: "AUCWeighted",
+     "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    :ivar training_settings: Inputs for training phase for an AutoML Job.
+    :vartype training_settings:
+     ~azure.mgmt.machinelearningservices.models.ClassificationTrainingSettings
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'TableVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'TableVerticalLimitSettings'},
+        'n_cross_validations': {'key': 'nCrossValidations', 'type': 'NCrossValidations'},
+        'test_data': {'key': 'testData', 'type': 'MLTableJobInput'},
+        'test_data_size': {'key': 'testDataSize', 'type': 'float'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'positive_label': {'key': 'positiveLabel', 'type': 'str'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+        'training_settings': {'key': 'trainingSettings', 'type': 'ClassificationTrainingSettings'},
+    }
+
+    def __init__(
+        self,
+        *,
+        training_data: "MLTableJobInput",
+        cv_split_column_names: Optional[List[str]] = None,
+        featurization_settings: Optional["TableVerticalFeaturizationSettings"] = None,
+        limit_settings: Optional["TableVerticalLimitSettings"] = None,
+        n_cross_validations: Optional["NCrossValidations"] = None,
+        test_data: Optional["MLTableJobInput"] = None,
+        test_data_size: Optional[float] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        weight_column_name: Optional[str] = None,
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        positive_label: Optional[str] = None,
+        primary_metric: Optional[Union[str, "ClassificationPrimaryMetrics"]] = None,
+        training_settings: Optional["ClassificationTrainingSettings"] = None,
+        **kwargs
+    ):
+        """
+        :keyword cv_split_column_names: Columns to use for CVSplit data.
+        :paramtype cv_split_column_names: list[str]
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+        :keyword n_cross_validations: Number of cross validation folds to be applied on training
+         dataset
+         when validation dataset is not provided.
+        :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword test_data: Test data input.
+        :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
+         purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype test_data_size: float
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword weight_column_name: The name of the sample weight column. Automated ML supports a
+         weighted column as an input, causing rows in the data to be weighted up or down.
+        :paramtype weight_column_name: str
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword positive_label: Positive label for binary metrics calculation.
+        :paramtype positive_label: str
+        :keyword primary_metric: Primary metric for the task. Possible values include: "AUCWeighted",
+         "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", "PrecisionScoreWeighted".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+        :keyword training_settings: Inputs for training phase for an AutoML Job.
+        :paramtype training_settings:
+         ~azure.mgmt.machinelearningservices.models.ClassificationTrainingSettings
+        """
+        super(Classification, self).__init__(log_verbosity=log_verbosity, target_column_name=target_column_name, training_data=training_data, cv_split_column_names=cv_split_column_names, featurization_settings=featurization_settings, limit_settings=limit_settings, n_cross_validations=n_cross_validations, test_data=test_data, test_data_size=test_data_size, validation_data=validation_data, validation_data_size=validation_data_size, weight_column_name=weight_column_name, **kwargs)
+        self.cv_split_column_names = cv_split_column_names
+        self.featurization_settings = featurization_settings
+        self.limit_settings = limit_settings
+        self.n_cross_validations = n_cross_validations
+        self.test_data = test_data
+        self.test_data_size = test_data_size
+        self.validation_data = validation_data
+        self.validation_data_size = validation_data_size
+        self.weight_column_name = weight_column_name
+        self.task_type = 'Classification'  # type: str
+        self.positive_label = positive_label
+        self.primary_metric = primary_metric
+        self.training_settings = training_settings
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+
+
+class TrainingSettings(msrest.serialization.Model):
+    """Training related configuration.
+
+    :ivar enable_dnn_training: Enable recommendation of DNN models.
+    :vartype enable_dnn_training: bool
+    :ivar enable_model_explainability: Flag to turn on explainability on best model.
+    :vartype enable_model_explainability: bool
+    :ivar enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+    :vartype enable_onnx_compatible_models: bool
+    :ivar enable_stack_ensemble: Enable stack ensemble run.
+    :vartype enable_stack_ensemble: bool
+    :ivar enable_vote_ensemble: Enable voting ensemble run.
+    :vartype enable_vote_ensemble: bool
+    :ivar ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+     generation, multiple fitted models from the previous child runs are downloaded.
+     Configure this parameter with a higher value than 300 secs, if more time is needed.
+    :vartype ensemble_model_download_timeout: ~datetime.timedelta
+    :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+    :vartype stack_ensemble_settings:
+     ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    """
+
+    _attribute_map = {
+        'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
+        'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
+        'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
+        'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
+        'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
+        'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'duration'},
+        'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'StackEnsembleSettings'},
+    }
+
+    def __init__(
+        self,
+        *,
+        enable_dnn_training: Optional[bool] = False,
+        enable_model_explainability: Optional[bool] = True,
+        enable_onnx_compatible_models: Optional[bool] = False,
+        enable_stack_ensemble: Optional[bool] = True,
+        enable_vote_ensemble: Optional[bool] = True,
+        ensemble_model_download_timeout: Optional[datetime.timedelta] = "PT5M",
+        stack_ensemble_settings: Optional["StackEnsembleSettings"] = None,
+        **kwargs
+    ):
+        """
+        :keyword enable_dnn_training: Enable recommendation of DNN models.
+        :paramtype enable_dnn_training: bool
+        :keyword enable_model_explainability: Flag to turn on explainability on best model.
+        :paramtype enable_model_explainability: bool
+        :keyword enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+        :paramtype enable_onnx_compatible_models: bool
+        :keyword enable_stack_ensemble: Enable stack ensemble run.
+        :paramtype enable_stack_ensemble: bool
+        :keyword enable_vote_ensemble: Enable voting ensemble run.
+        :paramtype enable_vote_ensemble: bool
+        :keyword ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+         generation, multiple fitted models from the previous child runs are downloaded.
+         Configure this parameter with a higher value than 300 secs, if more time is needed.
+        :paramtype ensemble_model_download_timeout: ~datetime.timedelta
+        :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+        :paramtype stack_ensemble_settings:
+         ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        """
+        super(TrainingSettings, self).__init__(**kwargs)
+        self.enable_dnn_training = enable_dnn_training
+        self.enable_model_explainability = enable_model_explainability
+        self.enable_onnx_compatible_models = enable_onnx_compatible_models
+        self.enable_stack_ensemble = enable_stack_ensemble
+        self.enable_vote_ensemble = enable_vote_ensemble
+        self.ensemble_model_download_timeout = ensemble_model_download_timeout
+        self.stack_ensemble_settings = stack_ensemble_settings
+
+
+class ClassificationTrainingSettings(TrainingSettings):
+    """Classification Training related configuration.
+
+    :ivar enable_dnn_training: Enable recommendation of DNN models.
+    :vartype enable_dnn_training: bool
+    :ivar enable_model_explainability: Flag to turn on explainability on best model.
+    :vartype enable_model_explainability: bool
+    :ivar enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+    :vartype enable_onnx_compatible_models: bool
+    :ivar enable_stack_ensemble: Enable stack ensemble run.
+    :vartype enable_stack_ensemble: bool
+    :ivar enable_vote_ensemble: Enable voting ensemble run.
+    :vartype enable_vote_ensemble: bool
+    :ivar ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+     generation, multiple fitted models from the previous child runs are downloaded.
+     Configure this parameter with a higher value than 300 secs, if more time is needed.
+    :vartype ensemble_model_download_timeout: ~datetime.timedelta
+    :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+    :vartype stack_ensemble_settings:
+     ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    :ivar allowed_training_algorithms: Allowed models for classification task.
+    :vartype allowed_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationModels]
+    :ivar blocked_training_algorithms: Blocked models for classification task.
+    :vartype blocked_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationModels]
+    """
+
+    _attribute_map = {
+        'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
+        'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
+        'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
+        'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
+        'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
+        'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'duration'},
+        'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'StackEnsembleSettings'},
+        'allowed_training_algorithms': {'key': 'allowedTrainingAlgorithms', 'type': '[str]'},
+        'blocked_training_algorithms': {'key': 'blockedTrainingAlgorithms', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        enable_dnn_training: Optional[bool] = False,
+        enable_model_explainability: Optional[bool] = True,
+        enable_onnx_compatible_models: Optional[bool] = False,
+        enable_stack_ensemble: Optional[bool] = True,
+        enable_vote_ensemble: Optional[bool] = True,
+        ensemble_model_download_timeout: Optional[datetime.timedelta] = "PT5M",
+        stack_ensemble_settings: Optional["StackEnsembleSettings"] = None,
+        allowed_training_algorithms: Optional[List[Union[str, "ClassificationModels"]]] = None,
+        blocked_training_algorithms: Optional[List[Union[str, "ClassificationModels"]]] = None,
+        **kwargs
+    ):
+        """
+        :keyword enable_dnn_training: Enable recommendation of DNN models.
+        :paramtype enable_dnn_training: bool
+        :keyword enable_model_explainability: Flag to turn on explainability on best model.
+        :paramtype enable_model_explainability: bool
+        :keyword enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+        :paramtype enable_onnx_compatible_models: bool
+        :keyword enable_stack_ensemble: Enable stack ensemble run.
+        :paramtype enable_stack_ensemble: bool
+        :keyword enable_vote_ensemble: Enable voting ensemble run.
+        :paramtype enable_vote_ensemble: bool
+        :keyword ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+         generation, multiple fitted models from the previous child runs are downloaded.
+         Configure this parameter with a higher value than 300 secs, if more time is needed.
+        :paramtype ensemble_model_download_timeout: ~datetime.timedelta
+        :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+        :paramtype stack_ensemble_settings:
+         ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        :keyword allowed_training_algorithms: Allowed models for classification task.
+        :paramtype allowed_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationModels]
+        :keyword blocked_training_algorithms: Blocked models for classification task.
+        :paramtype blocked_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationModels]
+        """
+        super(ClassificationTrainingSettings, self).__init__(enable_dnn_training=enable_dnn_training, enable_model_explainability=enable_model_explainability, enable_onnx_compatible_models=enable_onnx_compatible_models, enable_stack_ensemble=enable_stack_ensemble, enable_vote_ensemble=enable_vote_ensemble, ensemble_model_download_timeout=ensemble_model_download_timeout, stack_ensemble_settings=stack_ensemble_settings, **kwargs)
+        self.allowed_training_algorithms = allowed_training_algorithms
+        self.blocked_training_algorithms = blocked_training_algorithms
+
+
+class ClusterUpdateParameters(msrest.serialization.Model):
+    """AmlCompute update parameters.
+
+    :ivar properties: Properties of ClusterUpdate.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ScaleSettingsInformation
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties.properties', 'type': 'ScaleSettingsInformation'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["ScaleSettingsInformation"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of ClusterUpdate.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ScaleSettingsInformation
+        """
+        super(ClusterUpdateParameters, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class CodeConfiguration(msrest.serialization.Model):
+    """Configuration for a scoring code asset.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar code_id: ARM resource ID of the code asset.
+    :vartype code_id: str
+    :ivar scoring_script: Required. [Required] The script to execute on startup. eg. "score.py".
+    :vartype scoring_script: str
+    """
+
+    _validation = {
+        'scoring_script': {'required': True, 'min_length': 1, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'code_id': {'key': 'codeId', 'type': 'str'},
+        'scoring_script': {'key': 'scoringScript', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        scoring_script: str,
+        code_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword code_id: ARM resource ID of the code asset.
+        :paramtype code_id: str
+        :keyword scoring_script: Required. [Required] The script to execute on startup. eg. "score.py".
+        :paramtype scoring_script: str
+        """
+        super(CodeConfiguration, self).__init__(**kwargs)
+        self.code_id = code_id
+        self.scoring_script = scoring_script
+
+
+class CodeContainer(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.CodeContainerProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'CodeContainerProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "CodeContainerProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.CodeContainerProperties
+        """
+        super(CodeContainer, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class CodeContainerProperties(AssetContainer):
+    """Container for code asset versions.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_archived: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(CodeContainerProperties, self).__init__(description=description, properties=properties, tags=tags, is_archived=is_archived, **kwargs)
+
+
+class CodeContainerResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of CodeContainer entities.
+
+    :ivar next_link: The link to the next page of CodeContainer objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type CodeContainer.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.CodeContainer]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[CodeContainer]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["CodeContainer"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of CodeContainer objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type CodeContainer.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.CodeContainer]
+        """
+        super(CodeContainerResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class CodeVersion(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.CodeVersionProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'CodeVersionProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "CodeVersionProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.CodeVersionProperties
+        """
+        super(CodeVersion, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class CodeVersionProperties(AssetBase):
+    """Code asset version details.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar code_uri: Uri where code is located.
+    :vartype code_uri: str
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'code_uri': {'key': 'codeUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_anonymous: Optional[bool] = False,
+        is_archived: Optional[bool] = False,
+        code_uri: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword code_uri: Uri where code is located.
+        :paramtype code_uri: str
+        """
+        super(CodeVersionProperties, self).__init__(description=description, properties=properties, tags=tags, is_anonymous=is_anonymous, is_archived=is_archived, **kwargs)
+        self.code_uri = code_uri
+
+
+class CodeVersionResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of CodeVersion entities.
+
+    :ivar next_link: The link to the next page of CodeVersion objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type CodeVersion.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.CodeVersion]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[CodeVersion]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["CodeVersion"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of CodeVersion objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type CodeVersion.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.CodeVersion]
+        """
+        super(CodeVersionResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class ColumnTransformer(msrest.serialization.Model):
+    """Column transformer parameters.
+
+    :ivar fields: Fields to apply transformer logic on.
+    :vartype fields: list[str]
+    :ivar parameters: Different properties to be passed to transformer.
+     Input expected is dictionary of key,value pairs in JSON format.
+    :vartype parameters: any
+    """
+
+    _attribute_map = {
+        'fields': {'key': 'fields', 'type': '[str]'},
+        'parameters': {'key': 'parameters', 'type': 'object'},
+    }
+
+    def __init__(
+        self,
+        *,
+        fields: Optional[List[str]] = None,
+        parameters: Optional[Any] = None,
+        **kwargs
+    ):
+        """
+        :keyword fields: Fields to apply transformer logic on.
+        :paramtype fields: list[str]
+        :keyword parameters: Different properties to be passed to transformer.
+         Input expected is dictionary of key,value pairs in JSON format.
+        :paramtype parameters: any
+        """
+        super(ColumnTransformer, self).__init__(**kwargs)
+        self.fields = fields
+        self.parameters = parameters
+
+
+class CommandJob(JobBaseProperties):
+    """Command job definition.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "AutoML", "Command", "Sweep", "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Possible values include: "NotStarted", "Starting",
+     "Provisioning", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
+     "Failed", "Canceled", "NotResponding", "Paused", "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar code_id: ARM resource ID of the code asset.
+    :vartype code_id: str
+    :ivar command: Required. [Required] The command to execute on startup of the job. eg. "python
+     train.py".
+    :vartype command: str
+    :ivar distribution: Distribution configuration of the job. If set, this should be one of Mpi,
+     Tensorflow, PyTorch, or null.
+    :vartype distribution: ~azure.mgmt.machinelearningservices.models.DistributionConfiguration
+    :ivar environment_id: Required. [Required] The ARM resource ID of the Environment specification
+     for the job.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables included in the job.
+    :vartype environment_variables: dict[str, str]
+    :ivar inputs: Mapping of input data bindings used in the job.
+    :vartype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+    :ivar limits: Command Job limit.
+    :vartype limits: ~azure.mgmt.machinelearningservices.models.CommandJobLimits
+    :ivar outputs: Mapping of output data bindings used in the job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar parameters: Input parameters.
+    :vartype parameters: any
+    :ivar resources: Compute Resource configuration for the job.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+    """
+
+    _validation = {
+        'job_type': {'required': True},
+        'status': {'readonly': True},
+        'command': {'required': True, 'min_length': 1, 'pattern': r'[a-zA-Z0-9_]'},
+        'environment_id': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'parameters': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'component_id': {'key': 'componentId', 'type': 'str'},
+        'compute_id': {'key': 'computeId', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'experiment_name': {'key': 'experimentName', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'job_type': {'key': 'jobType', 'type': 'str'},
+        'services': {'key': 'services', 'type': '{JobService}'},
+        'status': {'key': 'status', 'type': 'str'},
+        'code_id': {'key': 'codeId', 'type': 'str'},
+        'command': {'key': 'command', 'type': 'str'},
+        'distribution': {'key': 'distribution', 'type': 'DistributionConfiguration'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'inputs': {'key': 'inputs', 'type': '{JobInput}'},
+        'limits': {'key': 'limits', 'type': 'CommandJobLimits'},
+        'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
+        'parameters': {'key': 'parameters', 'type': 'object'},
+        'resources': {'key': 'resources', 'type': 'JobResourceConfiguration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        command: str,
+        environment_id: str,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        component_id: Optional[str] = None,
+        compute_id: Optional[str] = None,
+        display_name: Optional[str] = None,
+        experiment_name: Optional[str] = "Default",
+        identity: Optional["IdentityConfiguration"] = None,
+        is_archived: Optional[bool] = False,
+        services: Optional[Dict[str, "JobService"]] = None,
+        code_id: Optional[str] = None,
+        distribution: Optional["DistributionConfiguration"] = None,
+        environment_variables: Optional[Dict[str, str]] = None,
+        inputs: Optional[Dict[str, "JobInput"]] = None,
+        limits: Optional["CommandJobLimits"] = None,
+        outputs: Optional[Dict[str, "JobOutput"]] = None,
+        resources: Optional["JobResourceConfiguration"] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword code_id: ARM resource ID of the code asset.
+        :paramtype code_id: str
+        :keyword command: Required. [Required] The command to execute on startup of the job. eg.
+         "python train.py".
+        :paramtype command: str
+        :keyword distribution: Distribution configuration of the job. If set, this should be one of
+         Mpi, Tensorflow, PyTorch, or null.
+        :paramtype distribution: ~azure.mgmt.machinelearningservices.models.DistributionConfiguration
+        :keyword environment_id: Required. [Required] The ARM resource ID of the Environment
+         specification for the job.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables included in the job.
+        :paramtype environment_variables: dict[str, str]
+        :keyword inputs: Mapping of input data bindings used in the job.
+        :paramtype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+        :keyword limits: Command Job limit.
+        :paramtype limits: ~azure.mgmt.machinelearningservices.models.CommandJobLimits
+        :keyword outputs: Mapping of output data bindings used in the job.
+        :paramtype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+        :keyword resources: Compute Resource configuration for the job.
+        :paramtype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+        """
+        super(CommandJob, self).__init__(description=description, properties=properties, tags=tags, component_id=component_id, compute_id=compute_id, display_name=display_name, experiment_name=experiment_name, identity=identity, is_archived=is_archived, services=services, **kwargs)
+        self.job_type = 'Command'  # type: str
+        self.code_id = code_id
+        self.command = command
+        self.distribution = distribution
+        self.environment_id = environment_id
+        self.environment_variables = environment_variables
+        self.inputs = inputs
+        self.limits = limits
+        self.outputs = outputs
+        self.parameters = None
+        self.resources = resources
+
+
+class JobLimits(msrest.serialization.Model):
+    """JobLimits.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: CommandJobLimits, SweepJobLimits.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar job_limits_type: Required. [Required] JobLimit type.Constant filled by server. Possible
+     values include: "Command", "Sweep".
+    :vartype job_limits_type: str or ~azure.mgmt.machinelearningservices.models.JobLimitsType
+    :ivar timeout: The max run duration in ISO 8601 format, after which the job will be cancelled.
+     Only supports duration with precision as low as Seconds.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _validation = {
+        'job_limits_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'job_limits_type': {'key': 'jobLimitsType', 'type': 'str'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    _subtype_map = {
+        'job_limits_type': {'Command': 'CommandJobLimits', 'Sweep': 'SweepJobLimits'}
+    }
+
+    def __init__(
+        self,
+        *,
+        timeout: Optional[datetime.timedelta] = None,
+        **kwargs
+    ):
+        """
+        :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
+         cancelled. Only supports duration with precision as low as Seconds.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(JobLimits, self).__init__(**kwargs)
+        self.job_limits_type = None  # type: Optional[str]
+        self.timeout = timeout
+
+
+class CommandJobLimits(JobLimits):
+    """Command Job limit class.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar job_limits_type: Required. [Required] JobLimit type.Constant filled by server. Possible
+     values include: "Command", "Sweep".
+    :vartype job_limits_type: str or ~azure.mgmt.machinelearningservices.models.JobLimitsType
+    :ivar timeout: The max run duration in ISO 8601 format, after which the job will be cancelled.
+     Only supports duration with precision as low as Seconds.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _validation = {
+        'job_limits_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'job_limits_type': {'key': 'jobLimitsType', 'type': 'str'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        timeout: Optional[datetime.timedelta] = None,
+        **kwargs
+    ):
+        """
+        :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
+         cancelled. Only supports duration with precision as low as Seconds.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(CommandJobLimits, self).__init__(timeout=timeout, **kwargs)
+        self.job_limits_type = 'Command'  # type: str
+
+
+class ComponentContainer(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ComponentContainerProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'ComponentContainerProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "ComponentContainerProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComponentContainerProperties
+        """
+        super(ComponentContainer, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class ComponentContainerProperties(AssetContainer):
+    """Component container definition.
+
+
+.. raw:: html
+
+   <see href="https://learn.microsoft.com/azure/machine-learning/reference-yaml-component-command" />.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_archived: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(ComponentContainerProperties, self).__init__(description=description, properties=properties, tags=tags, is_archived=is_archived, **kwargs)
+
+
+class ComponentContainerResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of ComponentContainer entities.
+
+    :ivar next_link: The link to the next page of ComponentContainer objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type ComponentContainer.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[ComponentContainer]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["ComponentContainer"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of ComponentContainer objects. If null, there are
+         no additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type ComponentContainer.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+        """
+        super(ComponentContainerResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class ComponentVersion(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ComponentVersionProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'ComponentVersionProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "ComponentVersionProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComponentVersionProperties
+        """
+        super(ComponentVersion, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class ComponentVersionProperties(AssetBase):
+    """Definition of a component version: defines resources that span component types.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar component_spec: Defines Component definition details.
+    
+    
+     .. raw:: html
+    
+        <see
+     href="https://learn.microsoft.com/azure/machine-learning/reference-yaml-component-command"
+     />.
+    :vartype component_spec: any
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'component_spec': {'key': 'componentSpec', 'type': 'object'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_anonymous: Optional[bool] = False,
+        is_archived: Optional[bool] = False,
+        component_spec: Optional[Any] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword component_spec: Defines Component definition details.
+        
+        
+         .. raw:: html
+        
+            <see
+         href="https://learn.microsoft.com/azure/machine-learning/reference-yaml-component-command"
+         />.
+        :paramtype component_spec: any
+        """
+        super(ComponentVersionProperties, self).__init__(description=description, properties=properties, tags=tags, is_anonymous=is_anonymous, is_archived=is_archived, **kwargs)
+        self.component_spec = component_spec
+
+
+class ComponentVersionResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of ComponentVersion entities.
+
+    :ivar next_link: The link to the next page of ComponentVersion objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type ComponentVersion.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[ComponentVersion]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["ComponentVersion"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of ComponentVersion objects. If null, there are
+         no additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type ComponentVersion.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+        """
+        super(ComponentVersionResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class ComputeInstanceSchema(msrest.serialization.Model):
+    """Properties(top level) of ComputeInstance.
+
+    :ivar properties: Properties of ComputeInstance.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'ComputeInstanceProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["ComputeInstanceProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of ComputeInstance.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
+        """
+        super(ComputeInstanceSchema, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class ComputeInstance(Compute, ComputeInstanceSchema):
+    """An Azure Machine Learning compute instance.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: Properties of ComputeInstance.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'ComputeInstanceProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["ComputeInstanceProperties"] = None,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of ComputeInstance.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(ComputeInstance, self).__init__(description=description, resource_id=resource_id, properties=properties, **kwargs)
+        self.properties = properties
+        self.compute_type = 'ComputeInstance'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = description
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = resource_id
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class ComputeInstanceApplication(msrest.serialization.Model):
+    """Defines an Aml Instance application and its connectivity endpoint URI.
+
+    :ivar display_name: Name of the ComputeInstance application.
+    :vartype display_name: str
+    :ivar endpoint_uri: Application' endpoint URI.
+    :vartype endpoint_uri: str
+    """
+
+    _attribute_map = {
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'endpoint_uri': {'key': 'endpointUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        display_name: Optional[str] = None,
+        endpoint_uri: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword display_name: Name of the ComputeInstance application.
+        :paramtype display_name: str
+        :keyword endpoint_uri: Application' endpoint URI.
+        :paramtype endpoint_uri: str
+        """
+        super(ComputeInstanceApplication, self).__init__(**kwargs)
+        self.display_name = display_name
+        self.endpoint_uri = endpoint_uri
+
+
+class ComputeInstanceConnectivityEndpoints(msrest.serialization.Model):
+    """Defines all connectivity endpoints and properties for an ComputeInstance.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar public_ip_address: Public IP Address of this ComputeInstance.
+    :vartype public_ip_address: str
+    :ivar private_ip_address: Private IP Address of this ComputeInstance (local to the VNET in
+     which the compute instance is deployed).
+    :vartype private_ip_address: str
+    """
+
+    _validation = {
+        'public_ip_address': {'readonly': True},
+        'private_ip_address': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'public_ip_address': {'key': 'publicIpAddress', 'type': 'str'},
+        'private_ip_address': {'key': 'privateIpAddress', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ComputeInstanceConnectivityEndpoints, self).__init__(**kwargs)
+        self.public_ip_address = None
+        self.private_ip_address = None
+
+
+class ComputeInstanceContainer(msrest.serialization.Model):
+    """Defines an Aml Instance container.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar name: Name of the ComputeInstance container.
+    :vartype name: str
+    :ivar autosave: Auto save settings. Possible values include: "None", "Local", "Remote".
+    :vartype autosave: str or ~azure.mgmt.machinelearningservices.models.Autosave
+    :ivar gpu: Information of GPU.
+    :vartype gpu: str
+    :ivar network: network of this container. Possible values include: "Bridge", "Host".
+    :vartype network: str or ~azure.mgmt.machinelearningservices.models.Network
+    :ivar environment: Environment information of this container.
+    :vartype environment: ~azure.mgmt.machinelearningservices.models.ComputeInstanceEnvironmentInfo
+    :ivar services: services of this containers.
+    :vartype services: list[any]
+    """
+
+    _validation = {
+        'services': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'autosave': {'key': 'autosave', 'type': 'str'},
+        'gpu': {'key': 'gpu', 'type': 'str'},
+        'network': {'key': 'network', 'type': 'str'},
+        'environment': {'key': 'environment', 'type': 'ComputeInstanceEnvironmentInfo'},
+        'services': {'key': 'services', 'type': '[object]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        name: Optional[str] = None,
+        autosave: Optional[Union[str, "Autosave"]] = None,
+        gpu: Optional[str] = None,
+        network: Optional[Union[str, "Network"]] = None,
+        environment: Optional["ComputeInstanceEnvironmentInfo"] = None,
+        **kwargs
+    ):
+        """
+        :keyword name: Name of the ComputeInstance container.
+        :paramtype name: str
+        :keyword autosave: Auto save settings. Possible values include: "None", "Local", "Remote".
+        :paramtype autosave: str or ~azure.mgmt.machinelearningservices.models.Autosave
+        :keyword gpu: Information of GPU.
+        :paramtype gpu: str
+        :keyword network: network of this container. Possible values include: "Bridge", "Host".
+        :paramtype network: str or ~azure.mgmt.machinelearningservices.models.Network
+        :keyword environment: Environment information of this container.
+        :paramtype environment:
+         ~azure.mgmt.machinelearningservices.models.ComputeInstanceEnvironmentInfo
+        """
+        super(ComputeInstanceContainer, self).__init__(**kwargs)
+        self.name = name
+        self.autosave = autosave
+        self.gpu = gpu
+        self.network = network
+        self.environment = environment
+        self.services = None
+
+
+class ComputeInstanceCreatedBy(msrest.serialization.Model):
+    """Describes information on user who created this ComputeInstance.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar user_name: Name of the user.
+    :vartype user_name: str
+    :ivar user_org_id: Uniquely identifies user' Azure Active Directory organization.
+    :vartype user_org_id: str
+    :ivar user_id: Uniquely identifies the user within his/her organization.
+    :vartype user_id: str
+    """
+
+    _validation = {
+        'user_name': {'readonly': True},
+        'user_org_id': {'readonly': True},
+        'user_id': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'user_name': {'key': 'userName', 'type': 'str'},
+        'user_org_id': {'key': 'userOrgId', 'type': 'str'},
+        'user_id': {'key': 'userId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ComputeInstanceCreatedBy, self).__init__(**kwargs)
+        self.user_name = None
+        self.user_org_id = None
+        self.user_id = None
+
+
+class ComputeInstanceDataDisk(msrest.serialization.Model):
+    """Defines an Aml Instance DataDisk.
+
+    :ivar caching: Caching type of Data Disk. Possible values include: "None", "ReadOnly",
+     "ReadWrite".
+    :vartype caching: str or ~azure.mgmt.machinelearningservices.models.Caching
+    :ivar disk_size_gb: The initial disk size in gigabytes.
+    :vartype disk_size_gb: int
+    :ivar lun: The lun is used to uniquely identify each data disk. If attaching multiple disks,
+     each should have a distinct lun.
+    :vartype lun: int
+    :ivar storage_account_type: type of this storage account. Possible values include:
+     "Standard_LRS", "Premium_LRS". Default value: "Standard_LRS".
+    :vartype storage_account_type: str or
+     ~azure.mgmt.machinelearningservices.models.StorageAccountType
+    """
+
+    _attribute_map = {
+        'caching': {'key': 'caching', 'type': 'str'},
+        'disk_size_gb': {'key': 'diskSizeGB', 'type': 'int'},
+        'lun': {'key': 'lun', 'type': 'int'},
+        'storage_account_type': {'key': 'storageAccountType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        caching: Optional[Union[str, "Caching"]] = None,
+        disk_size_gb: Optional[int] = None,
+        lun: Optional[int] = None,
+        storage_account_type: Optional[Union[str, "StorageAccountType"]] = "Standard_LRS",
+        **kwargs
+    ):
+        """
+        :keyword caching: Caching type of Data Disk. Possible values include: "None", "ReadOnly",
+         "ReadWrite".
+        :paramtype caching: str or ~azure.mgmt.machinelearningservices.models.Caching
+        :keyword disk_size_gb: The initial disk size in gigabytes.
+        :paramtype disk_size_gb: int
+        :keyword lun: The lun is used to uniquely identify each data disk. If attaching multiple disks,
+         each should have a distinct lun.
+        :paramtype lun: int
+        :keyword storage_account_type: type of this storage account. Possible values include:
+         "Standard_LRS", "Premium_LRS". Default value: "Standard_LRS".
+        :paramtype storage_account_type: str or
+         ~azure.mgmt.machinelearningservices.models.StorageAccountType
+        """
+        super(ComputeInstanceDataDisk, self).__init__(**kwargs)
+        self.caching = caching
+        self.disk_size_gb = disk_size_gb
+        self.lun = lun
+        self.storage_account_type = storage_account_type
+
+
+class ComputeInstanceDataMount(msrest.serialization.Model):
+    """Defines an Aml Instance DataMount.
+
+    :ivar source: Source of the ComputeInstance data mount.
+    :vartype source: str
+    :ivar source_type: Data source type. Possible values include: "Dataset", "Datastore", "URI".
+    :vartype source_type: str or ~azure.mgmt.machinelearningservices.models.SourceType
+    :ivar mount_name: name of the ComputeInstance data mount.
+    :vartype mount_name: str
+    :ivar mount_action: Mount Action. Possible values include: "Mount", "Unmount".
+    :vartype mount_action: str or ~azure.mgmt.machinelearningservices.models.MountAction
+    :ivar created_by: who this data mount created by.
+    :vartype created_by: str
+    :ivar mount_path: Path of this data mount.
+    :vartype mount_path: str
+    :ivar mount_state: Mount state. Possible values include: "MountRequested", "Mounted",
+     "MountFailed", "UnmountRequested", "UnmountFailed", "Unmounted".
+    :vartype mount_state: str or ~azure.mgmt.machinelearningservices.models.MountState
+    :ivar mounted_on: The time when the disk mounted.
+    :vartype mounted_on: ~datetime.datetime
+    :ivar error: Error of this data mount.
+    :vartype error: str
+    """
+
+    _attribute_map = {
+        'source': {'key': 'source', 'type': 'str'},
+        'source_type': {'key': 'sourceType', 'type': 'str'},
+        'mount_name': {'key': 'mountName', 'type': 'str'},
+        'mount_action': {'key': 'mountAction', 'type': 'str'},
+        'created_by': {'key': 'createdBy', 'type': 'str'},
+        'mount_path': {'key': 'mountPath', 'type': 'str'},
+        'mount_state': {'key': 'mountState', 'type': 'str'},
+        'mounted_on': {'key': 'mountedOn', 'type': 'iso-8601'},
+        'error': {'key': 'error', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        source: Optional[str] = None,
+        source_type: Optional[Union[str, "SourceType"]] = None,
+        mount_name: Optional[str] = None,
+        mount_action: Optional[Union[str, "MountAction"]] = None,
+        created_by: Optional[str] = None,
+        mount_path: Optional[str] = None,
+        mount_state: Optional[Union[str, "MountState"]] = None,
+        mounted_on: Optional[datetime.datetime] = None,
+        error: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword source: Source of the ComputeInstance data mount.
+        :paramtype source: str
+        :keyword source_type: Data source type. Possible values include: "Dataset", "Datastore", "URI".
+        :paramtype source_type: str or ~azure.mgmt.machinelearningservices.models.SourceType
+        :keyword mount_name: name of the ComputeInstance data mount.
+        :paramtype mount_name: str
+        :keyword mount_action: Mount Action. Possible values include: "Mount", "Unmount".
+        :paramtype mount_action: str or ~azure.mgmt.machinelearningservices.models.MountAction
+        :keyword created_by: who this data mount created by.
+        :paramtype created_by: str
+        :keyword mount_path: Path of this data mount.
+        :paramtype mount_path: str
+        :keyword mount_state: Mount state. Possible values include: "MountRequested", "Mounted",
+         "MountFailed", "UnmountRequested", "UnmountFailed", "Unmounted".
+        :paramtype mount_state: str or ~azure.mgmt.machinelearningservices.models.MountState
+        :keyword mounted_on: The time when the disk mounted.
+        :paramtype mounted_on: ~datetime.datetime
+        :keyword error: Error of this data mount.
+        :paramtype error: str
+        """
+        super(ComputeInstanceDataMount, self).__init__(**kwargs)
+        self.source = source
+        self.source_type = source_type
+        self.mount_name = mount_name
+        self.mount_action = mount_action
+        self.created_by = created_by
+        self.mount_path = mount_path
+        self.mount_state = mount_state
+        self.mounted_on = mounted_on
+        self.error = error
+
+
+class ComputeInstanceEnvironmentInfo(msrest.serialization.Model):
+    """Environment information.
+
+    :ivar name: name of environment.
+    :vartype name: str
+    :ivar version: version of environment.
+    :vartype version: str
+    """
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'version': {'key': 'version', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        name: Optional[str] = None,
+        version: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword name: name of environment.
+        :paramtype name: str
+        :keyword version: version of environment.
+        :paramtype version: str
+        """
+        super(ComputeInstanceEnvironmentInfo, self).__init__(**kwargs)
+        self.name = name
+        self.version = version
+
+
+class ComputeInstanceLastOperation(msrest.serialization.Model):
+    """The last operation on ComputeInstance.
+
+    :ivar operation_name: Name of the last operation. Possible values include: "Create", "Start",
+     "Stop", "Restart", "Reimage", "Delete".
+    :vartype operation_name: str or ~azure.mgmt.machinelearningservices.models.OperationName
+    :ivar operation_time: Time of the last operation.
+    :vartype operation_time: ~datetime.datetime
+    :ivar operation_status: Operation status. Possible values include: "InProgress", "Succeeded",
+     "CreateFailed", "StartFailed", "StopFailed", "RestartFailed", "ReimageFailed", "DeleteFailed".
+    :vartype operation_status: str or ~azure.mgmt.machinelearningservices.models.OperationStatus
+    :ivar operation_trigger: Trigger of operation. Possible values include: "User", "Schedule",
+     "IdleShutdown".
+    :vartype operation_trigger: str or ~azure.mgmt.machinelearningservices.models.OperationTrigger
+    """
+
+    _attribute_map = {
+        'operation_name': {'key': 'operationName', 'type': 'str'},
+        'operation_time': {'key': 'operationTime', 'type': 'iso-8601'},
+        'operation_status': {'key': 'operationStatus', 'type': 'str'},
+        'operation_trigger': {'key': 'operationTrigger', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        operation_name: Optional[Union[str, "OperationName"]] = None,
+        operation_time: Optional[datetime.datetime] = None,
+        operation_status: Optional[Union[str, "OperationStatus"]] = None,
+        operation_trigger: Optional[Union[str, "OperationTrigger"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword operation_name: Name of the last operation. Possible values include: "Create",
+         "Start", "Stop", "Restart", "Reimage", "Delete".
+        :paramtype operation_name: str or ~azure.mgmt.machinelearningservices.models.OperationName
+        :keyword operation_time: Time of the last operation.
+        :paramtype operation_time: ~datetime.datetime
+        :keyword operation_status: Operation status. Possible values include: "InProgress",
+         "Succeeded", "CreateFailed", "StartFailed", "StopFailed", "RestartFailed", "ReimageFailed",
+         "DeleteFailed".
+        :paramtype operation_status: str or ~azure.mgmt.machinelearningservices.models.OperationStatus
+        :keyword operation_trigger: Trigger of operation. Possible values include: "User", "Schedule",
+         "IdleShutdown".
+        :paramtype operation_trigger: str or
+         ~azure.mgmt.machinelearningservices.models.OperationTrigger
+        """
+        super(ComputeInstanceLastOperation, self).__init__(**kwargs)
+        self.operation_name = operation_name
+        self.operation_time = operation_time
+        self.operation_status = operation_status
+        self.operation_trigger = operation_trigger
+
+
+class ComputeInstanceProperties(msrest.serialization.Model):
+    """Compute Instance properties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar vm_size: Virtual Machine Size.
+    :vartype vm_size: str
+    :ivar subnet: Virtual network subnet resource ID the compute nodes belong to.
+    :vartype subnet: ~azure.mgmt.machinelearningservices.models.ResourceId
+    :ivar application_sharing_policy: Policy for sharing applications on this compute instance
+     among users of parent workspace. If Personal, only the creator can access applications on this
+     compute instance. When Shared, any workspace user can access applications on this instance
+     depending on his/her assigned role. Possible values include: "Personal", "Shared". Default
+     value: "Shared".
+    :vartype application_sharing_policy: str or
+     ~azure.mgmt.machinelearningservices.models.ApplicationSharingPolicy
+    :ivar ssh_settings: Specifies policy and settings for SSH access.
+    :vartype ssh_settings: ~azure.mgmt.machinelearningservices.models.ComputeInstanceSshSettings
+    :ivar connectivity_endpoints: Describes all connectivity endpoints available for this
+     ComputeInstance.
+    :vartype connectivity_endpoints:
+     ~azure.mgmt.machinelearningservices.models.ComputeInstanceConnectivityEndpoints
+    :ivar applications: Describes available applications and their endpoints on this
+     ComputeInstance.
+    :vartype applications:
+     list[~azure.mgmt.machinelearningservices.models.ComputeInstanceApplication]
+    :ivar created_by: Describes information on user who created this ComputeInstance.
+    :vartype created_by: ~azure.mgmt.machinelearningservices.models.ComputeInstanceCreatedBy
+    :ivar errors: Collection of errors encountered on this ComputeInstance.
+    :vartype errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar state: The current state of this ComputeInstance. Possible values include: "Creating",
+     "CreateFailed", "Deleting", "Running", "Restarting", "JobRunning", "SettingUp", "SetupFailed",
+     "Starting", "Stopped", "Stopping", "UserSettingUp", "UserSetupFailed", "Unknown", "Unusable".
+    :vartype state: str or ~azure.mgmt.machinelearningservices.models.ComputeInstanceState
+    :ivar compute_instance_authorization_type: The Compute Instance Authorization type. Available
+     values are personal (default). Possible values include: "personal". Default value: "personal".
+    :vartype compute_instance_authorization_type: str or
+     ~azure.mgmt.machinelearningservices.models.ComputeInstanceAuthorizationType
+    :ivar personal_compute_instance_settings: Settings for a personal compute instance.
+    :vartype personal_compute_instance_settings:
+     ~azure.mgmt.machinelearningservices.models.PersonalComputeInstanceSettings
+    :ivar setup_scripts: Details of customized scripts to execute for setting up the cluster.
+    :vartype setup_scripts: ~azure.mgmt.machinelearningservices.models.SetupScripts
+    :ivar last_operation: The last operation on ComputeInstance.
+    :vartype last_operation:
+     ~azure.mgmt.machinelearningservices.models.ComputeInstanceLastOperation
+    :ivar schedules: The list of schedules to be applied on the computes.
+    :vartype schedules: ~azure.mgmt.machinelearningservices.models.ComputeSchedules
+    :ivar enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
+     values are: Possible values are: true - Indicates that the compute nodes will have public IPs
+     provisioned. false - Indicates that the compute nodes will have a private endpoint and no
+     public IPs.
+    :vartype enable_node_public_ip: bool
+    :ivar containers: Describes informations of containers on this ComputeInstance.
+    :vartype containers: list[~azure.mgmt.machinelearningservices.models.ComputeInstanceContainer]
+    :ivar data_disks: Describes informations of dataDisks on this ComputeInstance.
+    :vartype data_disks: list[~azure.mgmt.machinelearningservices.models.ComputeInstanceDataDisk]
+    :ivar data_mounts: Describes informations of dataMounts on this ComputeInstance.
+    :vartype data_mounts: list[~azure.mgmt.machinelearningservices.models.ComputeInstanceDataMount]
+    :ivar versions: ComputeInstance version.
+    :vartype versions: ~azure.mgmt.machinelearningservices.models.ComputeInstanceVersion
+    """
+
+    _validation = {
+        'connectivity_endpoints': {'readonly': True},
+        'applications': {'readonly': True},
+        'created_by': {'readonly': True},
+        'errors': {'readonly': True},
+        'state': {'readonly': True},
+        'last_operation': {'readonly': True},
+        'schedules': {'readonly': True},
+        'containers': {'readonly': True},
+        'data_disks': {'readonly': True},
+        'data_mounts': {'readonly': True},
+        'versions': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'vm_size': {'key': 'vmSize', 'type': 'str'},
+        'subnet': {'key': 'subnet', 'type': 'ResourceId'},
+        'application_sharing_policy': {'key': 'applicationSharingPolicy', 'type': 'str'},
+        'ssh_settings': {'key': 'sshSettings', 'type': 'ComputeInstanceSshSettings'},
+        'connectivity_endpoints': {'key': 'connectivityEndpoints', 'type': 'ComputeInstanceConnectivityEndpoints'},
+        'applications': {'key': 'applications', 'type': '[ComputeInstanceApplication]'},
+        'created_by': {'key': 'createdBy', 'type': 'ComputeInstanceCreatedBy'},
+        'errors': {'key': 'errors', 'type': '[ErrorResponse]'},
+        'state': {'key': 'state', 'type': 'str'},
+        'compute_instance_authorization_type': {'key': 'computeInstanceAuthorizationType', 'type': 'str'},
+        'personal_compute_instance_settings': {'key': 'personalComputeInstanceSettings', 'type': 'PersonalComputeInstanceSettings'},
+        'setup_scripts': {'key': 'setupScripts', 'type': 'SetupScripts'},
+        'last_operation': {'key': 'lastOperation', 'type': 'ComputeInstanceLastOperation'},
+        'schedules': {'key': 'schedules', 'type': 'ComputeSchedules'},
+        'enable_node_public_ip': {'key': 'enableNodePublicIp', 'type': 'bool'},
+        'containers': {'key': 'containers', 'type': '[ComputeInstanceContainer]'},
+        'data_disks': {'key': 'dataDisks', 'type': '[ComputeInstanceDataDisk]'},
+        'data_mounts': {'key': 'dataMounts', 'type': '[ComputeInstanceDataMount]'},
+        'versions': {'key': 'versions', 'type': 'ComputeInstanceVersion'},
+    }
+
+    def __init__(
+        self,
+        *,
+        vm_size: Optional[str] = None,
+        subnet: Optional["ResourceId"] = None,
+        application_sharing_policy: Optional[Union[str, "ApplicationSharingPolicy"]] = "Shared",
+        ssh_settings: Optional["ComputeInstanceSshSettings"] = None,
+        compute_instance_authorization_type: Optional[Union[str, "ComputeInstanceAuthorizationType"]] = "personal",
+        personal_compute_instance_settings: Optional["PersonalComputeInstanceSettings"] = None,
+        setup_scripts: Optional["SetupScripts"] = None,
+        enable_node_public_ip: Optional[bool] = None,
+        **kwargs
+    ):
+        """
+        :keyword vm_size: Virtual Machine Size.
+        :paramtype vm_size: str
+        :keyword subnet: Virtual network subnet resource ID the compute nodes belong to.
+        :paramtype subnet: ~azure.mgmt.machinelearningservices.models.ResourceId
+        :keyword application_sharing_policy: Policy for sharing applications on this compute instance
+         among users of parent workspace. If Personal, only the creator can access applications on this
+         compute instance. When Shared, any workspace user can access applications on this instance
+         depending on his/her assigned role. Possible values include: "Personal", "Shared". Default
+         value: "Shared".
+        :paramtype application_sharing_policy: str or
+         ~azure.mgmt.machinelearningservices.models.ApplicationSharingPolicy
+        :keyword ssh_settings: Specifies policy and settings for SSH access.
+        :paramtype ssh_settings: ~azure.mgmt.machinelearningservices.models.ComputeInstanceSshSettings
+        :keyword compute_instance_authorization_type: The Compute Instance Authorization type.
+         Available values are personal (default). Possible values include: "personal". Default value:
+         "personal".
+        :paramtype compute_instance_authorization_type: str or
+         ~azure.mgmt.machinelearningservices.models.ComputeInstanceAuthorizationType
+        :keyword personal_compute_instance_settings: Settings for a personal compute instance.
+        :paramtype personal_compute_instance_settings:
+         ~azure.mgmt.machinelearningservices.models.PersonalComputeInstanceSettings
+        :keyword setup_scripts: Details of customized scripts to execute for setting up the cluster.
+        :paramtype setup_scripts: ~azure.mgmt.machinelearningservices.models.SetupScripts
+        :keyword enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
+         values are: Possible values are: true - Indicates that the compute nodes will have public IPs
+         provisioned. false - Indicates that the compute nodes will have a private endpoint and no
+         public IPs.
+        :paramtype enable_node_public_ip: bool
+        """
+        super(ComputeInstanceProperties, self).__init__(**kwargs)
+        self.vm_size = vm_size
+        self.subnet = subnet
+        self.application_sharing_policy = application_sharing_policy
+        self.ssh_settings = ssh_settings
+        self.connectivity_endpoints = None
+        self.applications = None
+        self.created_by = None
+        self.errors = None
+        self.state = None
+        self.compute_instance_authorization_type = compute_instance_authorization_type
+        self.personal_compute_instance_settings = personal_compute_instance_settings
+        self.setup_scripts = setup_scripts
+        self.last_operation = None
+        self.schedules = None
+        self.enable_node_public_ip = enable_node_public_ip
+        self.containers = None
+        self.data_disks = None
+        self.data_mounts = None
+        self.versions = None
+
+
+class ComputeInstanceSshSettings(msrest.serialization.Model):
+    """Specifies policy and settings for SSH access.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar ssh_public_access: State of the public SSH port. Possible values are: Disabled -
+     Indicates that the public ssh port is closed on this instance. Enabled - Indicates that the
+     public ssh port is open and accessible according to the VNet/subnet policy if applicable.
+     Possible values include: "Enabled", "Disabled". Default value: "Disabled".
+    :vartype ssh_public_access: str or ~azure.mgmt.machinelearningservices.models.SshPublicAccess
+    :ivar admin_user_name: Describes the admin user name.
+    :vartype admin_user_name: str
+    :ivar ssh_port: Describes the port for connecting through SSH.
+    :vartype ssh_port: int
+    :ivar admin_public_key: Specifies the SSH rsa public key file as a string. Use "ssh-keygen -t
+     rsa -b 2048" to generate your SSH key pairs.
+    :vartype admin_public_key: str
+    """
+
+    _validation = {
+        'admin_user_name': {'readonly': True},
+        'ssh_port': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'ssh_public_access': {'key': 'sshPublicAccess', 'type': 'str'},
+        'admin_user_name': {'key': 'adminUserName', 'type': 'str'},
+        'ssh_port': {'key': 'sshPort', 'type': 'int'},
+        'admin_public_key': {'key': 'adminPublicKey', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        ssh_public_access: Optional[Union[str, "SshPublicAccess"]] = "Disabled",
+        admin_public_key: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword ssh_public_access: State of the public SSH port. Possible values are: Disabled -
+         Indicates that the public ssh port is closed on this instance. Enabled - Indicates that the
+         public ssh port is open and accessible according to the VNet/subnet policy if applicable.
+         Possible values include: "Enabled", "Disabled". Default value: "Disabled".
+        :paramtype ssh_public_access: str or ~azure.mgmt.machinelearningservices.models.SshPublicAccess
+        :keyword admin_public_key: Specifies the SSH rsa public key file as a string. Use "ssh-keygen
+         -t rsa -b 2048" to generate your SSH key pairs.
+        :paramtype admin_public_key: str
+        """
+        super(ComputeInstanceSshSettings, self).__init__(**kwargs)
+        self.ssh_public_access = ssh_public_access
+        self.admin_user_name = None
+        self.ssh_port = None
+        self.admin_public_key = admin_public_key
+
+
+class ComputeInstanceVersion(msrest.serialization.Model):
+    """Version of computeInstance.
+
+    :ivar runtime: Runtime of compute instance.
+    :vartype runtime: str
+    """
+
+    _attribute_map = {
+        'runtime': {'key': 'runtime', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        runtime: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword runtime: Runtime of compute instance.
+        :paramtype runtime: str
+        """
+        super(ComputeInstanceVersion, self).__init__(**kwargs)
+        self.runtime = runtime
+
+
+class ComputeResourceSchema(msrest.serialization.Model):
+    """ComputeResourceSchema.
+
+    :ivar properties: Compute properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.Compute
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'Compute'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["Compute"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Compute properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.Compute
+        """
+        super(ComputeResourceSchema, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class ComputeResource(Resource, ComputeResourceSchema):
+    """Machine Learning compute object wrapped into ARM resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar properties: Compute properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.Compute
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar identity: The identity of the resource.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar location: Specifies the location of the resource.
+    :vartype location: str
+    :ivar tags: A set of tags. Contains resource tags defined as key/value pairs.
+    :vartype tags: dict[str, str]
+    :ivar sku: The sku of the workspace.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'Compute'},
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'location': {'key': 'location', 'type': 'str'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["Compute"] = None,
+        identity: Optional["ManagedServiceIdentity"] = None,
+        location: Optional[str] = None,
+        tags: Optional[Dict[str, str]] = None,
+        sku: Optional["Sku"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Compute properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.Compute
+        :keyword identity: The identity of the resource.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword location: Specifies the location of the resource.
+        :paramtype location: str
+        :keyword tags: A set of tags. Contains resource tags defined as key/value pairs.
+        :paramtype tags: dict[str, str]
+        :keyword sku: The sku of the workspace.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super(ComputeResource, self).__init__(properties=properties, **kwargs)
+        self.properties = properties
+        self.identity = identity
+        self.location = location
+        self.tags = tags
+        self.sku = sku
+        self.id = None
+        self.name = None
+        self.type = None
+        self.system_data = None
+
+
+class ComputeSchedules(msrest.serialization.Model):
+    """The list of schedules to be applied on the computes.
+
+    :ivar compute_start_stop: The list of compute start stop schedules to be applied.
+    :vartype compute_start_stop:
+     list[~azure.mgmt.machinelearningservices.models.ComputeStartStopSchedule]
+    """
+
+    _attribute_map = {
+        'compute_start_stop': {'key': 'computeStartStop', 'type': '[ComputeStartStopSchedule]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        compute_start_stop: Optional[List["ComputeStartStopSchedule"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword compute_start_stop: The list of compute start stop schedules to be applied.
+        :paramtype compute_start_stop:
+         list[~azure.mgmt.machinelearningservices.models.ComputeStartStopSchedule]
+        """
+        super(ComputeSchedules, self).__init__(**kwargs)
+        self.compute_start_stop = compute_start_stop
+
+
+class ComputeStartStopSchedule(msrest.serialization.Model):
+    """Compute start stop schedule properties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: A system assigned id for the schedule.
+    :vartype id: str
+    :ivar provisioning_status: The current deployment state of schedule. Possible values include:
+     "Completed", "Provisioning", "Failed".
+    :vartype provisioning_status: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningStatus
+    :ivar status: Is the schedule enabled or disabled?. Possible values include: "Enabled",
+     "Disabled".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.ScheduleStatus
+    :ivar action: [Required] The compute power action. Possible values include: "Start", "Stop".
+    :vartype action: str or ~azure.mgmt.machinelearningservices.models.ComputePowerAction
+    :ivar trigger_type: [Required] The schedule trigger type. Possible values include:
+     "Recurrence", "Cron".
+    :vartype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
+    :ivar recurrence: Required if triggerType is Recurrence.
+    :vartype recurrence: ~azure.mgmt.machinelearningservices.models.RecurrenceTrigger
+    :ivar cron: Required if triggerType is Cron.
+    :vartype cron: ~azure.mgmt.machinelearningservices.models.CronTrigger
+    :ivar schedule: [Deprecated] Not used any more.
+    :vartype schedule: ~azure.mgmt.machinelearningservices.models.ScheduleBase
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'provisioning_status': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'provisioning_status': {'key': 'provisioningStatus', 'type': 'str'},
+        'status': {'key': 'status', 'type': 'str'},
+        'action': {'key': 'action', 'type': 'str'},
+        'trigger_type': {'key': 'triggerType', 'type': 'str'},
+        'recurrence': {'key': 'recurrence', 'type': 'RecurrenceTrigger'},
+        'cron': {'key': 'cron', 'type': 'CronTrigger'},
+        'schedule': {'key': 'schedule', 'type': 'ScheduleBase'},
+    }
+
+    def __init__(
+        self,
+        *,
+        status: Optional[Union[str, "ScheduleStatus"]] = None,
+        action: Optional[Union[str, "ComputePowerAction"]] = None,
+        trigger_type: Optional[Union[str, "TriggerType"]] = None,
+        recurrence: Optional["RecurrenceTrigger"] = None,
+        cron: Optional["CronTrigger"] = None,
+        schedule: Optional["ScheduleBase"] = None,
+        **kwargs
+    ):
+        """
+        :keyword status: Is the schedule enabled or disabled?. Possible values include: "Enabled",
+         "Disabled".
+        :paramtype status: str or ~azure.mgmt.machinelearningservices.models.ScheduleStatus
+        :keyword action: [Required] The compute power action. Possible values include: "Start", "Stop".
+        :paramtype action: str or ~azure.mgmt.machinelearningservices.models.ComputePowerAction
+        :keyword trigger_type: [Required] The schedule trigger type. Possible values include:
+         "Recurrence", "Cron".
+        :paramtype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
+        :keyword recurrence: Required if triggerType is Recurrence.
+        :paramtype recurrence: ~azure.mgmt.machinelearningservices.models.RecurrenceTrigger
+        :keyword cron: Required if triggerType is Cron.
+        :paramtype cron: ~azure.mgmt.machinelearningservices.models.CronTrigger
+        :keyword schedule: [Deprecated] Not used any more.
+        :paramtype schedule: ~azure.mgmt.machinelearningservices.models.ScheduleBase
+        """
+        super(ComputeStartStopSchedule, self).__init__(**kwargs)
+        self.id = None
+        self.provisioning_status = None
+        self.status = status
+        self.action = action
+        self.trigger_type = trigger_type
+        self.recurrence = recurrence
+        self.cron = cron
+        self.schedule = schedule
+
+
+class ContainerResourceRequirements(msrest.serialization.Model):
+    """Resource requirements for each container instance within an online deployment.
+
+    :ivar container_resource_limits: Container resource limit info:.
+    :vartype container_resource_limits:
+     ~azure.mgmt.machinelearningservices.models.ContainerResourceSettings
+    :ivar container_resource_requests: Container resource request info:.
+    :vartype container_resource_requests:
+     ~azure.mgmt.machinelearningservices.models.ContainerResourceSettings
+    """
+
+    _attribute_map = {
+        'container_resource_limits': {'key': 'containerResourceLimits', 'type': 'ContainerResourceSettings'},
+        'container_resource_requests': {'key': 'containerResourceRequests', 'type': 'ContainerResourceSettings'},
+    }
+
+    def __init__(
+        self,
+        *,
+        container_resource_limits: Optional["ContainerResourceSettings"] = None,
+        container_resource_requests: Optional["ContainerResourceSettings"] = None,
+        **kwargs
+    ):
+        """
+        :keyword container_resource_limits: Container resource limit info:.
+        :paramtype container_resource_limits:
+         ~azure.mgmt.machinelearningservices.models.ContainerResourceSettings
+        :keyword container_resource_requests: Container resource request info:.
+        :paramtype container_resource_requests:
+         ~azure.mgmt.machinelearningservices.models.ContainerResourceSettings
+        """
+        super(ContainerResourceRequirements, self).__init__(**kwargs)
+        self.container_resource_limits = container_resource_limits
+        self.container_resource_requests = container_resource_requests
+
+
+class ContainerResourceSettings(msrest.serialization.Model):
+    """ContainerResourceSettings.
+
+    :ivar cpu: Number of vCPUs request/limit for container. More info:
+     https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+    :vartype cpu: str
+    :ivar gpu: Number of Nvidia GPU cards request/limit for container. More info:
+     https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+    :vartype gpu: str
+    :ivar memory: Memory size request/limit for container. More info:
+     https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+    :vartype memory: str
+    """
+
+    _attribute_map = {
+        'cpu': {'key': 'cpu', 'type': 'str'},
+        'gpu': {'key': 'gpu', 'type': 'str'},
+        'memory': {'key': 'memory', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        cpu: Optional[str] = None,
+        gpu: Optional[str] = None,
+        memory: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword cpu: Number of vCPUs request/limit for container. More info:
+         https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+        :paramtype cpu: str
+        :keyword gpu: Number of Nvidia GPU cards request/limit for container. More info:
+         https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+        :paramtype gpu: str
+        :keyword memory: Memory size request/limit for container. More info:
+         https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
+        :paramtype memory: str
+        """
+        super(ContainerResourceSettings, self).__init__(**kwargs)
+        self.cpu = cpu
+        self.gpu = gpu
+        self.memory = memory
+
+
+class CosmosDbSettings(msrest.serialization.Model):
+    """CosmosDbSettings.
+
+    :ivar collections_throughput: The throughput of the collections in cosmosdb database.
+    :vartype collections_throughput: int
+    """
+
+    _attribute_map = {
+        'collections_throughput': {'key': 'collectionsThroughput', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        collections_throughput: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword collections_throughput: The throughput of the collections in cosmosdb database.
+        :paramtype collections_throughput: int
+        """
+        super(CosmosDbSettings, self).__init__(**kwargs)
+        self.collections_throughput = collections_throughput
+
+
+class TriggerBase(msrest.serialization.Model):
+    """TriggerBase.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: CronTrigger, RecurrenceTrigger.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+     https://en.wikipedia.org/wiki/ISO_8601.
+     Recommented format would be "2022-06-01T00:00:01"
+     If not present, the schedule will run indefinitely.
+    :vartype end_time: str
+    :ivar start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+     offset.
+    :vartype start_time: str
+    :ivar time_zone: Specifies time zone in which the schedule runs.
+     TimeZone should follow Windows time zone format. Refer:
+     https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+    :vartype time_zone: str
+    :ivar trigger_type: Required. [Required].Constant filled by server. Possible values include:
+     "Recurrence", "Cron".
+    :vartype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
+    """
+
+    _validation = {
+        'trigger_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'end_time': {'key': 'endTime', 'type': 'str'},
+        'start_time': {'key': 'startTime', 'type': 'str'},
+        'time_zone': {'key': 'timeZone', 'type': 'str'},
+        'trigger_type': {'key': 'triggerType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'trigger_type': {'Cron': 'CronTrigger', 'Recurrence': 'RecurrenceTrigger'}
+    }
+
+    def __init__(
+        self,
+        *,
+        end_time: Optional[str] = None,
+        start_time: Optional[str] = None,
+        time_zone: Optional[str] = "UTC",
+        **kwargs
+    ):
+        """
+        :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+         https://en.wikipedia.org/wiki/ISO_8601.
+         Recommented format would be "2022-06-01T00:00:01"
+         If not present, the schedule will run indefinitely.
+        :paramtype end_time: str
+        :keyword start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+         offset.
+        :paramtype start_time: str
+        :keyword time_zone: Specifies time zone in which the schedule runs.
+         TimeZone should follow Windows time zone format. Refer:
+         https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+        :paramtype time_zone: str
+        """
+        super(TriggerBase, self).__init__(**kwargs)
+        self.end_time = end_time
+        self.start_time = start_time
+        self.time_zone = time_zone
+        self.trigger_type = None  # type: Optional[str]
+
+
+class CronTrigger(TriggerBase):
+    """CronTrigger.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+     https://en.wikipedia.org/wiki/ISO_8601.
+     Recommented format would be "2022-06-01T00:00:01"
+     If not present, the schedule will run indefinitely.
+    :vartype end_time: str
+    :ivar start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+     offset.
+    :vartype start_time: str
+    :ivar time_zone: Specifies time zone in which the schedule runs.
+     TimeZone should follow Windows time zone format. Refer:
+     https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+    :vartype time_zone: str
+    :ivar trigger_type: Required. [Required].Constant filled by server. Possible values include:
+     "Recurrence", "Cron".
+    :vartype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
+    :ivar expression: Required. [Required] Specifies cron expression of schedule.
+     The expression should follow NCronTab format.
+    :vartype expression: str
+    """
+
+    _validation = {
+        'trigger_type': {'required': True},
+        'expression': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'end_time': {'key': 'endTime', 'type': 'str'},
+        'start_time': {'key': 'startTime', 'type': 'str'},
+        'time_zone': {'key': 'timeZone', 'type': 'str'},
+        'trigger_type': {'key': 'triggerType', 'type': 'str'},
+        'expression': {'key': 'expression', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        expression: str,
+        end_time: Optional[str] = None,
+        start_time: Optional[str] = None,
+        time_zone: Optional[str] = "UTC",
+        **kwargs
+    ):
+        """
+        :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+         https://en.wikipedia.org/wiki/ISO_8601.
+         Recommented format would be "2022-06-01T00:00:01"
+         If not present, the schedule will run indefinitely.
+        :paramtype end_time: str
+        :keyword start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+         offset.
+        :paramtype start_time: str
+        :keyword time_zone: Specifies time zone in which the schedule runs.
+         TimeZone should follow Windows time zone format. Refer:
+         https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+        :paramtype time_zone: str
+        :keyword expression: Required. [Required] Specifies cron expression of schedule.
+         The expression should follow NCronTab format.
+        :paramtype expression: str
+        """
+        super(CronTrigger, self).__init__(end_time=end_time, start_time=start_time, time_zone=time_zone, **kwargs)
+        self.trigger_type = 'Cron'  # type: str
+        self.expression = expression
+
+
+class CustomForecastHorizon(ForecastHorizon):
+    """The desired maximum forecast horizon in units of time-series frequency.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set forecast horizon value selection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.ForecastHorizonMode
+    :ivar value: Required. [Required] Forecast horizon value.
+    :vartype value: int
+    """
+
+    _validation = {
+        'mode': {'required': True},
+        'value': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: int,
+        **kwargs
+    ):
+        """
+        :keyword value: Required. [Required] Forecast horizon value.
+        :paramtype value: int
+        """
+        super(CustomForecastHorizon, self).__init__(**kwargs)
+        self.mode = 'Custom'  # type: str
+        self.value = value
+
+
+class JobInput(msrest.serialization.Model):
+    """Command job definition.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: CustomModelJobInput, LiteralJobInput, MLFlowModelJobInput, MLTableJobInput, TritonModelJobInput, UriFileJobInput, UriFolderJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'job_input_type': {'custom_model': 'CustomModelJobInput', 'literal': 'LiteralJobInput', 'mlflow_model': 'MLFlowModelJobInput', 'mltable': 'MLTableJobInput', 'triton_model': 'TritonModelJobInput', 'uri_file': 'UriFileJobInput', 'uri_folder': 'UriFolderJobInput'}
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(JobInput, self).__init__(**kwargs)
+        self.description = description
+        self.job_input_type = None  # type: Optional[str]
+
+
+class CustomModelJobInput(JobInput, AssetJobInput):
+    """CustomModelJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        uri: str,
+        mode: Optional[Union[str, "InputDeliveryMode"]] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(CustomModelJobInput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_input_type = 'custom_model'  # type: str
+        self.description = description
+
+
+class JobOutput(msrest.serialization.Model):
+    """Job output definition container information on where to find job output/logs.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: CustomModelJobOutput, MLFlowModelJobOutput, MLTableJobOutput, TritonModelJobOutput, UriFileJobOutput, UriFolderJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'job_output_type': {'custom_model': 'CustomModelJobOutput', 'mlflow_model': 'MLFlowModelJobOutput', 'mltable': 'MLTableJobOutput', 'triton_model': 'TritonModelJobOutput', 'uri_file': 'UriFileJobOutput', 'uri_folder': 'UriFolderJobOutput'}
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(JobOutput, self).__init__(**kwargs)
+        self.description = description
+        self.job_output_type = None  # type: Optional[str]
+
+
+class CustomModelJobOutput(JobOutput, AssetJobOutput):
+    """CustomModelJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        mode: Optional[Union[str, "OutputDeliveryMode"]] = None,
+        uri: Optional[str] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(CustomModelJobOutput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_output_type = 'custom_model'  # type: str
+        self.description = description
+
+
+class CustomNCrossValidations(NCrossValidations):
+    """N-Cross validations are specified by user.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Mode for determining N-Cross validations.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.NCrossValidationsMode
+    :ivar value: Required. [Required] N-Cross validations value.
+    :vartype value: int
+    """
+
+    _validation = {
+        'mode': {'required': True},
+        'value': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: int,
+        **kwargs
+    ):
+        """
+        :keyword value: Required. [Required] N-Cross validations value.
+        :paramtype value: int
+        """
+        super(CustomNCrossValidations, self).__init__(**kwargs)
+        self.mode = 'Custom'  # type: str
+        self.value = value
+
+
+class CustomSeasonality(Seasonality):
+    """CustomSeasonality.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Seasonality mode.Constant filled by server. Possible values
+     include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.SeasonalityMode
+    :ivar value: Required. [Required] Seasonality value.
+    :vartype value: int
+    """
+
+    _validation = {
+        'mode': {'required': True},
+        'value': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: int,
+        **kwargs
+    ):
+        """
+        :keyword value: Required. [Required] Seasonality value.
+        :paramtype value: int
+        """
+        super(CustomSeasonality, self).__init__(**kwargs)
+        self.mode = 'Custom'  # type: str
+        self.value = value
+
+
+class CustomTargetLags(TargetLags):
+    """CustomTargetLags.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] Set target lags mode - Auto/Custom.Constant filled by server.
+     Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetLagsMode
+    :ivar values: Required. [Required] Set target lags values.
+    :vartype values: list[int]
+    """
+
+    _validation = {
+        'mode': {'required': True},
+        'values': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'values': {'key': 'values', 'type': '[int]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        values: List[int],
+        **kwargs
+    ):
+        """
+        :keyword values: Required. [Required] Set target lags values.
+        :paramtype values: list[int]
+        """
+        super(CustomTargetLags, self).__init__(**kwargs)
+        self.mode = 'Custom'  # type: str
+        self.values = values
+
+
+class CustomTargetRollingWindowSize(TargetRollingWindowSize):
+    """CustomTargetRollingWindowSize.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Required. [Required] TargetRollingWindowSiz detection mode.Constant filled by
+     server. Possible values include: "Auto", "Custom".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.TargetRollingWindowSizeMode
+    :ivar value: Required. [Required] TargetRollingWindowSize value.
+    :vartype value: int
+    """
+
+    _validation = {
+        'mode': {'required': True},
+        'value': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: int,
+        **kwargs
+    ):
+        """
+        :keyword value: Required. [Required] TargetRollingWindowSize value.
+        :paramtype value: int
+        """
+        super(CustomTargetRollingWindowSize, self).__init__(**kwargs)
+        self.mode = 'Custom'  # type: str
+        self.value = value
+
+
+class DatabricksSchema(msrest.serialization.Model):
+    """DatabricksSchema.
+
+    :ivar properties: Properties of Databricks.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'DatabricksProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["DatabricksProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of Databricks.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
+        """
+        super(DatabricksSchema, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class Databricks(Compute, DatabricksSchema):
+    """A DataFactory compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: Properties of Databricks.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'DatabricksProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["DatabricksProperties"] = None,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of Databricks.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(Databricks, self).__init__(description=description, resource_id=resource_id, properties=properties, **kwargs)
+        self.properties = properties
+        self.compute_type = 'Databricks'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = description
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = resource_id
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class DatabricksComputeSecretsProperties(msrest.serialization.Model):
+    """Properties of Databricks Compute Secrets.
+
+    :ivar databricks_access_token: access token for databricks account.
+    :vartype databricks_access_token: str
+    """
+
+    _attribute_map = {
+        'databricks_access_token': {'key': 'databricksAccessToken', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        databricks_access_token: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword databricks_access_token: access token for databricks account.
+        :paramtype databricks_access_token: str
+        """
+        super(DatabricksComputeSecretsProperties, self).__init__(**kwargs)
+        self.databricks_access_token = databricks_access_token
+
+
+class DatabricksComputeSecrets(ComputeSecrets, DatabricksComputeSecretsProperties):
+    """Secrets related to a Machine Learning compute based on Databricks.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar databricks_access_token: access token for databricks account.
+    :vartype databricks_access_token: str
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'databricks_access_token': {'key': 'databricksAccessToken', 'type': 'str'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        databricks_access_token: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword databricks_access_token: access token for databricks account.
+        :paramtype databricks_access_token: str
+        """
+        super(DatabricksComputeSecrets, self).__init__(databricks_access_token=databricks_access_token, **kwargs)
+        self.databricks_access_token = databricks_access_token
+        self.compute_type = 'Databricks'  # type: str
+
+
+class DatabricksProperties(msrest.serialization.Model):
+    """Properties of Databricks.
+
+    :ivar databricks_access_token: Databricks access token.
+    :vartype databricks_access_token: str
+    :ivar workspace_url: Workspace Url.
+    :vartype workspace_url: str
+    """
+
+    _attribute_map = {
+        'databricks_access_token': {'key': 'databricksAccessToken', 'type': 'str'},
+        'workspace_url': {'key': 'workspaceUrl', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        databricks_access_token: Optional[str] = None,
+        workspace_url: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword databricks_access_token: Databricks access token.
+        :paramtype databricks_access_token: str
+        :keyword workspace_url: Workspace Url.
+        :paramtype workspace_url: str
+        """
+        super(DatabricksProperties, self).__init__(**kwargs)
+        self.databricks_access_token = databricks_access_token
+        self.workspace_url = workspace_url
+
+
+class DataContainer(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.DataContainerProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'DataContainerProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "DataContainerProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.DataContainerProperties
+        """
+        super(DataContainer, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class DataContainerProperties(AssetContainer):
+    """Container for data asset versions.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    :ivar data_type: Required. [Required] Specifies the type of data. Possible values include:
+     "uri_file", "uri_folder", "mltable".
+    :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+        'data_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+        'data_type': {'key': 'dataType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        data_type: Union[str, "DataType"],
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_archived: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword data_type: Required. [Required] Specifies the type of data. Possible values include:
+         "uri_file", "uri_folder", "mltable".
+        :paramtype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+        """
+        super(DataContainerProperties, self).__init__(description=description, properties=properties, tags=tags, is_archived=is_archived, **kwargs)
+        self.data_type = data_type
+
+
+class DataContainerResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of DataContainer entities.
+
+    :ivar next_link: The link to the next page of DataContainer objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type DataContainer.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.DataContainer]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[DataContainer]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["DataContainer"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of DataContainer objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type DataContainer.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.DataContainer]
+        """
+        super(DataContainerResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class DataFactory(Compute):
+    """A DataFactory compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(DataFactory, self).__init__(description=description, resource_id=resource_id, **kwargs)
+        self.compute_type = 'DataFactory'  # type: str
+
+
+class DataLakeAnalyticsSchema(msrest.serialization.Model):
+    """DataLakeAnalyticsSchema.
+
+    :ivar properties:
+    :vartype properties:
+     ~azure.mgmt.machinelearningservices.models.DataLakeAnalyticsSchemaProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'DataLakeAnalyticsSchemaProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["DataLakeAnalyticsSchemaProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties:
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.DataLakeAnalyticsSchemaProperties
+        """
+        super(DataLakeAnalyticsSchema, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class DataLakeAnalytics(Compute, DataLakeAnalyticsSchema):
+    """A DataLakeAnalytics compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties:
+    :vartype properties:
+     ~azure.mgmt.machinelearningservices.models.DataLakeAnalyticsSchemaProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'DataLakeAnalyticsSchemaProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["DataLakeAnalyticsSchemaProperties"] = None,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties:
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.DataLakeAnalyticsSchemaProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(DataLakeAnalytics, self).__init__(description=description, resource_id=resource_id, properties=properties, **kwargs)
+        self.properties = properties
+        self.compute_type = 'DataLakeAnalytics'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = description
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = resource_id
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class DataLakeAnalyticsSchemaProperties(msrest.serialization.Model):
+    """DataLakeAnalyticsSchemaProperties.
+
+    :ivar data_lake_store_account_name: DataLake Store Account Name.
+    :vartype data_lake_store_account_name: str
+    """
+
+    _attribute_map = {
+        'data_lake_store_account_name': {'key': 'dataLakeStoreAccountName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        data_lake_store_account_name: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword data_lake_store_account_name: DataLake Store Account Name.
+        :paramtype data_lake_store_account_name: str
+        """
+        super(DataLakeAnalyticsSchemaProperties, self).__init__(**kwargs)
+        self.data_lake_store_account_name = data_lake_store_account_name
+
+
+class DataPathAssetReference(AssetReferenceBase):
+    """Reference to an asset via its path in a datastore.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar reference_type: Required. [Required] Specifies the type of asset reference.Constant
+     filled by server. Possible values include: "Id", "DataPath", "OutputPath".
+    :vartype reference_type: str or ~azure.mgmt.machinelearningservices.models.ReferenceType
+    :ivar datastore_id: ARM resource ID of the datastore where the asset is located.
+    :vartype datastore_id: str
+    :ivar path: The path of the file/directory in the datastore.
+    :vartype path: str
+    """
+
+    _validation = {
+        'reference_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'reference_type': {'key': 'referenceType', 'type': 'str'},
+        'datastore_id': {'key': 'datastoreId', 'type': 'str'},
+        'path': {'key': 'path', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        datastore_id: Optional[str] = None,
+        path: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword datastore_id: ARM resource ID of the datastore where the asset is located.
+        :paramtype datastore_id: str
+        :keyword path: The path of the file/directory in the datastore.
+        :paramtype path: str
+        """
+        super(DataPathAssetReference, self).__init__(**kwargs)
+        self.reference_type = 'DataPath'  # type: str
+        self.datastore_id = datastore_id
+        self.path = path
+
+
+class Datastore(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.DatastoreProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'DatastoreProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "DatastoreProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatastoreProperties
+        """
+        super(Datastore, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class DatastoreResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of Datastore entities.
+
+    :ivar next_link: The link to the next page of Datastore objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type Datastore.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Datastore]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[Datastore]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["Datastore"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of Datastore objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type Datastore.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.Datastore]
+        """
+        super(DatastoreResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class DataVersionBase(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.DataVersionBaseProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'DataVersionBaseProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "DataVersionBaseProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.DataVersionBaseProperties
+        """
+        super(DataVersionBase, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class DataVersionBaseProperties(AssetBase):
+    """Data version base definition.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: MLTableData, UriFileDataVersion, UriFolderDataVersion.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar data_type: Required. [Required] Specifies the type of data.Constant filled by server.
+     Possible values include: "uri_file", "uri_folder", "mltable".
+    :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+    :ivar data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+    :vartype data_uri: str
+    """
+
+    _validation = {
+        'data_type': {'required': True},
+        'data_uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'data_type': {'key': 'dataType', 'type': 'str'},
+        'data_uri': {'key': 'dataUri', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'data_type': {'mltable': 'MLTableData', 'uri_file': 'UriFileDataVersion', 'uri_folder': 'UriFolderDataVersion'}
+    }
+
+    def __init__(
+        self,
+        *,
+        data_uri: str,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_anonymous: Optional[bool] = False,
+        is_archived: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+        :paramtype data_uri: str
+        """
+        super(DataVersionBaseProperties, self).__init__(description=description, properties=properties, tags=tags, is_anonymous=is_anonymous, is_archived=is_archived, **kwargs)
+        self.data_type = 'DataVersionBaseProperties'  # type: str
+        self.data_uri = data_uri
+
+
+class DataVersionBaseResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of DataVersionBase entities.
+
+    :ivar next_link: The link to the next page of DataVersionBase objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type DataVersionBase.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.DataVersionBase]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[DataVersionBase]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["DataVersionBase"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of DataVersionBase objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type DataVersionBase.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.DataVersionBase]
+        """
+        super(DataVersionBaseResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class OnlineScaleSettings(msrest.serialization.Model):
+    """Online deployment scaling configuration.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: DefaultScaleSettings, TargetUtilizationScaleSettings.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar scale_type: Required. [Required] Type of deployment scaling algorithm.Constant filled by
+     server. Possible values include: "Default", "TargetUtilization".
+    :vartype scale_type: str or ~azure.mgmt.machinelearningservices.models.ScaleType
+    """
+
+    _validation = {
+        'scale_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'scale_type': {'key': 'scaleType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'scale_type': {'Default': 'DefaultScaleSettings', 'TargetUtilization': 'TargetUtilizationScaleSettings'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(OnlineScaleSettings, self).__init__(**kwargs)
+        self.scale_type = None  # type: Optional[str]
+
+
+class DefaultScaleSettings(OnlineScaleSettings):
+    """DefaultScaleSettings.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar scale_type: Required. [Required] Type of deployment scaling algorithm.Constant filled by
+     server. Possible values include: "Default", "TargetUtilization".
+    :vartype scale_type: str or ~azure.mgmt.machinelearningservices.models.ScaleType
+    """
+
+    _validation = {
+        'scale_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'scale_type': {'key': 'scaleType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(DefaultScaleSettings, self).__init__(**kwargs)
+        self.scale_type = 'Default'  # type: str
+
+
+class DeploymentLogs(msrest.serialization.Model):
+    """DeploymentLogs.
+
+    :ivar content: The retrieved online deployment logs.
+    :vartype content: str
+    """
+
+    _attribute_map = {
+        'content': {'key': 'content', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        content: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword content: The retrieved online deployment logs.
+        :paramtype content: str
+        """
+        super(DeploymentLogs, self).__init__(**kwargs)
+        self.content = content
+
+
+class DeploymentLogsRequest(msrest.serialization.Model):
+    """DeploymentLogsRequest.
+
+    :ivar container_type: The type of container to retrieve logs from. Possible values include:
+     "StorageInitializer", "InferenceServer".
+    :vartype container_type: str or ~azure.mgmt.machinelearningservices.models.ContainerType
+    :ivar tail: The maximum number of lines to tail.
+    :vartype tail: int
+    """
+
+    _attribute_map = {
+        'container_type': {'key': 'containerType', 'type': 'str'},
+        'tail': {'key': 'tail', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        container_type: Optional[Union[str, "ContainerType"]] = None,
+        tail: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword container_type: The type of container to retrieve logs from. Possible values include:
+         "StorageInitializer", "InferenceServer".
+        :paramtype container_type: str or ~azure.mgmt.machinelearningservices.models.ContainerType
+        :keyword tail: The maximum number of lines to tail.
+        :paramtype tail: int
+        """
+        super(DeploymentLogsRequest, self).__init__(**kwargs)
+        self.container_type = container_type
+        self.tail = tail
+
+
+class ResourceConfiguration(msrest.serialization.Model):
+    """ResourceConfiguration.
+
+    :ivar instance_count: Optional number of instances or nodes used by the compute target.
+    :vartype instance_count: int
+    :ivar instance_type: Optional type of VM used as supported by the compute target.
+    :vartype instance_type: str
+    :ivar properties: Additional properties bag.
+    :vartype properties: dict[str, any]
+    """
+
+    _attribute_map = {
+        'instance_count': {'key': 'instanceCount', 'type': 'int'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{object}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        instance_count: Optional[int] = 1,
+        instance_type: Optional[str] = None,
+        properties: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ):
+        """
+        :keyword instance_count: Optional number of instances or nodes used by the compute target.
+        :paramtype instance_count: int
+        :keyword instance_type: Optional type of VM used as supported by the compute target.
+        :paramtype instance_type: str
+        :keyword properties: Additional properties bag.
+        :paramtype properties: dict[str, any]
+        """
+        super(ResourceConfiguration, self).__init__(**kwargs)
+        self.instance_count = instance_count
+        self.instance_type = instance_type
+        self.properties = properties
+
+
+class DeploymentResourceConfiguration(ResourceConfiguration):
+    """DeploymentResourceConfiguration.
+
+    :ivar instance_count: Optional number of instances or nodes used by the compute target.
+    :vartype instance_count: int
+    :ivar instance_type: Optional type of VM used as supported by the compute target.
+    :vartype instance_type: str
+    :ivar properties: Additional properties bag.
+    :vartype properties: dict[str, any]
+    """
+
+    _attribute_map = {
+        'instance_count': {'key': 'instanceCount', 'type': 'int'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{object}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        instance_count: Optional[int] = 1,
+        instance_type: Optional[str] = None,
+        properties: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ):
+        """
+        :keyword instance_count: Optional number of instances or nodes used by the compute target.
+        :paramtype instance_count: int
+        :keyword instance_type: Optional type of VM used as supported by the compute target.
+        :paramtype instance_type: str
+        :keyword properties: Additional properties bag.
+        :paramtype properties: dict[str, any]
+        """
+        super(DeploymentResourceConfiguration, self).__init__(instance_count=instance_count, instance_type=instance_type, properties=properties, **kwargs)
+
+
+class DiagnoseRequestProperties(msrest.serialization.Model):
+    """DiagnoseRequestProperties.
+
+    :ivar udr: Setting for diagnosing user defined routing.
+    :vartype udr: dict[str, any]
+    :ivar nsg: Setting for diagnosing network security group.
+    :vartype nsg: dict[str, any]
+    :ivar resource_lock: Setting for diagnosing resource lock.
+    :vartype resource_lock: dict[str, any]
+    :ivar dns_resolution: Setting for diagnosing dns resolution.
+    :vartype dns_resolution: dict[str, any]
+    :ivar storage_account: Setting for diagnosing dependent storage account.
+    :vartype storage_account: dict[str, any]
+    :ivar key_vault: Setting for diagnosing dependent key vault.
+    :vartype key_vault: dict[str, any]
+    :ivar container_registry: Setting for diagnosing dependent container registry.
+    :vartype container_registry: dict[str, any]
+    :ivar application_insights: Setting for diagnosing dependent application insights.
+    :vartype application_insights: dict[str, any]
+    :ivar others: Setting for diagnosing unclassified category of problems.
+    :vartype others: dict[str, any]
+    """
+
+    _attribute_map = {
+        'udr': {'key': 'udr', 'type': '{object}'},
+        'nsg': {'key': 'nsg', 'type': '{object}'},
+        'resource_lock': {'key': 'resourceLock', 'type': '{object}'},
+        'dns_resolution': {'key': 'dnsResolution', 'type': '{object}'},
+        'storage_account': {'key': 'storageAccount', 'type': '{object}'},
+        'key_vault': {'key': 'keyVault', 'type': '{object}'},
+        'container_registry': {'key': 'containerRegistry', 'type': '{object}'},
+        'application_insights': {'key': 'applicationInsights', 'type': '{object}'},
+        'others': {'key': 'others', 'type': '{object}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        udr: Optional[Dict[str, Any]] = None,
+        nsg: Optional[Dict[str, Any]] = None,
+        resource_lock: Optional[Dict[str, Any]] = None,
+        dns_resolution: Optional[Dict[str, Any]] = None,
+        storage_account: Optional[Dict[str, Any]] = None,
+        key_vault: Optional[Dict[str, Any]] = None,
+        container_registry: Optional[Dict[str, Any]] = None,
+        application_insights: Optional[Dict[str, Any]] = None,
+        others: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ):
+        """
+        :keyword udr: Setting for diagnosing user defined routing.
+        :paramtype udr: dict[str, any]
+        :keyword nsg: Setting for diagnosing network security group.
+        :paramtype nsg: dict[str, any]
+        :keyword resource_lock: Setting for diagnosing resource lock.
+        :paramtype resource_lock: dict[str, any]
+        :keyword dns_resolution: Setting for diagnosing dns resolution.
+        :paramtype dns_resolution: dict[str, any]
+        :keyword storage_account: Setting for diagnosing dependent storage account.
+        :paramtype storage_account: dict[str, any]
+        :keyword key_vault: Setting for diagnosing dependent key vault.
+        :paramtype key_vault: dict[str, any]
+        :keyword container_registry: Setting for diagnosing dependent container registry.
+        :paramtype container_registry: dict[str, any]
+        :keyword application_insights: Setting for diagnosing dependent application insights.
+        :paramtype application_insights: dict[str, any]
+        :keyword others: Setting for diagnosing unclassified category of problems.
+        :paramtype others: dict[str, any]
+        """
+        super(DiagnoseRequestProperties, self).__init__(**kwargs)
+        self.udr = udr
+        self.nsg = nsg
+        self.resource_lock = resource_lock
+        self.dns_resolution = dns_resolution
+        self.storage_account = storage_account
+        self.key_vault = key_vault
+        self.container_registry = container_registry
+        self.application_insights = application_insights
+        self.others = others
+
+
+class DiagnoseResponseResult(msrest.serialization.Model):
+    """DiagnoseResponseResult.
+
+    :ivar value:
+    :vartype value: ~azure.mgmt.machinelearningservices.models.DiagnoseResponseResultValue
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': 'DiagnoseResponseResultValue'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional["DiagnoseResponseResultValue"] = None,
+        **kwargs
+    ):
+        """
+        :keyword value:
+        :paramtype value: ~azure.mgmt.machinelearningservices.models.DiagnoseResponseResultValue
+        """
+        super(DiagnoseResponseResult, self).__init__(**kwargs)
+        self.value = value
+
+
+class DiagnoseResponseResultValue(msrest.serialization.Model):
+    """DiagnoseResponseResultValue.
+
+    :ivar user_defined_route_results:
+    :vartype user_defined_route_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar network_security_rule_results:
+    :vartype network_security_rule_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar resource_lock_results:
+    :vartype resource_lock_results: list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar dns_resolution_results:
+    :vartype dns_resolution_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar storage_account_results:
+    :vartype storage_account_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar key_vault_results:
+    :vartype key_vault_results: list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar container_registry_results:
+    :vartype container_registry_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar application_insights_results:
+    :vartype application_insights_results:
+     list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    :ivar other_results:
+    :vartype other_results: list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+    """
+
+    _attribute_map = {
+        'user_defined_route_results': {'key': 'userDefinedRouteResults', 'type': '[DiagnoseResult]'},
+        'network_security_rule_results': {'key': 'networkSecurityRuleResults', 'type': '[DiagnoseResult]'},
+        'resource_lock_results': {'key': 'resourceLockResults', 'type': '[DiagnoseResult]'},
+        'dns_resolution_results': {'key': 'dnsResolutionResults', 'type': '[DiagnoseResult]'},
+        'storage_account_results': {'key': 'storageAccountResults', 'type': '[DiagnoseResult]'},
+        'key_vault_results': {'key': 'keyVaultResults', 'type': '[DiagnoseResult]'},
+        'container_registry_results': {'key': 'containerRegistryResults', 'type': '[DiagnoseResult]'},
+        'application_insights_results': {'key': 'applicationInsightsResults', 'type': '[DiagnoseResult]'},
+        'other_results': {'key': 'otherResults', 'type': '[DiagnoseResult]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        user_defined_route_results: Optional[List["DiagnoseResult"]] = None,
+        network_security_rule_results: Optional[List["DiagnoseResult"]] = None,
+        resource_lock_results: Optional[List["DiagnoseResult"]] = None,
+        dns_resolution_results: Optional[List["DiagnoseResult"]] = None,
+        storage_account_results: Optional[List["DiagnoseResult"]] = None,
+        key_vault_results: Optional[List["DiagnoseResult"]] = None,
+        container_registry_results: Optional[List["DiagnoseResult"]] = None,
+        application_insights_results: Optional[List["DiagnoseResult"]] = None,
+        other_results: Optional[List["DiagnoseResult"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword user_defined_route_results:
+        :paramtype user_defined_route_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword network_security_rule_results:
+        :paramtype network_security_rule_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword resource_lock_results:
+        :paramtype resource_lock_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword dns_resolution_results:
+        :paramtype dns_resolution_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword storage_account_results:
+        :paramtype storage_account_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword key_vault_results:
+        :paramtype key_vault_results: list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword container_registry_results:
+        :paramtype container_registry_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword application_insights_results:
+        :paramtype application_insights_results:
+         list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        :keyword other_results:
+        :paramtype other_results: list[~azure.mgmt.machinelearningservices.models.DiagnoseResult]
+        """
+        super(DiagnoseResponseResultValue, self).__init__(**kwargs)
+        self.user_defined_route_results = user_defined_route_results
+        self.network_security_rule_results = network_security_rule_results
+        self.resource_lock_results = resource_lock_results
+        self.dns_resolution_results = dns_resolution_results
+        self.storage_account_results = storage_account_results
+        self.key_vault_results = key_vault_results
+        self.container_registry_results = container_registry_results
+        self.application_insights_results = application_insights_results
+        self.other_results = other_results
+
+
+class DiagnoseResult(msrest.serialization.Model):
+    """Result of Diagnose.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar code: Code for workspace setup error.
+    :vartype code: str
+    :ivar level: Level of workspace setup error. Possible values include: "Warning", "Error",
+     "Information".
+    :vartype level: str or ~azure.mgmt.machinelearningservices.models.DiagnoseResultLevel
+    :ivar message: Message of workspace setup error.
+    :vartype message: str
+    """
+
+    _validation = {
+        'code': {'readonly': True},
+        'level': {'readonly': True},
+        'message': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code': {'key': 'code', 'type': 'str'},
+        'level': {'key': 'level', 'type': 'str'},
+        'message': {'key': 'message', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(DiagnoseResult, self).__init__(**kwargs)
+        self.code = None
+        self.level = None
+        self.message = None
+
+
+class DiagnoseWorkspaceParameters(msrest.serialization.Model):
+    """Parameters to diagnose a workspace.
+
+    :ivar value: Value of Parameters.
+    :vartype value: ~azure.mgmt.machinelearningservices.models.DiagnoseRequestProperties
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': 'DiagnoseRequestProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional["DiagnoseRequestProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword value: Value of Parameters.
+        :paramtype value: ~azure.mgmt.machinelearningservices.models.DiagnoseRequestProperties
+        """
+        super(DiagnoseWorkspaceParameters, self).__init__(**kwargs)
+        self.value = value
+
+
+class DistributionConfiguration(msrest.serialization.Model):
+    """Base definition for job distribution configuration.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: Mpi, PyTorch, TensorFlow.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar distribution_type: Required. [Required] Specifies the type of distribution
+     framework.Constant filled by server. Possible values include: "PyTorch", "TensorFlow", "Mpi".
+    :vartype distribution_type: str or ~azure.mgmt.machinelearningservices.models.DistributionType
+    """
+
+    _validation = {
+        'distribution_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'distribution_type': {'key': 'distributionType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'distribution_type': {'Mpi': 'Mpi', 'PyTorch': 'PyTorch', 'TensorFlow': 'TensorFlow'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(DistributionConfiguration, self).__init__(**kwargs)
+        self.distribution_type = None  # type: Optional[str]
+
+
+class EncryptionKeyVaultProperties(msrest.serialization.Model):
+    """EncryptionKeyVaultProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar key_vault_arm_id: Required. The ArmId of the keyVault where the customer owned encryption
+     key is present.
+    :vartype key_vault_arm_id: str
+    :ivar key_identifier: Required. Key vault uri to access the encryption key.
+    :vartype key_identifier: str
+    :ivar identity_client_id: For future use - The client id of the identity which will be used to
+     access key vault.
+    :vartype identity_client_id: str
+    """
+
+    _validation = {
+        'key_vault_arm_id': {'required': True},
+        'key_identifier': {'required': True},
+    }
+
+    _attribute_map = {
+        'key_vault_arm_id': {'key': 'keyVaultArmId', 'type': 'str'},
+        'key_identifier': {'key': 'keyIdentifier', 'type': 'str'},
+        'identity_client_id': {'key': 'identityClientId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        key_vault_arm_id: str,
+        key_identifier: str,
+        identity_client_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword key_vault_arm_id: Required. The ArmId of the keyVault where the customer owned
+         encryption key is present.
+        :paramtype key_vault_arm_id: str
+        :keyword key_identifier: Required. Key vault uri to access the encryption key.
+        :paramtype key_identifier: str
+        :keyword identity_client_id: For future use - The client id of the identity which will be used
+         to access key vault.
+        :paramtype identity_client_id: str
+        """
+        super(EncryptionKeyVaultProperties, self).__init__(**kwargs)
+        self.key_vault_arm_id = key_vault_arm_id
+        self.key_identifier = key_identifier
+        self.identity_client_id = identity_client_id
+
+
+class EncryptionProperty(msrest.serialization.Model):
+    """EncryptionProperty.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar status: Required. Indicates whether or not the encryption is enabled for the workspace.
+     Possible values include: "Enabled", "Disabled".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.EncryptionStatus
+    :ivar identity: The identity that will be used to access the key vault for encryption at rest.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityForCmk
+    :ivar key_vault_properties: Required. Customer Key vault properties.
+    :vartype key_vault_properties:
+     ~azure.mgmt.machinelearningservices.models.EncryptionKeyVaultProperties
+    """
+
+    _validation = {
+        'status': {'required': True},
+        'key_vault_properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'status': {'key': 'status', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityForCmk'},
+        'key_vault_properties': {'key': 'keyVaultProperties', 'type': 'EncryptionKeyVaultProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        status: Union[str, "EncryptionStatus"],
+        key_vault_properties: "EncryptionKeyVaultProperties",
+        identity: Optional["IdentityForCmk"] = None,
+        **kwargs
+    ):
+        """
+        :keyword status: Required. Indicates whether or not the encryption is enabled for the
+         workspace. Possible values include: "Enabled", "Disabled".
+        :paramtype status: str or ~azure.mgmt.machinelearningservices.models.EncryptionStatus
+        :keyword identity: The identity that will be used to access the key vault for encryption at
+         rest.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityForCmk
+        :keyword key_vault_properties: Required. Customer Key vault properties.
+        :paramtype key_vault_properties:
+         ~azure.mgmt.machinelearningservices.models.EncryptionKeyVaultProperties
+        """
+        super(EncryptionProperty, self).__init__(**kwargs)
+        self.status = status
+        self.identity = identity
+        self.key_vault_properties = key_vault_properties
+
+
+class EndpointAuthKeys(msrest.serialization.Model):
+    """Keys for endpoint authentication.
+
+    :ivar primary_key: The primary key.
+    :vartype primary_key: str
+    :ivar secondary_key: The secondary key.
+    :vartype secondary_key: str
+    """
+
+    _attribute_map = {
+        'primary_key': {'key': 'primaryKey', 'type': 'str'},
+        'secondary_key': {'key': 'secondaryKey', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        primary_key: Optional[str] = None,
+        secondary_key: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword primary_key: The primary key.
+        :paramtype primary_key: str
+        :keyword secondary_key: The secondary key.
+        :paramtype secondary_key: str
+        """
+        super(EndpointAuthKeys, self).__init__(**kwargs)
+        self.primary_key = primary_key
+        self.secondary_key = secondary_key
+
+
+class EndpointAuthToken(msrest.serialization.Model):
+    """Service Token.
+
+    :ivar access_token: Access token for endpoint authentication.
+    :vartype access_token: str
+    :ivar expiry_time_utc: Access token expiry time (UTC).
+    :vartype expiry_time_utc: long
+    :ivar refresh_after_time_utc: Refresh access token after time (UTC).
+    :vartype refresh_after_time_utc: long
+    :ivar token_type: Access token type.
+    :vartype token_type: str
+    """
+
+    _attribute_map = {
+        'access_token': {'key': 'accessToken', 'type': 'str'},
+        'expiry_time_utc': {'key': 'expiryTimeUtc', 'type': 'long'},
+        'refresh_after_time_utc': {'key': 'refreshAfterTimeUtc', 'type': 'long'},
+        'token_type': {'key': 'tokenType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        access_token: Optional[str] = None,
+        expiry_time_utc: Optional[int] = 0,
+        refresh_after_time_utc: Optional[int] = 0,
+        token_type: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword access_token: Access token for endpoint authentication.
+        :paramtype access_token: str
+        :keyword expiry_time_utc: Access token expiry time (UTC).
+        :paramtype expiry_time_utc: long
+        :keyword refresh_after_time_utc: Refresh access token after time (UTC).
+        :paramtype refresh_after_time_utc: long
+        :keyword token_type: Access token type.
+        :paramtype token_type: str
+        """
+        super(EndpointAuthToken, self).__init__(**kwargs)
+        self.access_token = access_token
+        self.expiry_time_utc = expiry_time_utc
+        self.refresh_after_time_utc = refresh_after_time_utc
+        self.token_type = token_type
+
+
+class ScheduleActionBase(msrest.serialization.Model):
+    """ScheduleActionBase.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: JobScheduleAction, EndpointScheduleAction.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar action_type: Required. [Required] Specifies the action type of the schedule.Constant
+     filled by server. Possible values include: "CreateJob", "InvokeBatchEndpoint".
+    :vartype action_type: str or ~azure.mgmt.machinelearningservices.models.ScheduleActionType
+    """
+
+    _validation = {
+        'action_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'action_type': {'key': 'actionType', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'action_type': {'CreateJob': 'JobScheduleAction', 'InvokeBatchEndpoint': 'EndpointScheduleAction'}
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ScheduleActionBase, self).__init__(**kwargs)
+        self.action_type = None  # type: Optional[str]
+
+
+class EndpointScheduleAction(ScheduleActionBase):
+    """EndpointScheduleAction.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar action_type: Required. [Required] Specifies the action type of the schedule.Constant
+     filled by server. Possible values include: "CreateJob", "InvokeBatchEndpoint".
+    :vartype action_type: str or ~azure.mgmt.machinelearningservices.models.ScheduleActionType
+    :ivar endpoint_invocation_definition: Required. [Required] Defines Schedule action definition
+     details.
+    
+    
+     .. raw:: html
+    
+        <see href="TBD" />.
+    :vartype endpoint_invocation_definition: any
+    """
+
+    _validation = {
+        'action_type': {'required': True},
+        'endpoint_invocation_definition': {'required': True},
+    }
+
+    _attribute_map = {
+        'action_type': {'key': 'actionType', 'type': 'str'},
+        'endpoint_invocation_definition': {'key': 'endpointInvocationDefinition', 'type': 'object'},
+    }
+
+    def __init__(
+        self,
+        *,
+        endpoint_invocation_definition: Any,
+        **kwargs
+    ):
+        """
+        :keyword endpoint_invocation_definition: Required. [Required] Defines Schedule action
+         definition details.
+        
+        
+         .. raw:: html
+        
+            <see href="TBD" />.
+        :paramtype endpoint_invocation_definition: any
+        """
+        super(EndpointScheduleAction, self).__init__(**kwargs)
+        self.action_type = 'InvokeBatchEndpoint'  # type: str
+        self.endpoint_invocation_definition = endpoint_invocation_definition
+
+
+class EnvironmentContainer(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.EnvironmentContainerProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'EnvironmentContainerProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "EnvironmentContainerProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.EnvironmentContainerProperties
+        """
+        super(EnvironmentContainer, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class EnvironmentContainerProperties(AssetContainer):
+    """Container for environment specification versions.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_archived: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(EnvironmentContainerProperties, self).__init__(description=description, properties=properties, tags=tags, is_archived=is_archived, **kwargs)
+
+
+class EnvironmentContainerResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of EnvironmentContainer entities.
+
+    :ivar next_link: The link to the next page of EnvironmentContainer objects. If null, there are
+     no additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type EnvironmentContainer.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[EnvironmentContainer]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["EnvironmentContainer"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of EnvironmentContainer objects. If null, there
+         are no additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type EnvironmentContainer.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+        """
+        super(EnvironmentContainerResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class EnvironmentVersion(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.EnvironmentVersionProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'EnvironmentVersionProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "EnvironmentVersionProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.EnvironmentVersionProperties
+        """
+        super(EnvironmentVersion, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class EnvironmentVersionProperties(AssetBase):
+    """Environment version details.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar auto_rebuild: Defines if image needs to be rebuilt based on base image changes. Possible
+     values include: "Disabled", "OnBaseImageUpdate".
+    :vartype auto_rebuild: str or ~azure.mgmt.machinelearningservices.models.AutoRebuildSetting
+    :ivar build: Configuration settings for Docker build context.
+    :vartype build: ~azure.mgmt.machinelearningservices.models.BuildContext
+    :ivar conda_file: Standard configuration file used by Conda that lets you install any kind of
+     package, including Python, R, and C/C++ packages.
+    
+    
+     .. raw:: html
+    
+        <see
+     href="https://repo2docker.readthedocs.io/en/latest/config_files.html#environment-yml-install-a-conda-environment"
+     />.
+    :vartype conda_file: str
+    :ivar environment_type: Environment type is either user managed or curated by the Azure ML
+     service
+    
+    
+     .. raw:: html
+    
+        <see
+     href="https://learn.microsoft.com/azure/machine-learning/resource-curated-environments"
+     />. Possible values include: "Curated", "UserCreated".
+    :vartype environment_type: str or ~azure.mgmt.machinelearningservices.models.EnvironmentType
+    :ivar image: Name of the image that will be used for the environment.
+    
+    
+     .. raw:: html
+    
+        <seealso
+     href="https://learn.microsoft.com/azure/machine-learning/how-to-deploy-custom-docker-image#use-a-custom-base-image"
+     />.
+    :vartype image: str
+    :ivar inference_config: Defines configuration specific to inference.
+    :vartype inference_config:
+     ~azure.mgmt.machinelearningservices.models.InferenceContainerProperties
+    :ivar os_type: The OS type of the environment. Possible values include: "Linux", "Windows".
+    :vartype os_type: str or ~azure.mgmt.machinelearningservices.models.OperatingSystemType
+    """
+
+    _validation = {
+        'environment_type': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'auto_rebuild': {'key': 'autoRebuild', 'type': 'str'},
+        'build': {'key': 'build', 'type': 'BuildContext'},
+        'conda_file': {'key': 'condaFile', 'type': 'str'},
+        'environment_type': {'key': 'environmentType', 'type': 'str'},
+        'image': {'key': 'image', 'type': 'str'},
+        'inference_config': {'key': 'inferenceConfig', 'type': 'InferenceContainerProperties'},
+        'os_type': {'key': 'osType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_anonymous: Optional[bool] = False,
+        is_archived: Optional[bool] = False,
+        auto_rebuild: Optional[Union[str, "AutoRebuildSetting"]] = None,
+        build: Optional["BuildContext"] = None,
+        conda_file: Optional[str] = None,
+        image: Optional[str] = None,
+        inference_config: Optional["InferenceContainerProperties"] = None,
+        os_type: Optional[Union[str, "OperatingSystemType"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword auto_rebuild: Defines if image needs to be rebuilt based on base image changes.
+         Possible values include: "Disabled", "OnBaseImageUpdate".
+        :paramtype auto_rebuild: str or ~azure.mgmt.machinelearningservices.models.AutoRebuildSetting
+        :keyword build: Configuration settings for Docker build context.
+        :paramtype build: ~azure.mgmt.machinelearningservices.models.BuildContext
+        :keyword conda_file: Standard configuration file used by Conda that lets you install any kind
+         of package, including Python, R, and C/C++ packages.
+        
+        
+         .. raw:: html
+        
+            <see
+         href="https://repo2docker.readthedocs.io/en/latest/config_files.html#environment-yml-install-a-conda-environment"
+         />.
+        :paramtype conda_file: str
+        :keyword image: Name of the image that will be used for the environment.
+        
+        
+         .. raw:: html
+        
+            <seealso
+         href="https://learn.microsoft.com/azure/machine-learning/how-to-deploy-custom-docker-image#use-a-custom-base-image"
+         />.
+        :paramtype image: str
+        :keyword inference_config: Defines configuration specific to inference.
+        :paramtype inference_config:
+         ~azure.mgmt.machinelearningservices.models.InferenceContainerProperties
+        :keyword os_type: The OS type of the environment. Possible values include: "Linux", "Windows".
+        :paramtype os_type: str or ~azure.mgmt.machinelearningservices.models.OperatingSystemType
+        """
+        super(EnvironmentVersionProperties, self).__init__(description=description, properties=properties, tags=tags, is_anonymous=is_anonymous, is_archived=is_archived, **kwargs)
+        self.auto_rebuild = auto_rebuild
+        self.build = build
+        self.conda_file = conda_file
+        self.environment_type = None
+        self.image = image
+        self.inference_config = inference_config
+        self.os_type = os_type
+
+
+class EnvironmentVersionResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of EnvironmentVersion entities.
+
+    :ivar next_link: The link to the next page of EnvironmentVersion objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type EnvironmentVersion.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[EnvironmentVersion]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["EnvironmentVersion"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of EnvironmentVersion objects. If null, there are
+         no additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type EnvironmentVersion.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+        """
+        super(EnvironmentVersionResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class ErrorAdditionalInfo(msrest.serialization.Model):
+    """The resource management error additional info.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar type: The additional info type.
+    :vartype type: str
+    :ivar info: The additional info.
+    :vartype info: any
+    """
+
+    _validation = {
+        'type': {'readonly': True},
+        'info': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'type': {'key': 'type', 'type': 'str'},
+        'info': {'key': 'info', 'type': 'object'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ErrorAdditionalInfo, self).__init__(**kwargs)
+        self.type = None
+        self.info = None
+
+
+class ErrorDetail(msrest.serialization.Model):
+    """The error detail.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar code: The error code.
+    :vartype code: str
+    :ivar message: The error message.
+    :vartype message: str
+    :ivar target: The error target.
+    :vartype target: str
+    :ivar details: The error details.
+    :vartype details: list[~azure.mgmt.machinelearningservices.models.ErrorDetail]
+    :ivar additional_info: The error additional info.
+    :vartype additional_info: list[~azure.mgmt.machinelearningservices.models.ErrorAdditionalInfo]
+    """
+
+    _validation = {
+        'code': {'readonly': True},
+        'message': {'readonly': True},
+        'target': {'readonly': True},
+        'details': {'readonly': True},
+        'additional_info': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code': {'key': 'code', 'type': 'str'},
+        'message': {'key': 'message', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'details': {'key': 'details', 'type': '[ErrorDetail]'},
+        'additional_info': {'key': 'additionalInfo', 'type': '[ErrorAdditionalInfo]'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ErrorDetail, self).__init__(**kwargs)
+        self.code = None
+        self.message = None
+        self.target = None
+        self.details = None
+        self.additional_info = None
+
+
+class ErrorResponse(msrest.serialization.Model):
+    """Common error response for all Azure Resource Manager APIs to return error details for failed operations. (This also follows the OData error response format.).
+
+    :ivar error: The error object.
+    :vartype error: ~azure.mgmt.machinelearningservices.models.ErrorDetail
+    """
+
+    _attribute_map = {
+        'error': {'key': 'error', 'type': 'ErrorDetail'},
+    }
+
+    def __init__(
+        self,
+        *,
+        error: Optional["ErrorDetail"] = None,
+        **kwargs
+    ):
+        """
+        :keyword error: The error object.
+        :paramtype error: ~azure.mgmt.machinelearningservices.models.ErrorDetail
+        """
+        super(ErrorResponse, self).__init__(**kwargs)
+        self.error = error
+
+
+class EstimatedVMPrice(msrest.serialization.Model):
+    """The estimated price info for using a VM of a particular OS type, tier, etc.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar retail_price: Required. The price charged for using the VM.
+    :vartype retail_price: float
+    :ivar os_type: Required. Operating system type used by the VM. Possible values include:
+     "Linux", "Windows".
+    :vartype os_type: str or ~azure.mgmt.machinelearningservices.models.VMPriceOSType
+    :ivar vm_tier: Required. The type of the VM. Possible values include: "Standard",
+     "LowPriority", "Spot".
+    :vartype vm_tier: str or ~azure.mgmt.machinelearningservices.models.VMTier
+    """
+
+    _validation = {
+        'retail_price': {'required': True},
+        'os_type': {'required': True},
+        'vm_tier': {'required': True},
+    }
+
+    _attribute_map = {
+        'retail_price': {'key': 'retailPrice', 'type': 'float'},
+        'os_type': {'key': 'osType', 'type': 'str'},
+        'vm_tier': {'key': 'vmTier', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        retail_price: float,
+        os_type: Union[str, "VMPriceOSType"],
+        vm_tier: Union[str, "VMTier"],
+        **kwargs
+    ):
+        """
+        :keyword retail_price: Required. The price charged for using the VM.
+        :paramtype retail_price: float
+        :keyword os_type: Required. Operating system type used by the VM. Possible values include:
+         "Linux", "Windows".
+        :paramtype os_type: str or ~azure.mgmt.machinelearningservices.models.VMPriceOSType
+        :keyword vm_tier: Required. The type of the VM. Possible values include: "Standard",
+         "LowPriority", "Spot".
+        :paramtype vm_tier: str or ~azure.mgmt.machinelearningservices.models.VMTier
+        """
+        super(EstimatedVMPrice, self).__init__(**kwargs)
+        self.retail_price = retail_price
+        self.os_type = os_type
+        self.vm_tier = vm_tier
+
+
+class EstimatedVMPrices(msrest.serialization.Model):
+    """The estimated price info for using a VM.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar billing_currency: Required. Three lettered code specifying the currency of the VM price.
+     Example: USD. Possible values include: "USD".
+    :vartype billing_currency: str or ~azure.mgmt.machinelearningservices.models.BillingCurrency
+    :ivar unit_of_measure: Required. The unit of time measurement for the specified VM price.
+     Example: OneHour. Possible values include: "OneHour".
+    :vartype unit_of_measure: str or ~azure.mgmt.machinelearningservices.models.UnitOfMeasure
+    :ivar values: Required. The list of estimated prices for using a VM of a particular OS type,
+     tier, etc.
+    :vartype values: list[~azure.mgmt.machinelearningservices.models.EstimatedVMPrice]
+    """
+
+    _validation = {
+        'billing_currency': {'required': True},
+        'unit_of_measure': {'required': True},
+        'values': {'required': True},
+    }
+
+    _attribute_map = {
+        'billing_currency': {'key': 'billingCurrency', 'type': 'str'},
+        'unit_of_measure': {'key': 'unitOfMeasure', 'type': 'str'},
+        'values': {'key': 'values', 'type': '[EstimatedVMPrice]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        billing_currency: Union[str, "BillingCurrency"],
+        unit_of_measure: Union[str, "UnitOfMeasure"],
+        values: List["EstimatedVMPrice"],
+        **kwargs
+    ):
+        """
+        :keyword billing_currency: Required. Three lettered code specifying the currency of the VM
+         price. Example: USD. Possible values include: "USD".
+        :paramtype billing_currency: str or ~azure.mgmt.machinelearningservices.models.BillingCurrency
+        :keyword unit_of_measure: Required. The unit of time measurement for the specified VM price.
+         Example: OneHour. Possible values include: "OneHour".
+        :paramtype unit_of_measure: str or ~azure.mgmt.machinelearningservices.models.UnitOfMeasure
+        :keyword values: Required. The list of estimated prices for using a VM of a particular OS type,
+         tier, etc.
+        :paramtype values: list[~azure.mgmt.machinelearningservices.models.EstimatedVMPrice]
+        """
+        super(EstimatedVMPrices, self).__init__(**kwargs)
+        self.billing_currency = billing_currency
+        self.unit_of_measure = unit_of_measure
+        self.values = values
+
+
+class ExternalFQDNResponse(msrest.serialization.Model):
+    """ExternalFQDNResponse.
+
+    :ivar value:
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.FQDNEndpoints]
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[FQDNEndpoints]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional[List["FQDNEndpoints"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword value:
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.FQDNEndpoints]
+        """
+        super(ExternalFQDNResponse, self).__init__(**kwargs)
+        self.value = value
+
+
+class FeaturizationSettings(msrest.serialization.Model):
+    """Featurization Configuration.
+
+    :ivar dataset_language: Dataset language, useful for the text data.
+    :vartype dataset_language: str
+    """
+
+    _attribute_map = {
+        'dataset_language': {'key': 'datasetLanguage', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        dataset_language: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword dataset_language: Dataset language, useful for the text data.
+        :paramtype dataset_language: str
+        """
+        super(FeaturizationSettings, self).__init__(**kwargs)
+        self.dataset_language = dataset_language
+
+
+class FlavorData(msrest.serialization.Model):
+    """FlavorData.
+
+    :ivar data: Model flavor-specific data.
+    :vartype data: dict[str, str]
+    """
+
+    _attribute_map = {
+        'data': {'key': 'data', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        data: Optional[Dict[str, str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword data: Model flavor-specific data.
+        :paramtype data: dict[str, str]
+        """
+        super(FlavorData, self).__init__(**kwargs)
+        self.data = data
+
+
+class Forecasting(AutoMLVertical, TableVertical):
+    """Forecasting task in AutoML Table vertical.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar cv_split_column_names: Columns to use for CVSplit data.
+    :vartype cv_split_column_names: list[str]
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+    :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
+     when validation dataset is not provided.
+    :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar test_data: Test data input.
+    :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
+     purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype test_data_size: float
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar weight_column_name: The name of the sample weight column. Automated ML supports a
+     weighted column as an input, causing rows in the data to be weighted up or down.
+    :vartype weight_column_name: str
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar forecasting_settings: Forecasting task specific inputs.
+    :vartype forecasting_settings: ~azure.mgmt.machinelearningservices.models.ForecastingSettings
+    :ivar primary_metric: Primary metric for forecasting task. Possible values include:
+     "SpearmanCorrelation", "NormalizedRootMeanSquaredError", "R2Score",
+     "NormalizedMeanAbsoluteError".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ForecastingPrimaryMetrics
+    :ivar training_settings: Inputs for training phase for an AutoML Job.
+    :vartype training_settings:
+     ~azure.mgmt.machinelearningservices.models.ForecastingTrainingSettings
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'TableVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'TableVerticalLimitSettings'},
+        'n_cross_validations': {'key': 'nCrossValidations', 'type': 'NCrossValidations'},
+        'test_data': {'key': 'testData', 'type': 'MLTableJobInput'},
+        'test_data_size': {'key': 'testDataSize', 'type': 'float'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'forecasting_settings': {'key': 'forecastingSettings', 'type': 'ForecastingSettings'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+        'training_settings': {'key': 'trainingSettings', 'type': 'ForecastingTrainingSettings'},
+    }
+
+    def __init__(
+        self,
+        *,
+        training_data: "MLTableJobInput",
+        cv_split_column_names: Optional[List[str]] = None,
+        featurization_settings: Optional["TableVerticalFeaturizationSettings"] = None,
+        limit_settings: Optional["TableVerticalLimitSettings"] = None,
+        n_cross_validations: Optional["NCrossValidations"] = None,
+        test_data: Optional["MLTableJobInput"] = None,
+        test_data_size: Optional[float] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        weight_column_name: Optional[str] = None,
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        forecasting_settings: Optional["ForecastingSettings"] = None,
+        primary_metric: Optional[Union[str, "ForecastingPrimaryMetrics"]] = None,
+        training_settings: Optional["ForecastingTrainingSettings"] = None,
+        **kwargs
+    ):
+        """
+        :keyword cv_split_column_names: Columns to use for CVSplit data.
+        :paramtype cv_split_column_names: list[str]
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+        :keyword n_cross_validations: Number of cross validation folds to be applied on training
+         dataset
+         when validation dataset is not provided.
+        :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword test_data: Test data input.
+        :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
+         purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype test_data_size: float
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword weight_column_name: The name of the sample weight column. Automated ML supports a
+         weighted column as an input, causing rows in the data to be weighted up or down.
+        :paramtype weight_column_name: str
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword forecasting_settings: Forecasting task specific inputs.
+        :paramtype forecasting_settings: ~azure.mgmt.machinelearningservices.models.ForecastingSettings
+        :keyword primary_metric: Primary metric for forecasting task. Possible values include:
+         "SpearmanCorrelation", "NormalizedRootMeanSquaredError", "R2Score",
+         "NormalizedMeanAbsoluteError".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ForecastingPrimaryMetrics
+        :keyword training_settings: Inputs for training phase for an AutoML Job.
+        :paramtype training_settings:
+         ~azure.mgmt.machinelearningservices.models.ForecastingTrainingSettings
+        """
+        super(Forecasting, self).__init__(log_verbosity=log_verbosity, target_column_name=target_column_name, training_data=training_data, cv_split_column_names=cv_split_column_names, featurization_settings=featurization_settings, limit_settings=limit_settings, n_cross_validations=n_cross_validations, test_data=test_data, test_data_size=test_data_size, validation_data=validation_data, validation_data_size=validation_data_size, weight_column_name=weight_column_name, **kwargs)
+        self.cv_split_column_names = cv_split_column_names
+        self.featurization_settings = featurization_settings
+        self.limit_settings = limit_settings
+        self.n_cross_validations = n_cross_validations
+        self.test_data = test_data
+        self.test_data_size = test_data_size
+        self.validation_data = validation_data
+        self.validation_data_size = validation_data_size
+        self.weight_column_name = weight_column_name
+        self.task_type = 'Forecasting'  # type: str
+        self.forecasting_settings = forecasting_settings
+        self.primary_metric = primary_metric
+        self.training_settings = training_settings
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+
+
+class ForecastingSettings(msrest.serialization.Model):
+    """Forecasting specific parameters.
+
+    :ivar country_or_region_for_holidays: Country or region for holidays for forecasting tasks.
+     These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
+    :vartype country_or_region_for_holidays: str
+    :ivar cv_step_size: Number of periods between the origin time of one CV fold and the next fold.
+     For
+     example, if ``CVStepSize`` = 3 for daily data, the origin time for each fold will be
+     three days apart.
+    :vartype cv_step_size: int
+    :ivar feature_lags: Flag for generating lags for the numeric features with 'auto' or null.
+     Possible values include: "None", "Auto".
+    :vartype feature_lags: str or ~azure.mgmt.machinelearningservices.models.FeatureLags
+    :ivar forecast_horizon: The desired maximum forecast horizon in units of time-series frequency.
+    :vartype forecast_horizon: ~azure.mgmt.machinelearningservices.models.ForecastHorizon
+    :ivar frequency: When forecasting, this parameter represents the period with which the forecast
+     is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency
+     by default.
+    :vartype frequency: str
+    :ivar seasonality: Set time series seasonality as an integer multiple of the series frequency.
+     If seasonality is set to 'auto', it will be inferred.
+    :vartype seasonality: ~azure.mgmt.machinelearningservices.models.Seasonality
+    :ivar short_series_handling_config: The parameter defining how if AutoML should handle short
+     time series. Possible values include: "None", "Auto", "Pad", "Drop".
+    :vartype short_series_handling_config: str or
+     ~azure.mgmt.machinelearningservices.models.ShortSeriesHandlingConfiguration
+    :ivar target_aggregate_function: The function to be used to aggregate the time series target
+     column to conform to a user specified frequency.
+     If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the
+     error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
+     Possible values include: "None", "Sum", "Max", "Min", "Mean".
+    :vartype target_aggregate_function: str or
+     ~azure.mgmt.machinelearningservices.models.TargetAggregationFunction
+    :ivar target_lags: The number of past periods to lag from the target column.
+    :vartype target_lags: ~azure.mgmt.machinelearningservices.models.TargetLags
+    :ivar target_rolling_window_size: The number of past periods used to create a rolling window
+     average of the target column.
+    :vartype target_rolling_window_size:
+     ~azure.mgmt.machinelearningservices.models.TargetRollingWindowSize
+    :ivar time_column_name: The name of the time column. This parameter is required when
+     forecasting to specify the datetime column in the input data used for building the time series
+     and inferring its frequency.
+    :vartype time_column_name: str
+    :ivar time_series_id_column_names: The names of columns used to group a timeseries. It can be
+     used to create multiple series.
+     If grain is not defined, the data set is assumed to be one time-series. This parameter is used
+     with task type forecasting.
+    :vartype time_series_id_column_names: list[str]
+    :ivar use_stl: Configure STL Decomposition of the time-series target column. Possible values
+     include: "None", "Season", "SeasonTrend".
+    :vartype use_stl: str or ~azure.mgmt.machinelearningservices.models.UseStl
+    """
+
+    _attribute_map = {
+        'country_or_region_for_holidays': {'key': 'countryOrRegionForHolidays', 'type': 'str'},
+        'cv_step_size': {'key': 'cvStepSize', 'type': 'int'},
+        'feature_lags': {'key': 'featureLags', 'type': 'str'},
+        'forecast_horizon': {'key': 'forecastHorizon', 'type': 'ForecastHorizon'},
+        'frequency': {'key': 'frequency', 'type': 'str'},
+        'seasonality': {'key': 'seasonality', 'type': 'Seasonality'},
+        'short_series_handling_config': {'key': 'shortSeriesHandlingConfig', 'type': 'str'},
+        'target_aggregate_function': {'key': 'targetAggregateFunction', 'type': 'str'},
+        'target_lags': {'key': 'targetLags', 'type': 'TargetLags'},
+        'target_rolling_window_size': {'key': 'targetRollingWindowSize', 'type': 'TargetRollingWindowSize'},
+        'time_column_name': {'key': 'timeColumnName', 'type': 'str'},
+        'time_series_id_column_names': {'key': 'timeSeriesIdColumnNames', 'type': '[str]'},
+        'use_stl': {'key': 'useStl', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        country_or_region_for_holidays: Optional[str] = None,
+        cv_step_size: Optional[int] = None,
+        feature_lags: Optional[Union[str, "FeatureLags"]] = None,
+        forecast_horizon: Optional["ForecastHorizon"] = None,
+        frequency: Optional[str] = None,
+        seasonality: Optional["Seasonality"] = None,
+        short_series_handling_config: Optional[Union[str, "ShortSeriesHandlingConfiguration"]] = None,
+        target_aggregate_function: Optional[Union[str, "TargetAggregationFunction"]] = None,
+        target_lags: Optional["TargetLags"] = None,
+        target_rolling_window_size: Optional["TargetRollingWindowSize"] = None,
+        time_column_name: Optional[str] = None,
+        time_series_id_column_names: Optional[List[str]] = None,
+        use_stl: Optional[Union[str, "UseStl"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword country_or_region_for_holidays: Country or region for holidays for forecasting tasks.
+         These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
+        :paramtype country_or_region_for_holidays: str
+        :keyword cv_step_size: Number of periods between the origin time of one CV fold and the next
+         fold. For
+         example, if ``CVStepSize`` = 3 for daily data, the origin time for each fold will be
+         three days apart.
+        :paramtype cv_step_size: int
+        :keyword feature_lags: Flag for generating lags for the numeric features with 'auto' or null.
+         Possible values include: "None", "Auto".
+        :paramtype feature_lags: str or ~azure.mgmt.machinelearningservices.models.FeatureLags
+        :keyword forecast_horizon: The desired maximum forecast horizon in units of time-series
+         frequency.
+        :paramtype forecast_horizon: ~azure.mgmt.machinelearningservices.models.ForecastHorizon
+        :keyword frequency: When forecasting, this parameter represents the period with which the
+         forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset
+         frequency by default.
+        :paramtype frequency: str
+        :keyword seasonality: Set time series seasonality as an integer multiple of the series
+         frequency.
+         If seasonality is set to 'auto', it will be inferred.
+        :paramtype seasonality: ~azure.mgmt.machinelearningservices.models.Seasonality
+        :keyword short_series_handling_config: The parameter defining how if AutoML should handle short
+         time series. Possible values include: "None", "Auto", "Pad", "Drop".
+        :paramtype short_series_handling_config: str or
+         ~azure.mgmt.machinelearningservices.models.ShortSeriesHandlingConfiguration
+        :keyword target_aggregate_function: The function to be used to aggregate the time series target
+         column to conform to a user specified frequency.
+         If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the
+         error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
+         Possible values include: "None", "Sum", "Max", "Min", "Mean".
+        :paramtype target_aggregate_function: str or
+         ~azure.mgmt.machinelearningservices.models.TargetAggregationFunction
+        :keyword target_lags: The number of past periods to lag from the target column.
+        :paramtype target_lags: ~azure.mgmt.machinelearningservices.models.TargetLags
+        :keyword target_rolling_window_size: The number of past periods used to create a rolling window
+         average of the target column.
+        :paramtype target_rolling_window_size:
+         ~azure.mgmt.machinelearningservices.models.TargetRollingWindowSize
+        :keyword time_column_name: The name of the time column. This parameter is required when
+         forecasting to specify the datetime column in the input data used for building the time series
+         and inferring its frequency.
+        :paramtype time_column_name: str
+        :keyword time_series_id_column_names: The names of columns used to group a timeseries. It can
+         be used to create multiple series.
+         If grain is not defined, the data set is assumed to be one time-series. This parameter is used
+         with task type forecasting.
+        :paramtype time_series_id_column_names: list[str]
+        :keyword use_stl: Configure STL Decomposition of the time-series target column. Possible values
+         include: "None", "Season", "SeasonTrend".
+        :paramtype use_stl: str or ~azure.mgmt.machinelearningservices.models.UseStl
+        """
+        super(ForecastingSettings, self).__init__(**kwargs)
+        self.country_or_region_for_holidays = country_or_region_for_holidays
+        self.cv_step_size = cv_step_size
+        self.feature_lags = feature_lags
+        self.forecast_horizon = forecast_horizon
+        self.frequency = frequency
+        self.seasonality = seasonality
+        self.short_series_handling_config = short_series_handling_config
+        self.target_aggregate_function = target_aggregate_function
+        self.target_lags = target_lags
+        self.target_rolling_window_size = target_rolling_window_size
+        self.time_column_name = time_column_name
+        self.time_series_id_column_names = time_series_id_column_names
+        self.use_stl = use_stl
+
+
+class ForecastingTrainingSettings(TrainingSettings):
+    """Forecasting Training related configuration.
+
+    :ivar enable_dnn_training: Enable recommendation of DNN models.
+    :vartype enable_dnn_training: bool
+    :ivar enable_model_explainability: Flag to turn on explainability on best model.
+    :vartype enable_model_explainability: bool
+    :ivar enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+    :vartype enable_onnx_compatible_models: bool
+    :ivar enable_stack_ensemble: Enable stack ensemble run.
+    :vartype enable_stack_ensemble: bool
+    :ivar enable_vote_ensemble: Enable voting ensemble run.
+    :vartype enable_vote_ensemble: bool
+    :ivar ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+     generation, multiple fitted models from the previous child runs are downloaded.
+     Configure this parameter with a higher value than 300 secs, if more time is needed.
+    :vartype ensemble_model_download_timeout: ~datetime.timedelta
+    :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+    :vartype stack_ensemble_settings:
+     ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    :ivar allowed_training_algorithms: Allowed models for forecasting task.
+    :vartype allowed_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.ForecastingModels]
+    :ivar blocked_training_algorithms: Blocked models for forecasting task.
+    :vartype blocked_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.ForecastingModels]
+    """
+
+    _attribute_map = {
+        'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
+        'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
+        'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
+        'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
+        'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
+        'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'duration'},
+        'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'StackEnsembleSettings'},
+        'allowed_training_algorithms': {'key': 'allowedTrainingAlgorithms', 'type': '[str]'},
+        'blocked_training_algorithms': {'key': 'blockedTrainingAlgorithms', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        enable_dnn_training: Optional[bool] = False,
+        enable_model_explainability: Optional[bool] = True,
+        enable_onnx_compatible_models: Optional[bool] = False,
+        enable_stack_ensemble: Optional[bool] = True,
+        enable_vote_ensemble: Optional[bool] = True,
+        ensemble_model_download_timeout: Optional[datetime.timedelta] = "PT5M",
+        stack_ensemble_settings: Optional["StackEnsembleSettings"] = None,
+        allowed_training_algorithms: Optional[List[Union[str, "ForecastingModels"]]] = None,
+        blocked_training_algorithms: Optional[List[Union[str, "ForecastingModels"]]] = None,
+        **kwargs
+    ):
+        """
+        :keyword enable_dnn_training: Enable recommendation of DNN models.
+        :paramtype enable_dnn_training: bool
+        :keyword enable_model_explainability: Flag to turn on explainability on best model.
+        :paramtype enable_model_explainability: bool
+        :keyword enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+        :paramtype enable_onnx_compatible_models: bool
+        :keyword enable_stack_ensemble: Enable stack ensemble run.
+        :paramtype enable_stack_ensemble: bool
+        :keyword enable_vote_ensemble: Enable voting ensemble run.
+        :paramtype enable_vote_ensemble: bool
+        :keyword ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+         generation, multiple fitted models from the previous child runs are downloaded.
+         Configure this parameter with a higher value than 300 secs, if more time is needed.
+        :paramtype ensemble_model_download_timeout: ~datetime.timedelta
+        :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+        :paramtype stack_ensemble_settings:
+         ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        :keyword allowed_training_algorithms: Allowed models for forecasting task.
+        :paramtype allowed_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.ForecastingModels]
+        :keyword blocked_training_algorithms: Blocked models for forecasting task.
+        :paramtype blocked_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.ForecastingModels]
+        """
+        super(ForecastingTrainingSettings, self).__init__(enable_dnn_training=enable_dnn_training, enable_model_explainability=enable_model_explainability, enable_onnx_compatible_models=enable_onnx_compatible_models, enable_stack_ensemble=enable_stack_ensemble, enable_vote_ensemble=enable_vote_ensemble, ensemble_model_download_timeout=ensemble_model_download_timeout, stack_ensemble_settings=stack_ensemble_settings, **kwargs)
+        self.allowed_training_algorithms = allowed_training_algorithms
+        self.blocked_training_algorithms = blocked_training_algorithms
+
+
+class FQDNEndpoint(msrest.serialization.Model):
+    """FQDNEndpoint.
+
+    :ivar domain_name:
+    :vartype domain_name: str
+    :ivar endpoint_details:
+    :vartype endpoint_details: list[~azure.mgmt.machinelearningservices.models.FQDNEndpointDetail]
+    """
+
+    _attribute_map = {
+        'domain_name': {'key': 'domainName', 'type': 'str'},
+        'endpoint_details': {'key': 'endpointDetails', 'type': '[FQDNEndpointDetail]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        domain_name: Optional[str] = None,
+        endpoint_details: Optional[List["FQDNEndpointDetail"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword domain_name:
+        :paramtype domain_name: str
+        :keyword endpoint_details:
+        :paramtype endpoint_details:
+         list[~azure.mgmt.machinelearningservices.models.FQDNEndpointDetail]
+        """
+        super(FQDNEndpoint, self).__init__(**kwargs)
+        self.domain_name = domain_name
+        self.endpoint_details = endpoint_details
+
+
+class FQDNEndpointDetail(msrest.serialization.Model):
+    """FQDNEndpointDetail.
+
+    :ivar port:
+    :vartype port: int
+    """
+
+    _attribute_map = {
+        'port': {'key': 'port', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        port: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword port:
+        :paramtype port: int
+        """
+        super(FQDNEndpointDetail, self).__init__(**kwargs)
+        self.port = port
+
+
+class FQDNEndpoints(msrest.serialization.Model):
+    """FQDNEndpoints.
+
+    :ivar properties:
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.FQDNEndpointsProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'FQDNEndpointsProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["FQDNEndpointsProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties:
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.FQDNEndpointsProperties
+        """
+        super(FQDNEndpoints, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class FQDNEndpointsProperties(msrest.serialization.Model):
+    """FQDNEndpointsProperties.
+
+    :ivar category:
+    :vartype category: str
+    :ivar endpoints:
+    :vartype endpoints: list[~azure.mgmt.machinelearningservices.models.FQDNEndpoint]
+    """
+
+    _attribute_map = {
+        'category': {'key': 'category', 'type': 'str'},
+        'endpoints': {'key': 'endpoints', 'type': '[FQDNEndpoint]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        category: Optional[str] = None,
+        endpoints: Optional[List["FQDNEndpoint"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword category:
+        :paramtype category: str
+        :keyword endpoints:
+        :paramtype endpoints: list[~azure.mgmt.machinelearningservices.models.FQDNEndpoint]
+        """
+        super(FQDNEndpointsProperties, self).__init__(**kwargs)
+        self.category = category
+        self.endpoints = endpoints
+
+
+class GridSamplingAlgorithm(SamplingAlgorithm):
+    """Defines a Sampling Algorithm that exhaustively generates every value combination in the space.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar sampling_algorithm_type: Required. [Required] The algorithm used for generating
+     hyperparameter values, along with configuration properties.Constant filled by server. Possible
+     values include: "Grid", "Random", "Bayesian".
+    :vartype sampling_algorithm_type: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    """
+
+    _validation = {
+        'sampling_algorithm_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'sampling_algorithm_type': {'key': 'samplingAlgorithmType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(GridSamplingAlgorithm, self).__init__(**kwargs)
+        self.sampling_algorithm_type = 'Grid'  # type: str
+
+
+class HDInsightSchema(msrest.serialization.Model):
+    """HDInsightSchema.
+
+    :ivar properties: HDInsight compute properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'HDInsightProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["HDInsightProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: HDInsight compute properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+        """
+        super(HDInsightSchema, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class HDInsight(Compute, HDInsightSchema):
+    """A HDInsight compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: HDInsight compute properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'HDInsightProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["HDInsightProperties"] = None,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: HDInsight compute properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(HDInsight, self).__init__(description=description, resource_id=resource_id, properties=properties, **kwargs)
+        self.properties = properties
+        self.compute_type = 'HDInsight'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = description
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = resource_id
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class HDInsightProperties(msrest.serialization.Model):
+    """HDInsight compute properties.
+
+    :ivar ssh_port: Port open for ssh connections on the master node of the cluster.
+    :vartype ssh_port: int
+    :ivar address: Public IP address of the master node of the cluster.
+    :vartype address: str
+    :ivar administrator_account: Admin credentials for master node of the cluster.
+    :vartype administrator_account:
+     ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+    """
+
+    _attribute_map = {
+        'ssh_port': {'key': 'sshPort', 'type': 'int'},
+        'address': {'key': 'address', 'type': 'str'},
+        'administrator_account': {'key': 'administratorAccount', 'type': 'VirtualMachineSshCredentials'},
+    }
+
+    def __init__(
+        self,
+        *,
+        ssh_port: Optional[int] = None,
+        address: Optional[str] = None,
+        administrator_account: Optional["VirtualMachineSshCredentials"] = None,
+        **kwargs
+    ):
+        """
+        :keyword ssh_port: Port open for ssh connections on the master node of the cluster.
+        :paramtype ssh_port: int
+        :keyword address: Public IP address of the master node of the cluster.
+        :paramtype address: str
+        :keyword administrator_account: Admin credentials for master node of the cluster.
+        :paramtype administrator_account:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+        """
+        super(HDInsightProperties, self).__init__(**kwargs)
+        self.ssh_port = ssh_port
+        self.address = address
+        self.administrator_account = administrator_account
+
+
+class IdAssetReference(AssetReferenceBase):
+    """Reference to an asset via its ARM resource ID.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar reference_type: Required. [Required] Specifies the type of asset reference.Constant
+     filled by server. Possible values include: "Id", "DataPath", "OutputPath".
+    :vartype reference_type: str or ~azure.mgmt.machinelearningservices.models.ReferenceType
+    :ivar asset_id: Required. [Required] ARM resource ID of the asset.
+    :vartype asset_id: str
+    """
+
+    _validation = {
+        'reference_type': {'required': True},
+        'asset_id': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'reference_type': {'key': 'referenceType', 'type': 'str'},
+        'asset_id': {'key': 'assetId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        asset_id: str,
+        **kwargs
+    ):
+        """
+        :keyword asset_id: Required. [Required] ARM resource ID of the asset.
+        :paramtype asset_id: str
+        """
+        super(IdAssetReference, self).__init__(**kwargs)
+        self.reference_type = 'Id'  # type: str
+        self.asset_id = asset_id
+
+
+class IdentityForCmk(msrest.serialization.Model):
+    """Identity that will be used to access key vault for encryption at rest.
+
+    :ivar user_assigned_identity: The ArmId of the user assigned identity that will be used to
+     access the customer managed key vault.
+    :vartype user_assigned_identity: str
+    """
+
+    _attribute_map = {
+        'user_assigned_identity': {'key': 'userAssignedIdentity', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        user_assigned_identity: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword user_assigned_identity: The ArmId of the user assigned identity that will be used to
+         access the customer managed key vault.
+        :paramtype user_assigned_identity: str
+        """
+        super(IdentityForCmk, self).__init__(**kwargs)
+        self.user_assigned_identity = user_assigned_identity
+
+
+class ImageVertical(msrest.serialization.Model):
+    """Abstract class for AutoML tasks that train image (computer vision) models -
+such as Image Classification / Image Classification Multilabel / Image Object Detection / Image Instance Segmentation.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+    }
+
+    def __init__(
+        self,
+        *,
+        limit_settings: "ImageLimitSettings",
+        sweep_settings: Optional["ImageSweepSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        """
+        super(ImageVertical, self).__init__(**kwargs)
+        self.limit_settings = limit_settings
+        self.sweep_settings = sweep_settings
+        self.validation_data = validation_data
+        self.validation_data_size = validation_data_size
+
+
+class ImageClassificationBase(ImageVertical):
+    """ImageClassificationBase.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsClassification'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsClassification]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        limit_settings: "ImageLimitSettings",
+        sweep_settings: Optional["ImageSweepSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        model_settings: Optional["ImageModelSettingsClassification"] = None,
+        search_space: Optional[List["ImageModelDistributionSettingsClassification"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+        """
+        super(ImageClassificationBase, self).__init__(limit_settings=limit_settings, sweep_settings=sweep_settings, validation_data=validation_data, validation_data_size=validation_data_size, **kwargs)
+        self.model_settings = model_settings
+        self.search_space = search_space
+
+
+class ImageClassification(AutoMLVertical, ImageClassificationBase):
+    """Image Classification. Multi-class image classification is used when an image is classified with only a single label
+from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog' or a 'duck'.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric to optimize for this task. Possible values include:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsClassification'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsClassification]'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        limit_settings: "ImageLimitSettings",
+        training_data: "MLTableJobInput",
+        sweep_settings: Optional["ImageSweepSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        model_settings: Optional["ImageModelSettingsClassification"] = None,
+        search_space: Optional[List["ImageModelDistributionSettingsClassification"]] = None,
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        primary_metric: Optional[Union[str, "ClassificationPrimaryMetrics"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric to optimize for this task. Possible values include:
+         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+         "PrecisionScoreWeighted".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+        """
+        super(ImageClassification, self).__init__(log_verbosity=log_verbosity, target_column_name=target_column_name, training_data=training_data, limit_settings=limit_settings, sweep_settings=sweep_settings, validation_data=validation_data, validation_data_size=validation_data_size, model_settings=model_settings, search_space=search_space, **kwargs)
+        self.limit_settings = limit_settings
+        self.sweep_settings = sweep_settings
+        self.validation_data = validation_data
+        self.validation_data_size = validation_data_size
+        self.model_settings = model_settings
+        self.search_space = search_space
+        self.task_type = 'ImageClassification'  # type: str
+        self.primary_metric = primary_metric
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+
+
+class ImageClassificationMultilabel(AutoMLVertical, ImageClassificationBase):
+    """Image Classification Multilabel. Multi-label image classification is used when an image could have one or more labels
+from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric to optimize for this task. Possible values include:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+     "PrecisionScoreWeighted", "IOU".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsClassification'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsClassification]'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        limit_settings: "ImageLimitSettings",
+        training_data: "MLTableJobInput",
+        sweep_settings: Optional["ImageSweepSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        model_settings: Optional["ImageModelSettingsClassification"] = None,
+        search_space: Optional[List["ImageModelDistributionSettingsClassification"]] = None,
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        primary_metric: Optional[Union[str, "ClassificationMultilabelPrimaryMetrics"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric to optimize for this task. Possible values include:
+         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+         "PrecisionScoreWeighted", "IOU".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
+        """
+        super(ImageClassificationMultilabel, self).__init__(log_verbosity=log_verbosity, target_column_name=target_column_name, training_data=training_data, limit_settings=limit_settings, sweep_settings=sweep_settings, validation_data=validation_data, validation_data_size=validation_data_size, model_settings=model_settings, search_space=search_space, **kwargs)
+        self.limit_settings = limit_settings
+        self.sweep_settings = sweep_settings
+        self.validation_data = validation_data
+        self.validation_data_size = validation_data_size
+        self.model_settings = model_settings
+        self.search_space = search_space
+        self.task_type = 'ImageClassificationMultilabel'  # type: str
+        self.primary_metric = primary_metric
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+
+
+class ImageObjectDetectionBase(ImageVertical):
+    """ImageObjectDetectionBase.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsObjectDetection'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsObjectDetection]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        limit_settings: "ImageLimitSettings",
+        sweep_settings: Optional["ImageSweepSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        model_settings: Optional["ImageModelSettingsObjectDetection"] = None,
+        search_space: Optional[List["ImageModelDistributionSettingsObjectDetection"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+        """
+        super(ImageObjectDetectionBase, self).__init__(limit_settings=limit_settings, sweep_settings=sweep_settings, validation_data=validation_data, validation_data_size=validation_data_size, **kwargs)
+        self.model_settings = model_settings
+        self.search_space = search_space
+
+
+class ImageInstanceSegmentation(AutoMLVertical, ImageObjectDetectionBase):
+    """Image Instance Segmentation. Instance segmentation is used to identify objects in an image at the pixel level,
+drawing a polygon around each object in the image.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric to optimize for this task. Possible values include:
+     "MeanAveragePrecision".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.InstanceSegmentationPrimaryMetrics
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsObjectDetection'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsObjectDetection]'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        limit_settings: "ImageLimitSettings",
+        training_data: "MLTableJobInput",
+        sweep_settings: Optional["ImageSweepSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        model_settings: Optional["ImageModelSettingsObjectDetection"] = None,
+        search_space: Optional[List["ImageModelDistributionSettingsObjectDetection"]] = None,
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        primary_metric: Optional[Union[str, "InstanceSegmentationPrimaryMetrics"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric to optimize for this task. Possible values include:
+         "MeanAveragePrecision".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.InstanceSegmentationPrimaryMetrics
+        """
+        super(ImageInstanceSegmentation, self).__init__(log_verbosity=log_verbosity, target_column_name=target_column_name, training_data=training_data, limit_settings=limit_settings, sweep_settings=sweep_settings, validation_data=validation_data, validation_data_size=validation_data_size, model_settings=model_settings, search_space=search_space, **kwargs)
+        self.limit_settings = limit_settings
+        self.sweep_settings = sweep_settings
+        self.validation_data = validation_data
+        self.validation_data_size = validation_data_size
+        self.model_settings = model_settings
+        self.search_space = search_space
+        self.task_type = 'ImageInstanceSegmentation'  # type: str
+        self.primary_metric = primary_metric
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+
+
+class ImageLimitSettings(msrest.serialization.Model):
+    """Limit settings for the AutoML job.
+
+    :ivar max_concurrent_trials: Maximum number of concurrent AutoML iterations.
+    :vartype max_concurrent_trials: int
+    :ivar max_trials: Maximum number of AutoML iterations.
+    :vartype max_trials: int
+    :ivar timeout: AutoML job timeout.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
+        'max_trials': {'key': 'maxTrials', 'type': 'int'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        max_concurrent_trials: Optional[int] = 1,
+        max_trials: Optional[int] = 1,
+        timeout: Optional[datetime.timedelta] = "P7D",
+        **kwargs
+    ):
+        """
+        :keyword max_concurrent_trials: Maximum number of concurrent AutoML iterations.
+        :paramtype max_concurrent_trials: int
+        :keyword max_trials: Maximum number of AutoML iterations.
+        :paramtype max_trials: int
+        :keyword timeout: AutoML job timeout.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(ImageLimitSettings, self).__init__(**kwargs)
+        self.max_concurrent_trials = max_concurrent_trials
+        self.max_trials = max_trials
+        self.timeout = timeout
+
+
+class ImageModelDistributionSettings(msrest.serialization.Model):
+    """Distribution expressions to sweep over values of model settings.
+
+:code:`<example>
+Some examples are:
+<code>
+ModelName = "choice('seresnext', 'resnest50')";
+LearningRate = "uniform(0.001, 0.01)";
+LayersToFreeze = "choice(0, 2)";
+</code></example>`
+All distributions can be specified as distribution_name(min, max) or choice(val1, val2, ..., valn)
+where distribution name can be: uniform, quniform, loguniform, etc
+For more details on how to compose distribution expressions please check the documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-tune-hyperparameters
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
+    """
+
+    _attribute_map = {
+        'ams_gradient': {'key': 'amsGradient', 'type': 'str'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'str'},
+        'beta2': {'key': 'beta2', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'str'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'str'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'str'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'str'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'str'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'str'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'str'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'str'},
+        'learning_rate': {'key': 'learningRate', 'type': 'str'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'str'},
+        'nesterov': {'key': 'nesterov', 'type': 'str'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'str'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'str'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'str'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'str'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'str'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'str'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'str'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'str'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'str'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        ams_gradient: Optional[str] = None,
+        augmentations: Optional[str] = None,
+        beta1: Optional[str] = None,
+        beta2: Optional[str] = None,
+        distributed: Optional[str] = None,
+        early_stopping: Optional[str] = None,
+        early_stopping_delay: Optional[str] = None,
+        early_stopping_patience: Optional[str] = None,
+        enable_onnx_normalization: Optional[str] = None,
+        evaluation_frequency: Optional[str] = None,
+        gradient_accumulation_step: Optional[str] = None,
+        layers_to_freeze: Optional[str] = None,
+        learning_rate: Optional[str] = None,
+        learning_rate_scheduler: Optional[str] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[str] = None,
+        nesterov: Optional[str] = None,
+        number_of_epochs: Optional[str] = None,
+        number_of_workers: Optional[str] = None,
+        optimizer: Optional[str] = None,
+        random_seed: Optional[str] = None,
+        step_lr_gamma: Optional[str] = None,
+        step_lr_step_size: Optional[str] = None,
+        training_batch_size: Optional[str] = None,
+        validation_batch_size: Optional[str] = None,
+        warmup_cosine_lr_cycles: Optional[str] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[str] = None,
+        weight_decay: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: str
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: str
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: str
+        :keyword distributed: Whether to use distributer training.
+        :paramtype distributed: str
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: str
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: str
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: str
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: str
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: str
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: str
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: str
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: str
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'.
+        :paramtype learning_rate_scheduler: str
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: str
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: str
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: str
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: str
+        :keyword optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+        :paramtype optimizer: str
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: str
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: str
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: str
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: str
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: str
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: str
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: str
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: str
+        """
+        super(ImageModelDistributionSettings, self).__init__(**kwargs)
+        self.ams_gradient = ams_gradient
+        self.augmentations = augmentations
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.distributed = distributed
+        self.early_stopping = early_stopping
+        self.early_stopping_delay = early_stopping_delay
+        self.early_stopping_patience = early_stopping_patience
+        self.enable_onnx_normalization = enable_onnx_normalization
+        self.evaluation_frequency = evaluation_frequency
+        self.gradient_accumulation_step = gradient_accumulation_step
+        self.layers_to_freeze = layers_to_freeze
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.momentum = momentum
+        self.nesterov = nesterov
+        self.number_of_epochs = number_of_epochs
+        self.number_of_workers = number_of_workers
+        self.optimizer = optimizer
+        self.random_seed = random_seed
+        self.step_lr_gamma = step_lr_gamma
+        self.step_lr_step_size = step_lr_step_size
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_cosine_lr_cycles = warmup_cosine_lr_cycles
+        self.warmup_cosine_lr_warmup_epochs = warmup_cosine_lr_warmup_epochs
+        self.weight_decay = weight_decay
+
+
+class ImageModelDistributionSettingsClassification(ImageModelDistributionSettings):
+    """Distribution expressions to sweep over values of model settings.
+
+:code:`<example>
+Some examples are:
+<code>
+ModelName = "choice('seresnext', 'resnest50')";
+LearningRate = "uniform(0.001, 0.01)";
+LayersToFreeze = "choice(0, 2)";
+</code></example>`
+For more details on how to compose distribution expressions please check the documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-tune-hyperparameters
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
+    :ivar training_crop_size: Image crop size that is input to the neural network for the training
+     dataset. Must be a positive integer.
+    :vartype training_crop_size: str
+    :ivar validation_crop_size: Image crop size that is input to the neural network for the
+     validation dataset. Must be a positive integer.
+    :vartype validation_crop_size: str
+    :ivar validation_resize_size: Image size to which to resize before cropping for validation
+     dataset. Must be a positive integer.
+    :vartype validation_resize_size: str
+    :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+     1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+     0 or 1 or 2.
+    :vartype weighted_loss: str
+    """
+
+    _attribute_map = {
+        'ams_gradient': {'key': 'amsGradient', 'type': 'str'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'str'},
+        'beta2': {'key': 'beta2', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'str'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'str'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'str'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'str'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'str'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'str'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'str'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'str'},
+        'learning_rate': {'key': 'learningRate', 'type': 'str'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'str'},
+        'nesterov': {'key': 'nesterov', 'type': 'str'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'str'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'str'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'str'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'str'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'str'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'str'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'str'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'str'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'str'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'str'},
+        'training_crop_size': {'key': 'trainingCropSize', 'type': 'str'},
+        'validation_crop_size': {'key': 'validationCropSize', 'type': 'str'},
+        'validation_resize_size': {'key': 'validationResizeSize', 'type': 'str'},
+        'weighted_loss': {'key': 'weightedLoss', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        ams_gradient: Optional[str] = None,
+        augmentations: Optional[str] = None,
+        beta1: Optional[str] = None,
+        beta2: Optional[str] = None,
+        distributed: Optional[str] = None,
+        early_stopping: Optional[str] = None,
+        early_stopping_delay: Optional[str] = None,
+        early_stopping_patience: Optional[str] = None,
+        enable_onnx_normalization: Optional[str] = None,
+        evaluation_frequency: Optional[str] = None,
+        gradient_accumulation_step: Optional[str] = None,
+        layers_to_freeze: Optional[str] = None,
+        learning_rate: Optional[str] = None,
+        learning_rate_scheduler: Optional[str] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[str] = None,
+        nesterov: Optional[str] = None,
+        number_of_epochs: Optional[str] = None,
+        number_of_workers: Optional[str] = None,
+        optimizer: Optional[str] = None,
+        random_seed: Optional[str] = None,
+        step_lr_gamma: Optional[str] = None,
+        step_lr_step_size: Optional[str] = None,
+        training_batch_size: Optional[str] = None,
+        validation_batch_size: Optional[str] = None,
+        warmup_cosine_lr_cycles: Optional[str] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[str] = None,
+        weight_decay: Optional[str] = None,
+        training_crop_size: Optional[str] = None,
+        validation_crop_size: Optional[str] = None,
+        validation_resize_size: Optional[str] = None,
+        weighted_loss: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: str
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: str
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: str
+        :keyword distributed: Whether to use distributer training.
+        :paramtype distributed: str
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: str
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: str
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: str
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: str
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: str
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: str
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: str
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: str
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'.
+        :paramtype learning_rate_scheduler: str
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: str
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: str
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: str
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: str
+        :keyword optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+        :paramtype optimizer: str
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: str
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: str
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: str
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: str
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: str
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: str
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: str
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: str
+        :keyword training_crop_size: Image crop size that is input to the neural network for the
+         training dataset. Must be a positive integer.
+        :paramtype training_crop_size: str
+        :keyword validation_crop_size: Image crop size that is input to the neural network for the
+         validation dataset. Must be a positive integer.
+        :paramtype validation_crop_size: str
+        :keyword validation_resize_size: Image size to which to resize before cropping for validation
+         dataset. Must be a positive integer.
+        :paramtype validation_resize_size: str
+        :keyword weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+         1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+         0 or 1 or 2.
+        :paramtype weighted_loss: str
+        """
+        super(ImageModelDistributionSettingsClassification, self).__init__(ams_gradient=ams_gradient, augmentations=augmentations, beta1=beta1, beta2=beta2, distributed=distributed, early_stopping=early_stopping, early_stopping_delay=early_stopping_delay, early_stopping_patience=early_stopping_patience, enable_onnx_normalization=enable_onnx_normalization, evaluation_frequency=evaluation_frequency, gradient_accumulation_step=gradient_accumulation_step, layers_to_freeze=layers_to_freeze, learning_rate=learning_rate, learning_rate_scheduler=learning_rate_scheduler, model_name=model_name, momentum=momentum, nesterov=nesterov, number_of_epochs=number_of_epochs, number_of_workers=number_of_workers, optimizer=optimizer, random_seed=random_seed, step_lr_gamma=step_lr_gamma, step_lr_step_size=step_lr_step_size, training_batch_size=training_batch_size, validation_batch_size=validation_batch_size, warmup_cosine_lr_cycles=warmup_cosine_lr_cycles, warmup_cosine_lr_warmup_epochs=warmup_cosine_lr_warmup_epochs, weight_decay=weight_decay, **kwargs)
+        self.training_crop_size = training_crop_size
+        self.validation_crop_size = validation_crop_size
+        self.validation_resize_size = validation_resize_size
+        self.weighted_loss = weighted_loss
+
+
+class ImageModelDistributionSettingsObjectDetection(ImageModelDistributionSettings):
+    """Distribution expressions to sweep over values of model settings.
+
+:code:`<example>
+Some examples are:
+<code>
+ModelName = "choice('seresnext', 'resnest50')";
+LearningRate = "uniform(0.001, 0.01)";
+LayersToFreeze = "choice(0, 2)";
+</code></example>`
+For more details on how to compose distribution expressions please check the documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-tune-hyperparameters
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
+    :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
+     be a positive integer.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype box_detections_per_image: str
+    :ivar box_score_threshold: During inference, only return proposals with a classification score
+     greater than
+     BoxScoreThreshold. Must be a float in the range[0, 1].
+    :vartype box_score_threshold: str
+    :ivar image_size: Image size for train and validation. Must be a positive integer.
+     Note: The training run may get into CUDA OOM if the size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype image_size: str
+    :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype max_size: str
+    :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype min_size: str
+    :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+     Note: training run may get into CUDA OOM if the model size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype model_size: str
+    :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+     Note: training run may get into CUDA OOM if no sufficient GPU memory.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype multi_scale: str
+    :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
+     float in the range [0, 1].
+    :vartype nms_iou_threshold: str
+    :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
+     be
+     None to enable small object detection logic. A string containing two integers in mxn format.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_grid_size: str
+    :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
+     in the range [0, 1).
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_overlap_ratio: str
+    :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+     predictions from tiles and image.
+     Used in validation/ inference. Must be float in the range [0, 1].
+     Note: This settings is not supported for the 'yolov5' algorithm.
+     NMS: Non-maximum suppression.
+    :vartype tile_predictions_nms_threshold: str
+    :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
+     float in the range [0, 1].
+    :vartype validation_iou_threshold: str
+    :ivar validation_metric_type: Metric computation method to use for validation metrics. Must be
+     'none', 'coco', 'voc', or 'coco_voc'.
+    :vartype validation_metric_type: str
+    """
+
+    _attribute_map = {
+        'ams_gradient': {'key': 'amsGradient', 'type': 'str'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'str'},
+        'beta2': {'key': 'beta2', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'str'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'str'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'str'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'str'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'str'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'str'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'str'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'str'},
+        'learning_rate': {'key': 'learningRate', 'type': 'str'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'str'},
+        'nesterov': {'key': 'nesterov', 'type': 'str'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'str'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'str'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'str'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'str'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'str'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'str'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'str'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'str'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'str'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'str'},
+        'box_detections_per_image': {'key': 'boxDetectionsPerImage', 'type': 'str'},
+        'box_score_threshold': {'key': 'boxScoreThreshold', 'type': 'str'},
+        'image_size': {'key': 'imageSize', 'type': 'str'},
+        'max_size': {'key': 'maxSize', 'type': 'str'},
+        'min_size': {'key': 'minSize', 'type': 'str'},
+        'model_size': {'key': 'modelSize', 'type': 'str'},
+        'multi_scale': {'key': 'multiScale', 'type': 'str'},
+        'nms_iou_threshold': {'key': 'nmsIouThreshold', 'type': 'str'},
+        'tile_grid_size': {'key': 'tileGridSize', 'type': 'str'},
+        'tile_overlap_ratio': {'key': 'tileOverlapRatio', 'type': 'str'},
+        'tile_predictions_nms_threshold': {'key': 'tilePredictionsNmsThreshold', 'type': 'str'},
+        'validation_iou_threshold': {'key': 'validationIouThreshold', 'type': 'str'},
+        'validation_metric_type': {'key': 'validationMetricType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        ams_gradient: Optional[str] = None,
+        augmentations: Optional[str] = None,
+        beta1: Optional[str] = None,
+        beta2: Optional[str] = None,
+        distributed: Optional[str] = None,
+        early_stopping: Optional[str] = None,
+        early_stopping_delay: Optional[str] = None,
+        early_stopping_patience: Optional[str] = None,
+        enable_onnx_normalization: Optional[str] = None,
+        evaluation_frequency: Optional[str] = None,
+        gradient_accumulation_step: Optional[str] = None,
+        layers_to_freeze: Optional[str] = None,
+        learning_rate: Optional[str] = None,
+        learning_rate_scheduler: Optional[str] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[str] = None,
+        nesterov: Optional[str] = None,
+        number_of_epochs: Optional[str] = None,
+        number_of_workers: Optional[str] = None,
+        optimizer: Optional[str] = None,
+        random_seed: Optional[str] = None,
+        step_lr_gamma: Optional[str] = None,
+        step_lr_step_size: Optional[str] = None,
+        training_batch_size: Optional[str] = None,
+        validation_batch_size: Optional[str] = None,
+        warmup_cosine_lr_cycles: Optional[str] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[str] = None,
+        weight_decay: Optional[str] = None,
+        box_detections_per_image: Optional[str] = None,
+        box_score_threshold: Optional[str] = None,
+        image_size: Optional[str] = None,
+        max_size: Optional[str] = None,
+        min_size: Optional[str] = None,
+        model_size: Optional[str] = None,
+        multi_scale: Optional[str] = None,
+        nms_iou_threshold: Optional[str] = None,
+        tile_grid_size: Optional[str] = None,
+        tile_overlap_ratio: Optional[str] = None,
+        tile_predictions_nms_threshold: Optional[str] = None,
+        validation_iou_threshold: Optional[str] = None,
+        validation_metric_type: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: str
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: str
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: str
+        :keyword distributed: Whether to use distributer training.
+        :paramtype distributed: str
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: str
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: str
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: str
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: str
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: str
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: str
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: str
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: str
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'.
+        :paramtype learning_rate_scheduler: str
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: str
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: str
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: str
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: str
+        :keyword optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+        :paramtype optimizer: str
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: str
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: str
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: str
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: str
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: str
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: str
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: str
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: str
+        :keyword box_detections_per_image: Maximum number of detections per image, for all classes.
+         Must be a positive integer.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype box_detections_per_image: str
+        :keyword box_score_threshold: During inference, only return proposals with a classification
+         score greater than
+         BoxScoreThreshold. Must be a float in the range[0, 1].
+        :paramtype box_score_threshold: str
+        :keyword image_size: Image size for train and validation. Must be a positive integer.
+         Note: The training run may get into CUDA OOM if the size is too big.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :paramtype image_size: str
+        :keyword max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype max_size: str
+        :keyword min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype min_size: str
+        :keyword model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+         Note: training run may get into CUDA OOM if the model size is too big.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :paramtype model_size: str
+        :keyword multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+         Note: training run may get into CUDA OOM if no sufficient GPU memory.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :paramtype multi_scale: str
+        :keyword nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
+         float in the range [0, 1].
+        :paramtype nms_iou_threshold: str
+        :keyword tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must
+         not be
+         None to enable small object detection logic. A string containing two integers in mxn format.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype tile_grid_size: str
+        :keyword tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be
+         float in the range [0, 1).
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype tile_overlap_ratio: str
+        :keyword tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+         predictions from tiles and image.
+         Used in validation/ inference. Must be float in the range [0, 1].
+         Note: This settings is not supported for the 'yolov5' algorithm.
+         NMS: Non-maximum suppression.
+        :paramtype tile_predictions_nms_threshold: str
+        :keyword validation_iou_threshold: IOU threshold to use when computing validation metric. Must
+         be float in the range [0, 1].
+        :paramtype validation_iou_threshold: str
+        :keyword validation_metric_type: Metric computation method to use for validation metrics. Must
+         be 'none', 'coco', 'voc', or 'coco_voc'.
+        :paramtype validation_metric_type: str
+        """
+        super(ImageModelDistributionSettingsObjectDetection, self).__init__(ams_gradient=ams_gradient, augmentations=augmentations, beta1=beta1, beta2=beta2, distributed=distributed, early_stopping=early_stopping, early_stopping_delay=early_stopping_delay, early_stopping_patience=early_stopping_patience, enable_onnx_normalization=enable_onnx_normalization, evaluation_frequency=evaluation_frequency, gradient_accumulation_step=gradient_accumulation_step, layers_to_freeze=layers_to_freeze, learning_rate=learning_rate, learning_rate_scheduler=learning_rate_scheduler, model_name=model_name, momentum=momentum, nesterov=nesterov, number_of_epochs=number_of_epochs, number_of_workers=number_of_workers, optimizer=optimizer, random_seed=random_seed, step_lr_gamma=step_lr_gamma, step_lr_step_size=step_lr_step_size, training_batch_size=training_batch_size, validation_batch_size=validation_batch_size, warmup_cosine_lr_cycles=warmup_cosine_lr_cycles, warmup_cosine_lr_warmup_epochs=warmup_cosine_lr_warmup_epochs, weight_decay=weight_decay, **kwargs)
+        self.box_detections_per_image = box_detections_per_image
+        self.box_score_threshold = box_score_threshold
+        self.image_size = image_size
+        self.max_size = max_size
+        self.min_size = min_size
+        self.model_size = model_size
+        self.multi_scale = multi_scale
+        self.nms_iou_threshold = nms_iou_threshold
+        self.tile_grid_size = tile_grid_size
+        self.tile_overlap_ratio = tile_overlap_ratio
+        self.tile_predictions_nms_threshold = tile_predictions_nms_threshold
+        self.validation_iou_threshold = validation_iou_threshold
+        self.validation_metric_type = validation_metric_type
+
+
+class ImageModelSettings(msrest.serialization.Model):
+    """Settings used for training the model.
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Possible values include: "None", "WarmupCosine", "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
+    """
+
+    _attribute_map = {
+        'advanced_settings': {'key': 'advancedSettings', 'type': 'str'},
+        'ams_gradient': {'key': 'amsGradient', 'type': 'bool'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'float'},
+        'beta2': {'key': 'beta2', 'type': 'float'},
+        'checkpoint_frequency': {'key': 'checkpointFrequency', 'type': 'int'},
+        'checkpoint_model': {'key': 'checkpointModel', 'type': 'MLFlowModelJobInput'},
+        'checkpoint_run_id': {'key': 'checkpointRunId', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'bool'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'bool'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'int'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'int'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'bool'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'int'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'int'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'int'},
+        'learning_rate': {'key': 'learningRate', 'type': 'float'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'float'},
+        'nesterov': {'key': 'nesterov', 'type': 'bool'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'int'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'int'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'int'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'float'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'int'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'int'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'int'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'float'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'int'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'float'},
+    }
+
+    def __init__(
+        self,
+        *,
+        advanced_settings: Optional[str] = None,
+        ams_gradient: Optional[bool] = None,
+        augmentations: Optional[str] = None,
+        beta1: Optional[float] = None,
+        beta2: Optional[float] = None,
+        checkpoint_frequency: Optional[int] = None,
+        checkpoint_model: Optional["MLFlowModelJobInput"] = None,
+        checkpoint_run_id: Optional[str] = None,
+        distributed: Optional[bool] = None,
+        early_stopping: Optional[bool] = None,
+        early_stopping_delay: Optional[int] = None,
+        early_stopping_patience: Optional[int] = None,
+        enable_onnx_normalization: Optional[bool] = None,
+        evaluation_frequency: Optional[int] = None,
+        gradient_accumulation_step: Optional[int] = None,
+        layers_to_freeze: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[Union[str, "LearningRateScheduler"]] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[float] = None,
+        nesterov: Optional[bool] = None,
+        number_of_epochs: Optional[int] = None,
+        number_of_workers: Optional[int] = None,
+        optimizer: Optional[Union[str, "StochasticOptimizer"]] = None,
+        random_seed: Optional[int] = None,
+        step_lr_gamma: Optional[float] = None,
+        step_lr_step_size: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_cosine_lr_cycles: Optional[float] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[int] = None,
+        weight_decay: Optional[float] = None,
+        **kwargs
+    ):
+        """
+        :keyword advanced_settings: Settings for advanced scenarios.
+        :paramtype advanced_settings: str
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: bool
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: float
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: float
+        :keyword checkpoint_frequency: Frequency to store model checkpoints. Must be a positive
+         integer.
+        :paramtype checkpoint_frequency: int
+        :keyword checkpoint_model: The pretrained checkpoint model for incremental training.
+        :paramtype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+        :keyword checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+         incremental training.
+        :paramtype checkpoint_run_id: str
+        :keyword distributed: Whether to use distributed training.
+        :paramtype distributed: bool
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: bool
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: int
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: int
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: bool
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: int
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: int
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: int
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: float
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'. Possible values include: "None", "WarmupCosine", "Step".
+        :paramtype learning_rate_scheduler: str or
+         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: float
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: bool
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: int
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: int
+        :keyword optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+        :paramtype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: int
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: float
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: int
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: int
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: int
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: float
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: int
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: float
+        """
+        super(ImageModelSettings, self).__init__(**kwargs)
+        self.advanced_settings = advanced_settings
+        self.ams_gradient = ams_gradient
+        self.augmentations = augmentations
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.checkpoint_frequency = checkpoint_frequency
+        self.checkpoint_model = checkpoint_model
+        self.checkpoint_run_id = checkpoint_run_id
+        self.distributed = distributed
+        self.early_stopping = early_stopping
+        self.early_stopping_delay = early_stopping_delay
+        self.early_stopping_patience = early_stopping_patience
+        self.enable_onnx_normalization = enable_onnx_normalization
+        self.evaluation_frequency = evaluation_frequency
+        self.gradient_accumulation_step = gradient_accumulation_step
+        self.layers_to_freeze = layers_to_freeze
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.momentum = momentum
+        self.nesterov = nesterov
+        self.number_of_epochs = number_of_epochs
+        self.number_of_workers = number_of_workers
+        self.optimizer = optimizer
+        self.random_seed = random_seed
+        self.step_lr_gamma = step_lr_gamma
+        self.step_lr_step_size = step_lr_step_size
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_cosine_lr_cycles = warmup_cosine_lr_cycles
+        self.warmup_cosine_lr_warmup_epochs = warmup_cosine_lr_warmup_epochs
+        self.weight_decay = weight_decay
+
+
+class ImageModelSettingsClassification(ImageModelSettings):
+    """Settings used for training the model.
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Possible values include: "None", "WarmupCosine", "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
+    :ivar training_crop_size: Image crop size that is input to the neural network for the training
+     dataset. Must be a positive integer.
+    :vartype training_crop_size: int
+    :ivar validation_crop_size: Image crop size that is input to the neural network for the
+     validation dataset. Must be a positive integer.
+    :vartype validation_crop_size: int
+    :ivar validation_resize_size: Image size to which to resize before cropping for validation
+     dataset. Must be a positive integer.
+    :vartype validation_resize_size: int
+    :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+     1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+     0 or 1 or 2.
+    :vartype weighted_loss: int
+    """
+
+    _attribute_map = {
+        'advanced_settings': {'key': 'advancedSettings', 'type': 'str'},
+        'ams_gradient': {'key': 'amsGradient', 'type': 'bool'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'float'},
+        'beta2': {'key': 'beta2', 'type': 'float'},
+        'checkpoint_frequency': {'key': 'checkpointFrequency', 'type': 'int'},
+        'checkpoint_model': {'key': 'checkpointModel', 'type': 'MLFlowModelJobInput'},
+        'checkpoint_run_id': {'key': 'checkpointRunId', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'bool'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'bool'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'int'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'int'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'bool'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'int'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'int'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'int'},
+        'learning_rate': {'key': 'learningRate', 'type': 'float'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'float'},
+        'nesterov': {'key': 'nesterov', 'type': 'bool'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'int'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'int'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'int'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'float'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'int'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'int'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'int'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'float'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'int'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'float'},
+        'training_crop_size': {'key': 'trainingCropSize', 'type': 'int'},
+        'validation_crop_size': {'key': 'validationCropSize', 'type': 'int'},
+        'validation_resize_size': {'key': 'validationResizeSize', 'type': 'int'},
+        'weighted_loss': {'key': 'weightedLoss', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        advanced_settings: Optional[str] = None,
+        ams_gradient: Optional[bool] = None,
+        augmentations: Optional[str] = None,
+        beta1: Optional[float] = None,
+        beta2: Optional[float] = None,
+        checkpoint_frequency: Optional[int] = None,
+        checkpoint_model: Optional["MLFlowModelJobInput"] = None,
+        checkpoint_run_id: Optional[str] = None,
+        distributed: Optional[bool] = None,
+        early_stopping: Optional[bool] = None,
+        early_stopping_delay: Optional[int] = None,
+        early_stopping_patience: Optional[int] = None,
+        enable_onnx_normalization: Optional[bool] = None,
+        evaluation_frequency: Optional[int] = None,
+        gradient_accumulation_step: Optional[int] = None,
+        layers_to_freeze: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[Union[str, "LearningRateScheduler"]] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[float] = None,
+        nesterov: Optional[bool] = None,
+        number_of_epochs: Optional[int] = None,
+        number_of_workers: Optional[int] = None,
+        optimizer: Optional[Union[str, "StochasticOptimizer"]] = None,
+        random_seed: Optional[int] = None,
+        step_lr_gamma: Optional[float] = None,
+        step_lr_step_size: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_cosine_lr_cycles: Optional[float] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[int] = None,
+        weight_decay: Optional[float] = None,
+        training_crop_size: Optional[int] = None,
+        validation_crop_size: Optional[int] = None,
+        validation_resize_size: Optional[int] = None,
+        weighted_loss: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword advanced_settings: Settings for advanced scenarios.
+        :paramtype advanced_settings: str
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: bool
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: float
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: float
+        :keyword checkpoint_frequency: Frequency to store model checkpoints. Must be a positive
+         integer.
+        :paramtype checkpoint_frequency: int
+        :keyword checkpoint_model: The pretrained checkpoint model for incremental training.
+        :paramtype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+        :keyword checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+         incremental training.
+        :paramtype checkpoint_run_id: str
+        :keyword distributed: Whether to use distributed training.
+        :paramtype distributed: bool
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: bool
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: int
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: int
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: bool
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: int
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: int
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: int
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: float
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'. Possible values include: "None", "WarmupCosine", "Step".
+        :paramtype learning_rate_scheduler: str or
+         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: float
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: bool
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: int
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: int
+        :keyword optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+        :paramtype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: int
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: float
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: int
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: int
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: int
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: float
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: int
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: float
+        :keyword training_crop_size: Image crop size that is input to the neural network for the
+         training dataset. Must be a positive integer.
+        :paramtype training_crop_size: int
+        :keyword validation_crop_size: Image crop size that is input to the neural network for the
+         validation dataset. Must be a positive integer.
+        :paramtype validation_crop_size: int
+        :keyword validation_resize_size: Image size to which to resize before cropping for validation
+         dataset. Must be a positive integer.
+        :paramtype validation_resize_size: int
+        :keyword weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+         1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+         0 or 1 or 2.
+        :paramtype weighted_loss: int
+        """
+        super(ImageModelSettingsClassification, self).__init__(advanced_settings=advanced_settings, ams_gradient=ams_gradient, augmentations=augmentations, beta1=beta1, beta2=beta2, checkpoint_frequency=checkpoint_frequency, checkpoint_model=checkpoint_model, checkpoint_run_id=checkpoint_run_id, distributed=distributed, early_stopping=early_stopping, early_stopping_delay=early_stopping_delay, early_stopping_patience=early_stopping_patience, enable_onnx_normalization=enable_onnx_normalization, evaluation_frequency=evaluation_frequency, gradient_accumulation_step=gradient_accumulation_step, layers_to_freeze=layers_to_freeze, learning_rate=learning_rate, learning_rate_scheduler=learning_rate_scheduler, model_name=model_name, momentum=momentum, nesterov=nesterov, number_of_epochs=number_of_epochs, number_of_workers=number_of_workers, optimizer=optimizer, random_seed=random_seed, step_lr_gamma=step_lr_gamma, step_lr_step_size=step_lr_step_size, training_batch_size=training_batch_size, validation_batch_size=validation_batch_size, warmup_cosine_lr_cycles=warmup_cosine_lr_cycles, warmup_cosine_lr_warmup_epochs=warmup_cosine_lr_warmup_epochs, weight_decay=weight_decay, **kwargs)
+        self.training_crop_size = training_crop_size
+        self.validation_crop_size = validation_crop_size
+        self.validation_resize_size = validation_resize_size
+        self.weighted_loss = weighted_loss
+
+
+class ImageModelSettingsObjectDetection(ImageModelSettings):
+    """Settings used for training the model.
+For more information on the available settings please visit the official documentation:
+https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Possible values include: "None", "WarmupCosine", "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
+    :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
+     be a positive integer.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype box_detections_per_image: int
+    :ivar box_score_threshold: During inference, only return proposals with a classification score
+     greater than
+     BoxScoreThreshold. Must be a float in the range[0, 1].
+    :vartype box_score_threshold: float
+    :ivar image_size: Image size for train and validation. Must be a positive integer.
+     Note: The training run may get into CUDA OOM if the size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype image_size: int
+    :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype max_size: int
+    :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype min_size: int
+    :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+     Note: training run may get into CUDA OOM if the model size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm. Possible values include:
+     "None", "Small", "Medium", "Large", "ExtraLarge".
+    :vartype model_size: str or ~azure.mgmt.machinelearningservices.models.ModelSize
+    :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+     Note: training run may get into CUDA OOM if no sufficient GPU memory.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype multi_scale: bool
+    :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be a
+     float in the range [0, 1].
+    :vartype nms_iou_threshold: float
+    :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
+     be
+     None to enable small object detection logic. A string containing two integers in mxn format.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_grid_size: str
+    :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
+     in the range [0, 1).
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_overlap_ratio: float
+    :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+     predictions from tiles and image.
+     Used in validation/ inference. Must be float in the range [0, 1].
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_predictions_nms_threshold: float
+    :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
+     float in the range [0, 1].
+    :vartype validation_iou_threshold: float
+    :ivar validation_metric_type: Metric computation method to use for validation metrics. Possible
+     values include: "None", "Coco", "Voc", "CocoVoc".
+    :vartype validation_metric_type: str or
+     ~azure.mgmt.machinelearningservices.models.ValidationMetricType
+    """
+
+    _attribute_map = {
+        'advanced_settings': {'key': 'advancedSettings', 'type': 'str'},
+        'ams_gradient': {'key': 'amsGradient', 'type': 'bool'},
+        'augmentations': {'key': 'augmentations', 'type': 'str'},
+        'beta1': {'key': 'beta1', 'type': 'float'},
+        'beta2': {'key': 'beta2', 'type': 'float'},
+        'checkpoint_frequency': {'key': 'checkpointFrequency', 'type': 'int'},
+        'checkpoint_model': {'key': 'checkpointModel', 'type': 'MLFlowModelJobInput'},
+        'checkpoint_run_id': {'key': 'checkpointRunId', 'type': 'str'},
+        'distributed': {'key': 'distributed', 'type': 'bool'},
+        'early_stopping': {'key': 'earlyStopping', 'type': 'bool'},
+        'early_stopping_delay': {'key': 'earlyStoppingDelay', 'type': 'int'},
+        'early_stopping_patience': {'key': 'earlyStoppingPatience', 'type': 'int'},
+        'enable_onnx_normalization': {'key': 'enableOnnxNormalization', 'type': 'bool'},
+        'evaluation_frequency': {'key': 'evaluationFrequency', 'type': 'int'},
+        'gradient_accumulation_step': {'key': 'gradientAccumulationStep', 'type': 'int'},
+        'layers_to_freeze': {'key': 'layersToFreeze', 'type': 'int'},
+        'learning_rate': {'key': 'learningRate', 'type': 'float'},
+        'learning_rate_scheduler': {'key': 'learningRateScheduler', 'type': 'str'},
+        'model_name': {'key': 'modelName', 'type': 'str'},
+        'momentum': {'key': 'momentum', 'type': 'float'},
+        'nesterov': {'key': 'nesterov', 'type': 'bool'},
+        'number_of_epochs': {'key': 'numberOfEpochs', 'type': 'int'},
+        'number_of_workers': {'key': 'numberOfWorkers', 'type': 'int'},
+        'optimizer': {'key': 'optimizer', 'type': 'str'},
+        'random_seed': {'key': 'randomSeed', 'type': 'int'},
+        'step_lr_gamma': {'key': 'stepLRGamma', 'type': 'float'},
+        'step_lr_step_size': {'key': 'stepLRStepSize', 'type': 'int'},
+        'training_batch_size': {'key': 'trainingBatchSize', 'type': 'int'},
+        'validation_batch_size': {'key': 'validationBatchSize', 'type': 'int'},
+        'warmup_cosine_lr_cycles': {'key': 'warmupCosineLRCycles', 'type': 'float'},
+        'warmup_cosine_lr_warmup_epochs': {'key': 'warmupCosineLRWarmupEpochs', 'type': 'int'},
+        'weight_decay': {'key': 'weightDecay', 'type': 'float'},
+        'box_detections_per_image': {'key': 'boxDetectionsPerImage', 'type': 'int'},
+        'box_score_threshold': {'key': 'boxScoreThreshold', 'type': 'float'},
+        'image_size': {'key': 'imageSize', 'type': 'int'},
+        'max_size': {'key': 'maxSize', 'type': 'int'},
+        'min_size': {'key': 'minSize', 'type': 'int'},
+        'model_size': {'key': 'modelSize', 'type': 'str'},
+        'multi_scale': {'key': 'multiScale', 'type': 'bool'},
+        'nms_iou_threshold': {'key': 'nmsIouThreshold', 'type': 'float'},
+        'tile_grid_size': {'key': 'tileGridSize', 'type': 'str'},
+        'tile_overlap_ratio': {'key': 'tileOverlapRatio', 'type': 'float'},
+        'tile_predictions_nms_threshold': {'key': 'tilePredictionsNmsThreshold', 'type': 'float'},
+        'validation_iou_threshold': {'key': 'validationIouThreshold', 'type': 'float'},
+        'validation_metric_type': {'key': 'validationMetricType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        advanced_settings: Optional[str] = None,
+        ams_gradient: Optional[bool] = None,
+        augmentations: Optional[str] = None,
+        beta1: Optional[float] = None,
+        beta2: Optional[float] = None,
+        checkpoint_frequency: Optional[int] = None,
+        checkpoint_model: Optional["MLFlowModelJobInput"] = None,
+        checkpoint_run_id: Optional[str] = None,
+        distributed: Optional[bool] = None,
+        early_stopping: Optional[bool] = None,
+        early_stopping_delay: Optional[int] = None,
+        early_stopping_patience: Optional[int] = None,
+        enable_onnx_normalization: Optional[bool] = None,
+        evaluation_frequency: Optional[int] = None,
+        gradient_accumulation_step: Optional[int] = None,
+        layers_to_freeze: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[Union[str, "LearningRateScheduler"]] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[float] = None,
+        nesterov: Optional[bool] = None,
+        number_of_epochs: Optional[int] = None,
+        number_of_workers: Optional[int] = None,
+        optimizer: Optional[Union[str, "StochasticOptimizer"]] = None,
+        random_seed: Optional[int] = None,
+        step_lr_gamma: Optional[float] = None,
+        step_lr_step_size: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_cosine_lr_cycles: Optional[float] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[int] = None,
+        weight_decay: Optional[float] = None,
+        box_detections_per_image: Optional[int] = None,
+        box_score_threshold: Optional[float] = None,
+        image_size: Optional[int] = None,
+        max_size: Optional[int] = None,
+        min_size: Optional[int] = None,
+        model_size: Optional[Union[str, "ModelSize"]] = None,
+        multi_scale: Optional[bool] = None,
+        nms_iou_threshold: Optional[float] = None,
+        tile_grid_size: Optional[str] = None,
+        tile_overlap_ratio: Optional[float] = None,
+        tile_predictions_nms_threshold: Optional[float] = None,
+        validation_iou_threshold: Optional[float] = None,
+        validation_metric_type: Optional[Union[str, "ValidationMetricType"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword advanced_settings: Settings for advanced scenarios.
+        :paramtype advanced_settings: str
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: bool
+        :keyword augmentations: Settings for using Augmentations.
+        :paramtype augmentations: str
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: float
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: float
+        :keyword checkpoint_frequency: Frequency to store model checkpoints. Must be a positive
+         integer.
+        :paramtype checkpoint_frequency: int
+        :keyword checkpoint_model: The pretrained checkpoint model for incremental training.
+        :paramtype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+        :keyword checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+         incremental training.
+        :paramtype checkpoint_run_id: str
+        :keyword distributed: Whether to use distributed training.
+        :paramtype distributed: bool
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: bool
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: int
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: int
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: bool
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: int
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: int
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype layers_to_freeze: int
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: float
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'. Possible values include: "None", "WarmupCosine", "Step".
+        :paramtype learning_rate_scheduler: str or
+         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :paramtype model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: float
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: bool
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: int
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: int
+        :keyword optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+        :paramtype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: int
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: float
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: int
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: int
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: int
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: float
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: int
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: float
+        :keyword box_detections_per_image: Maximum number of detections per image, for all classes.
+         Must be a positive integer.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype box_detections_per_image: int
+        :keyword box_score_threshold: During inference, only return proposals with a classification
+         score greater than
+         BoxScoreThreshold. Must be a float in the range[0, 1].
+        :paramtype box_score_threshold: float
+        :keyword image_size: Image size for train and validation. Must be a positive integer.
+         Note: The training run may get into CUDA OOM if the size is too big.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :paramtype image_size: int
+        :keyword max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype max_size: int
+        :keyword min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype min_size: int
+        :keyword model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+         Note: training run may get into CUDA OOM if the model size is too big.
+         Note: This settings is only supported for the 'yolov5' algorithm. Possible values include:
+         "None", "Small", "Medium", "Large", "ExtraLarge".
+        :paramtype model_size: str or ~azure.mgmt.machinelearningservices.models.ModelSize
+        :keyword multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+         Note: training run may get into CUDA OOM if no sufficient GPU memory.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :paramtype multi_scale: bool
+        :keyword nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
+         a float in the range [0, 1].
+        :paramtype nms_iou_threshold: float
+        :keyword tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must
+         not be
+         None to enable small object detection logic. A string containing two integers in mxn format.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype tile_grid_size: str
+        :keyword tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be
+         float in the range [0, 1).
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype tile_overlap_ratio: float
+        :keyword tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+         predictions from tiles and image.
+         Used in validation/ inference. Must be float in the range [0, 1].
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :paramtype tile_predictions_nms_threshold: float
+        :keyword validation_iou_threshold: IOU threshold to use when computing validation metric. Must
+         be float in the range [0, 1].
+        :paramtype validation_iou_threshold: float
+        :keyword validation_metric_type: Metric computation method to use for validation metrics.
+         Possible values include: "None", "Coco", "Voc", "CocoVoc".
+        :paramtype validation_metric_type: str or
+         ~azure.mgmt.machinelearningservices.models.ValidationMetricType
+        """
+        super(ImageModelSettingsObjectDetection, self).__init__(advanced_settings=advanced_settings, ams_gradient=ams_gradient, augmentations=augmentations, beta1=beta1, beta2=beta2, checkpoint_frequency=checkpoint_frequency, checkpoint_model=checkpoint_model, checkpoint_run_id=checkpoint_run_id, distributed=distributed, early_stopping=early_stopping, early_stopping_delay=early_stopping_delay, early_stopping_patience=early_stopping_patience, enable_onnx_normalization=enable_onnx_normalization, evaluation_frequency=evaluation_frequency, gradient_accumulation_step=gradient_accumulation_step, layers_to_freeze=layers_to_freeze, learning_rate=learning_rate, learning_rate_scheduler=learning_rate_scheduler, model_name=model_name, momentum=momentum, nesterov=nesterov, number_of_epochs=number_of_epochs, number_of_workers=number_of_workers, optimizer=optimizer, random_seed=random_seed, step_lr_gamma=step_lr_gamma, step_lr_step_size=step_lr_step_size, training_batch_size=training_batch_size, validation_batch_size=validation_batch_size, warmup_cosine_lr_cycles=warmup_cosine_lr_cycles, warmup_cosine_lr_warmup_epochs=warmup_cosine_lr_warmup_epochs, weight_decay=weight_decay, **kwargs)
+        self.box_detections_per_image = box_detections_per_image
+        self.box_score_threshold = box_score_threshold
+        self.image_size = image_size
+        self.max_size = max_size
+        self.min_size = min_size
+        self.model_size = model_size
+        self.multi_scale = multi_scale
+        self.nms_iou_threshold = nms_iou_threshold
+        self.tile_grid_size = tile_grid_size
+        self.tile_overlap_ratio = tile_overlap_ratio
+        self.tile_predictions_nms_threshold = tile_predictions_nms_threshold
+        self.validation_iou_threshold = validation_iou_threshold
+        self.validation_metric_type = validation_metric_type
+
+
+class ImageObjectDetection(AutoMLVertical, ImageObjectDetectionBase):
+    """Image Object Detection. Object detection is used to identify objects in an image and locate each object with a
+bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar limit_settings: Required. [Required] Limit settings for the AutoML job.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric to optimize for this task. Possible values include:
+     "MeanAveragePrecision".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ObjectDetectionPrimaryMetrics
+    """
+
+    _validation = {
+        'limit_settings': {'required': True},
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'limit_settings': {'key': 'limitSettings', 'type': 'ImageLimitSettings'},
+        'sweep_settings': {'key': 'sweepSettings', 'type': 'ImageSweepSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'model_settings': {'key': 'modelSettings', 'type': 'ImageModelSettingsObjectDetection'},
+        'search_space': {'key': 'searchSpace', 'type': '[ImageModelDistributionSettingsObjectDetection]'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        limit_settings: "ImageLimitSettings",
+        training_data: "MLTableJobInput",
+        sweep_settings: Optional["ImageSweepSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        model_settings: Optional["ImageModelSettingsObjectDetection"] = None,
+        search_space: Optional[List["ImageModelDistributionSettingsObjectDetection"]] = None,
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        primary_metric: Optional[Union[str, "ObjectDetectionPrimaryMetrics"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword limit_settings: Required. [Required] Limit settings for the AutoML job.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+        :keyword sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword model_settings: Settings used for training the model.
+        :paramtype model_settings:
+         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric to optimize for this task. Possible values include:
+         "MeanAveragePrecision".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ObjectDetectionPrimaryMetrics
+        """
+        super(ImageObjectDetection, self).__init__(log_verbosity=log_verbosity, target_column_name=target_column_name, training_data=training_data, limit_settings=limit_settings, sweep_settings=sweep_settings, validation_data=validation_data, validation_data_size=validation_data_size, model_settings=model_settings, search_space=search_space, **kwargs)
+        self.limit_settings = limit_settings
+        self.sweep_settings = sweep_settings
+        self.validation_data = validation_data
+        self.validation_data_size = validation_data_size
+        self.model_settings = model_settings
+        self.search_space = search_space
+        self.task_type = 'ImageObjectDetection'  # type: str
+        self.primary_metric = primary_metric
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+
+
+class ImageSweepSettings(msrest.serialization.Model):
+    """Model sweeping and hyperparameter sweeping related settings.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar early_termination: Type of early termination policy.
+    :vartype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+    :ivar sampling_algorithm: Required. [Required] Type of the hyperparameter sampling algorithms.
+     Possible values include: "Grid", "Random", "Bayesian".
+    :vartype sampling_algorithm: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    """
+
+    _validation = {
+        'sampling_algorithm': {'required': True},
+    }
+
+    _attribute_map = {
+        'early_termination': {'key': 'earlyTermination', 'type': 'EarlyTerminationPolicy'},
+        'sampling_algorithm': {'key': 'samplingAlgorithm', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        sampling_algorithm: Union[str, "SamplingAlgorithmType"],
+        early_termination: Optional["EarlyTerminationPolicy"] = None,
+        **kwargs
+    ):
+        """
+        :keyword early_termination: Type of early termination policy.
+        :paramtype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+        :keyword sampling_algorithm: Required. [Required] Type of the hyperparameter sampling
+         algorithms. Possible values include: "Grid", "Random", "Bayesian".
+        :paramtype sampling_algorithm: str or
+         ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+        """
+        super(ImageSweepSettings, self).__init__(**kwargs)
+        self.early_termination = early_termination
+        self.sampling_algorithm = sampling_algorithm
+
+
+class InferenceContainerProperties(msrest.serialization.Model):
+    """InferenceContainerProperties.
+
+    :ivar liveness_route: The route to check the liveness of the inference server container.
+    :vartype liveness_route: ~azure.mgmt.machinelearningservices.models.Route
+    :ivar readiness_route: The route to check the readiness of the inference server container.
+    :vartype readiness_route: ~azure.mgmt.machinelearningservices.models.Route
+    :ivar scoring_route: The port to send the scoring requests to, within the inference server
+     container.
+    :vartype scoring_route: ~azure.mgmt.machinelearningservices.models.Route
+    """
+
+    _attribute_map = {
+        'liveness_route': {'key': 'livenessRoute', 'type': 'Route'},
+        'readiness_route': {'key': 'readinessRoute', 'type': 'Route'},
+        'scoring_route': {'key': 'scoringRoute', 'type': 'Route'},
+    }
+
+    def __init__(
+        self,
+        *,
+        liveness_route: Optional["Route"] = None,
+        readiness_route: Optional["Route"] = None,
+        scoring_route: Optional["Route"] = None,
+        **kwargs
+    ):
+        """
+        :keyword liveness_route: The route to check the liveness of the inference server container.
+        :paramtype liveness_route: ~azure.mgmt.machinelearningservices.models.Route
+        :keyword readiness_route: The route to check the readiness of the inference server container.
+        :paramtype readiness_route: ~azure.mgmt.machinelearningservices.models.Route
+        :keyword scoring_route: The port to send the scoring requests to, within the inference server
+         container.
+        :paramtype scoring_route: ~azure.mgmt.machinelearningservices.models.Route
+        """
+        super(InferenceContainerProperties, self).__init__(**kwargs)
+        self.liveness_route = liveness_route
+        self.readiness_route = readiness_route
+        self.scoring_route = scoring_route
+
+
+class InstanceTypeSchema(msrest.serialization.Model):
+    """Instance type schema.
+
+    :ivar node_selector: Node Selector.
+    :vartype node_selector: dict[str, str]
+    :ivar resources: Resource requests/limits for this instance type.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.InstanceTypeSchemaResources
+    """
+
+    _attribute_map = {
+        'node_selector': {'key': 'nodeSelector', 'type': '{str}'},
+        'resources': {'key': 'resources', 'type': 'InstanceTypeSchemaResources'},
+    }
+
+    def __init__(
+        self,
+        *,
+        node_selector: Optional[Dict[str, str]] = None,
+        resources: Optional["InstanceTypeSchemaResources"] = None,
+        **kwargs
+    ):
+        """
+        :keyword node_selector: Node Selector.
+        :paramtype node_selector: dict[str, str]
+        :keyword resources: Resource requests/limits for this instance type.
+        :paramtype resources: ~azure.mgmt.machinelearningservices.models.InstanceTypeSchemaResources
+        """
+        super(InstanceTypeSchema, self).__init__(**kwargs)
+        self.node_selector = node_selector
+        self.resources = resources
+
+
+class InstanceTypeSchemaResources(msrest.serialization.Model):
+    """Resource requests/limits for this instance type.
+
+    :ivar requests: Resource requests for this instance type.
+    :vartype requests: dict[str, str]
+    :ivar limits: Resource limits for this instance type.
+    :vartype limits: dict[str, str]
+    """
+
+    _attribute_map = {
+        'requests': {'key': 'requests', 'type': '{str}'},
+        'limits': {'key': 'limits', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        requests: Optional[Dict[str, str]] = None,
+        limits: Optional[Dict[str, str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword requests: Resource requests for this instance type.
+        :paramtype requests: dict[str, str]
+        :keyword limits: Resource limits for this instance type.
+        :paramtype limits: dict[str, str]
+        """
+        super(InstanceTypeSchemaResources, self).__init__(**kwargs)
+        self.requests = requests
+        self.limits = limits
+
+
+class JobBase(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'JobBaseProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "JobBaseProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
+        """
+        super(JobBase, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class JobBaseResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of JobBase entities.
+
+    :ivar next_link: The link to the next page of JobBase objects. If null, there are no additional
+     pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type JobBase.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.JobBase]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[JobBase]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["JobBase"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of JobBase objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type JobBase.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.JobBase]
+        """
+        super(JobBaseResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class JobResourceConfiguration(ResourceConfiguration):
+    """JobResourceConfiguration.
+
+    :ivar instance_count: Optional number of instances or nodes used by the compute target.
+    :vartype instance_count: int
+    :ivar instance_type: Optional type of VM used as supported by the compute target.
+    :vartype instance_type: str
+    :ivar properties: Additional properties bag.
+    :vartype properties: dict[str, any]
+    :ivar docker_args: Extra arguments to pass to the Docker run command. This would override any
+     parameters that have already been set by the system, or in this section. This parameter is only
+     supported for Azure ML compute types.
+    :vartype docker_args: str
+    :ivar shm_size: Size of the docker container's shared memory block. This should be in the
+     format of (number)(unit) where number as to be greater than 0 and the unit can be one of
+     b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
+    :vartype shm_size: str
+    """
+
+    _validation = {
+        'shm_size': {'pattern': r'\d+[bBkKmMgG]'},
+    }
+
+    _attribute_map = {
+        'instance_count': {'key': 'instanceCount', 'type': 'int'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{object}'},
+        'docker_args': {'key': 'dockerArgs', 'type': 'str'},
+        'shm_size': {'key': 'shmSize', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        instance_count: Optional[int] = 1,
+        instance_type: Optional[str] = None,
+        properties: Optional[Dict[str, Any]] = None,
+        docker_args: Optional[str] = None,
+        shm_size: Optional[str] = "2g",
+        **kwargs
+    ):
+        """
+        :keyword instance_count: Optional number of instances or nodes used by the compute target.
+        :paramtype instance_count: int
+        :keyword instance_type: Optional type of VM used as supported by the compute target.
+        :paramtype instance_type: str
+        :keyword properties: Additional properties bag.
+        :paramtype properties: dict[str, any]
+        :keyword docker_args: Extra arguments to pass to the Docker run command. This would override
+         any parameters that have already been set by the system, or in this section. This parameter is
+         only supported for Azure ML compute types.
+        :paramtype docker_args: str
+        :keyword shm_size: Size of the docker container's shared memory block. This should be in the
+         format of (number)(unit) where number as to be greater than 0 and the unit can be one of
+         b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
+        :paramtype shm_size: str
+        """
+        super(JobResourceConfiguration, self).__init__(instance_count=instance_count, instance_type=instance_type, properties=properties, **kwargs)
+        self.docker_args = docker_args
+        self.shm_size = shm_size
+
+
+class JobScheduleAction(ScheduleActionBase):
+    """JobScheduleAction.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar action_type: Required. [Required] Specifies the action type of the schedule.Constant
+     filled by server. Possible values include: "CreateJob", "InvokeBatchEndpoint".
+    :vartype action_type: str or ~azure.mgmt.machinelearningservices.models.ScheduleActionType
+    :ivar job_definition: Required. [Required] Defines Schedule action definition details.
+    :vartype job_definition: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
+    """
+
+    _validation = {
+        'action_type': {'required': True},
+        'job_definition': {'required': True},
+    }
+
+    _attribute_map = {
+        'action_type': {'key': 'actionType', 'type': 'str'},
+        'job_definition': {'key': 'jobDefinition', 'type': 'JobBaseProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        job_definition: "JobBaseProperties",
+        **kwargs
+    ):
+        """
+        :keyword job_definition: Required. [Required] Defines Schedule action definition details.
+        :paramtype job_definition: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
+        """
+        super(JobScheduleAction, self).__init__(**kwargs)
+        self.action_type = 'CreateJob'  # type: str
+        self.job_definition = job_definition
+
+
+class JobService(msrest.serialization.Model):
+    """Job endpoint definition.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar endpoint: Url for endpoint.
+    :vartype endpoint: str
+    :ivar error_message: Any error in the service.
+    :vartype error_message: str
+    :ivar job_service_type: Endpoint type.
+    :vartype job_service_type: str
+    :ivar port: Port for endpoint.
+    :vartype port: int
+    :ivar properties: Additional properties to set on the endpoint.
+    :vartype properties: dict[str, str]
+    :ivar status: Status of endpoint.
+    :vartype status: str
+    """
+
+    _validation = {
+        'error_message': {'readonly': True},
+        'status': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'endpoint': {'key': 'endpoint', 'type': 'str'},
+        'error_message': {'key': 'errorMessage', 'type': 'str'},
+        'job_service_type': {'key': 'jobServiceType', 'type': 'str'},
+        'port': {'key': 'port', 'type': 'int'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'status': {'key': 'status', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        endpoint: Optional[str] = None,
+        job_service_type: Optional[str] = None,
+        port: Optional[int] = None,
+        properties: Optional[Dict[str, str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword endpoint: Url for endpoint.
+        :paramtype endpoint: str
+        :keyword job_service_type: Endpoint type.
+        :paramtype job_service_type: str
+        :keyword port: Port for endpoint.
+        :paramtype port: int
+        :keyword properties: Additional properties to set on the endpoint.
+        :paramtype properties: dict[str, str]
+        """
+        super(JobService, self).__init__(**kwargs)
+        self.endpoint = endpoint
+        self.error_message = None
+        self.job_service_type = job_service_type
+        self.port = port
+        self.properties = properties
+        self.status = None
+
+
+class KubernetesSchema(msrest.serialization.Model):
+    """Kubernetes Compute Schema.
+
+    :ivar properties: Properties of Kubernetes.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'KubernetesProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["KubernetesProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of Kubernetes.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+        """
+        super(KubernetesSchema, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class Kubernetes(Compute, KubernetesSchema):
+    """A Machine Learning compute based on Kubernetes Compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: Properties of Kubernetes.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'KubernetesProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["KubernetesProperties"] = None,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Properties of Kubernetes.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(Kubernetes, self).__init__(description=description, resource_id=resource_id, properties=properties, **kwargs)
+        self.properties = properties
+        self.compute_type = 'Kubernetes'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = description
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = resource_id
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class OnlineDeploymentProperties(EndpointDeploymentPropertiesBase):
+    """OnlineDeploymentProperties.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: KubernetesOnlineDeployment, ManagedOnlineDeployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar code_configuration: Code configuration for the endpoint deployment.
+    :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
+     endpoint deployment.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables configuration for the deployment.
+    :vartype environment_variables: dict[str, str]
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar app_insights_enabled: If true, enables Application Insights logging.
+    :vartype app_insights_enabled: bool
+    :ivar egress_public_network_access: If Enabled, allow egress public network access. If
+     Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+     "Disabled".
+    :vartype egress_public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+    :ivar endpoint_compute_type: Required. [Required] The compute type of the endpoint.Constant
+     filled by server. Possible values include: "Managed", "Kubernetes", "AzureMLCompute".
+    :vartype endpoint_compute_type: str or
+     ~azure.mgmt.machinelearningservices.models.EndpointComputeType
+    :ivar instance_type: Compute instance type.
+    :vartype instance_type: str
+    :ivar liveness_probe: Liveness probe monitors the health of the container regularly.
+    :vartype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar model: The URI path to the model.
+    :vartype model: str
+    :ivar model_mount_path: The path to mount the model in custom container.
+    :vartype model_mount_path: str
+    :ivar provisioning_state: Provisioning state for the endpoint deployment. Possible values
+     include: "Creating", "Deleting", "Scaling", "Updating", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.DeploymentProvisioningState
+    :ivar readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+     The properties and defaults are the same as liveness probe.
+    :vartype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar request_settings: Request settings for the deployment.
+    :vartype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+    :ivar scale_settings: Scale settings for the deployment.
+     If it is null or not provided,
+     it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+     and to DefaultScaleSettings for ManagedOnlineDeployment.
+    :vartype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+    """
+
+    _validation = {
+        'endpoint_compute_type': {'required': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code_configuration': {'key': 'codeConfiguration', 'type': 'CodeConfiguration'},
+        'description': {'key': 'description', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
+        'egress_public_network_access': {'key': 'egressPublicNetworkAccess', 'type': 'str'},
+        'endpoint_compute_type': {'key': 'endpointComputeType', 'type': 'str'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'liveness_probe': {'key': 'livenessProbe', 'type': 'ProbeSettings'},
+        'model': {'key': 'model', 'type': 'str'},
+        'model_mount_path': {'key': 'modelMountPath', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'readiness_probe': {'key': 'readinessProbe', 'type': 'ProbeSettings'},
+        'request_settings': {'key': 'requestSettings', 'type': 'OnlineRequestSettings'},
+        'scale_settings': {'key': 'scaleSettings', 'type': 'OnlineScaleSettings'},
+    }
+
+    _subtype_map = {
+        'endpoint_compute_type': {'Kubernetes': 'KubernetesOnlineDeployment', 'Managed': 'ManagedOnlineDeployment'}
+    }
+
+    def __init__(
+        self,
+        *,
+        code_configuration: Optional["CodeConfiguration"] = None,
+        description: Optional[str] = None,
+        environment_id: Optional[str] = None,
+        environment_variables: Optional[Dict[str, str]] = None,
+        properties: Optional[Dict[str, str]] = None,
+        app_insights_enabled: Optional[bool] = False,
+        egress_public_network_access: Optional[Union[str, "EgressPublicNetworkAccessType"]] = None,
+        instance_type: Optional[str] = None,
+        liveness_probe: Optional["ProbeSettings"] = None,
+        model: Optional[str] = None,
+        model_mount_path: Optional[str] = None,
+        readiness_probe: Optional["ProbeSettings"] = None,
+        request_settings: Optional["OnlineRequestSettings"] = None,
+        scale_settings: Optional["OnlineScaleSettings"] = None,
+        **kwargs
+    ):
+        """
+        :keyword code_configuration: Code configuration for the endpoint deployment.
+        :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
+         endpoint deployment.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables configuration for the deployment.
+        :paramtype environment_variables: dict[str, str]
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword app_insights_enabled: If true, enables Application Insights logging.
+        :paramtype app_insights_enabled: bool
+        :keyword egress_public_network_access: If Enabled, allow egress public network access. If
+         Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+         "Disabled".
+        :paramtype egress_public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+        :keyword instance_type: Compute instance type.
+        :paramtype instance_type: str
+        :keyword liveness_probe: Liveness probe monitors the health of the container regularly.
+        :paramtype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword model: The URI path to the model.
+        :paramtype model: str
+        :keyword model_mount_path: The path to mount the model in custom container.
+        :paramtype model_mount_path: str
+        :keyword readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+         The properties and defaults are the same as liveness probe.
+        :paramtype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword request_settings: Request settings for the deployment.
+        :paramtype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+        :keyword scale_settings: Scale settings for the deployment.
+         If it is null or not provided,
+         it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+         and to DefaultScaleSettings for ManagedOnlineDeployment.
+        :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+        """
+        super(OnlineDeploymentProperties, self).__init__(code_configuration=code_configuration, description=description, environment_id=environment_id, environment_variables=environment_variables, properties=properties, **kwargs)
+        self.app_insights_enabled = app_insights_enabled
+        self.egress_public_network_access = egress_public_network_access
+        self.endpoint_compute_type = 'OnlineDeploymentProperties'  # type: str
+        self.instance_type = instance_type
+        self.liveness_probe = liveness_probe
+        self.model = model
+        self.model_mount_path = model_mount_path
+        self.provisioning_state = None
+        self.readiness_probe = readiness_probe
+        self.request_settings = request_settings
+        self.scale_settings = scale_settings
+
+
+class KubernetesOnlineDeployment(OnlineDeploymentProperties):
+    """Properties specific to a KubernetesOnlineDeployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar code_configuration: Code configuration for the endpoint deployment.
+    :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
+     endpoint deployment.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables configuration for the deployment.
+    :vartype environment_variables: dict[str, str]
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar app_insights_enabled: If true, enables Application Insights logging.
+    :vartype app_insights_enabled: bool
+    :ivar egress_public_network_access: If Enabled, allow egress public network access. If
+     Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+     "Disabled".
+    :vartype egress_public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+    :ivar endpoint_compute_type: Required. [Required] The compute type of the endpoint.Constant
+     filled by server. Possible values include: "Managed", "Kubernetes", "AzureMLCompute".
+    :vartype endpoint_compute_type: str or
+     ~azure.mgmt.machinelearningservices.models.EndpointComputeType
+    :ivar instance_type: Compute instance type.
+    :vartype instance_type: str
+    :ivar liveness_probe: Liveness probe monitors the health of the container regularly.
+    :vartype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar model: The URI path to the model.
+    :vartype model: str
+    :ivar model_mount_path: The path to mount the model in custom container.
+    :vartype model_mount_path: str
+    :ivar provisioning_state: Provisioning state for the endpoint deployment. Possible values
+     include: "Creating", "Deleting", "Scaling", "Updating", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.DeploymentProvisioningState
+    :ivar readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+     The properties and defaults are the same as liveness probe.
+    :vartype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar request_settings: Request settings for the deployment.
+    :vartype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+    :ivar scale_settings: Scale settings for the deployment.
+     If it is null or not provided,
+     it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+     and to DefaultScaleSettings for ManagedOnlineDeployment.
+    :vartype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+    :ivar container_resource_requirements: The resource requirements for the container (cpu and
+     memory).
+    :vartype container_resource_requirements:
+     ~azure.mgmt.machinelearningservices.models.ContainerResourceRequirements
+    """
+
+    _validation = {
+        'endpoint_compute_type': {'required': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code_configuration': {'key': 'codeConfiguration', 'type': 'CodeConfiguration'},
+        'description': {'key': 'description', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
+        'egress_public_network_access': {'key': 'egressPublicNetworkAccess', 'type': 'str'},
+        'endpoint_compute_type': {'key': 'endpointComputeType', 'type': 'str'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'liveness_probe': {'key': 'livenessProbe', 'type': 'ProbeSettings'},
+        'model': {'key': 'model', 'type': 'str'},
+        'model_mount_path': {'key': 'modelMountPath', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'readiness_probe': {'key': 'readinessProbe', 'type': 'ProbeSettings'},
+        'request_settings': {'key': 'requestSettings', 'type': 'OnlineRequestSettings'},
+        'scale_settings': {'key': 'scaleSettings', 'type': 'OnlineScaleSettings'},
+        'container_resource_requirements': {'key': 'containerResourceRequirements', 'type': 'ContainerResourceRequirements'},
+    }
+
+    def __init__(
+        self,
+        *,
+        code_configuration: Optional["CodeConfiguration"] = None,
+        description: Optional[str] = None,
+        environment_id: Optional[str] = None,
+        environment_variables: Optional[Dict[str, str]] = None,
+        properties: Optional[Dict[str, str]] = None,
+        app_insights_enabled: Optional[bool] = False,
+        egress_public_network_access: Optional[Union[str, "EgressPublicNetworkAccessType"]] = None,
+        instance_type: Optional[str] = None,
+        liveness_probe: Optional["ProbeSettings"] = None,
+        model: Optional[str] = None,
+        model_mount_path: Optional[str] = None,
+        readiness_probe: Optional["ProbeSettings"] = None,
+        request_settings: Optional["OnlineRequestSettings"] = None,
+        scale_settings: Optional["OnlineScaleSettings"] = None,
+        container_resource_requirements: Optional["ContainerResourceRequirements"] = None,
+        **kwargs
+    ):
+        """
+        :keyword code_configuration: Code configuration for the endpoint deployment.
+        :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
+         endpoint deployment.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables configuration for the deployment.
+        :paramtype environment_variables: dict[str, str]
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword app_insights_enabled: If true, enables Application Insights logging.
+        :paramtype app_insights_enabled: bool
+        :keyword egress_public_network_access: If Enabled, allow egress public network access. If
+         Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+         "Disabled".
+        :paramtype egress_public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+        :keyword instance_type: Compute instance type.
+        :paramtype instance_type: str
+        :keyword liveness_probe: Liveness probe monitors the health of the container regularly.
+        :paramtype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword model: The URI path to the model.
+        :paramtype model: str
+        :keyword model_mount_path: The path to mount the model in custom container.
+        :paramtype model_mount_path: str
+        :keyword readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+         The properties and defaults are the same as liveness probe.
+        :paramtype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword request_settings: Request settings for the deployment.
+        :paramtype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+        :keyword scale_settings: Scale settings for the deployment.
+         If it is null or not provided,
+         it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+         and to DefaultScaleSettings for ManagedOnlineDeployment.
+        :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+        :keyword container_resource_requirements: The resource requirements for the container (cpu and
+         memory).
+        :paramtype container_resource_requirements:
+         ~azure.mgmt.machinelearningservices.models.ContainerResourceRequirements
+        """
+        super(KubernetesOnlineDeployment, self).__init__(code_configuration=code_configuration, description=description, environment_id=environment_id, environment_variables=environment_variables, properties=properties, app_insights_enabled=app_insights_enabled, egress_public_network_access=egress_public_network_access, instance_type=instance_type, liveness_probe=liveness_probe, model=model, model_mount_path=model_mount_path, readiness_probe=readiness_probe, request_settings=request_settings, scale_settings=scale_settings, **kwargs)
+        self.endpoint_compute_type = 'Kubernetes'  # type: str
+        self.container_resource_requirements = container_resource_requirements
+
+
+class KubernetesProperties(msrest.serialization.Model):
+    """Kubernetes properties.
+
+    :ivar relay_connection_string: Relay connection string.
+    :vartype relay_connection_string: str
+    :ivar service_bus_connection_string: ServiceBus connection string.
+    :vartype service_bus_connection_string: str
+    :ivar extension_principal_id: Extension principal-id.
+    :vartype extension_principal_id: str
+    :ivar extension_instance_release_train: Extension instance release train.
+    :vartype extension_instance_release_train: str
+    :ivar vc_name: VC name.
+    :vartype vc_name: str
+    :ivar namespace: Compute namespace.
+    :vartype namespace: str
+    :ivar default_instance_type: Default instance type.
+    :vartype default_instance_type: str
+    :ivar instance_types: Instance Type Schema.
+    :vartype instance_types: dict[str,
+     ~azure.mgmt.machinelearningservices.models.InstanceTypeSchema]
+    """
+
+    _attribute_map = {
+        'relay_connection_string': {'key': 'relayConnectionString', 'type': 'str'},
+        'service_bus_connection_string': {'key': 'serviceBusConnectionString', 'type': 'str'},
+        'extension_principal_id': {'key': 'extensionPrincipalId', 'type': 'str'},
+        'extension_instance_release_train': {'key': 'extensionInstanceReleaseTrain', 'type': 'str'},
+        'vc_name': {'key': 'vcName', 'type': 'str'},
+        'namespace': {'key': 'namespace', 'type': 'str'},
+        'default_instance_type': {'key': 'defaultInstanceType', 'type': 'str'},
+        'instance_types': {'key': 'instanceTypes', 'type': '{InstanceTypeSchema}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        relay_connection_string: Optional[str] = None,
+        service_bus_connection_string: Optional[str] = None,
+        extension_principal_id: Optional[str] = None,
+        extension_instance_release_train: Optional[str] = None,
+        vc_name: Optional[str] = None,
+        namespace: Optional[str] = "default",
+        default_instance_type: Optional[str] = None,
+        instance_types: Optional[Dict[str, "InstanceTypeSchema"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword relay_connection_string: Relay connection string.
+        :paramtype relay_connection_string: str
+        :keyword service_bus_connection_string: ServiceBus connection string.
+        :paramtype service_bus_connection_string: str
+        :keyword extension_principal_id: Extension principal-id.
+        :paramtype extension_principal_id: str
+        :keyword extension_instance_release_train: Extension instance release train.
+        :paramtype extension_instance_release_train: str
+        :keyword vc_name: VC name.
+        :paramtype vc_name: str
+        :keyword namespace: Compute namespace.
+        :paramtype namespace: str
+        :keyword default_instance_type: Default instance type.
+        :paramtype default_instance_type: str
+        :keyword instance_types: Instance Type Schema.
+        :paramtype instance_types: dict[str,
+         ~azure.mgmt.machinelearningservices.models.InstanceTypeSchema]
+        """
+        super(KubernetesProperties, self).__init__(**kwargs)
+        self.relay_connection_string = relay_connection_string
+        self.service_bus_connection_string = service_bus_connection_string
+        self.extension_principal_id = extension_principal_id
+        self.extension_instance_release_train = extension_instance_release_train
+        self.vc_name = vc_name
+        self.namespace = namespace
+        self.default_instance_type = default_instance_type
+        self.instance_types = instance_types
+
+
+class ListAmlUserFeatureResult(msrest.serialization.Model):
+    """The List Aml user feature operation response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of AML user facing features.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.AmlUserFeature]
+    :ivar next_link: The URI to fetch the next page of AML user features information. Call
+     ListNext() with this to fetch the next page of AML user features information.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[AmlUserFeature]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListAmlUserFeatureResult, self).__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class ListNotebookKeysResult(msrest.serialization.Model):
+    """ListNotebookKeysResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar primary_access_key:
+    :vartype primary_access_key: str
+    :ivar secondary_access_key:
+    :vartype secondary_access_key: str
+    """
+
+    _validation = {
+        'primary_access_key': {'readonly': True},
+        'secondary_access_key': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'primary_access_key': {'key': 'primaryAccessKey', 'type': 'str'},
+        'secondary_access_key': {'key': 'secondaryAccessKey', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListNotebookKeysResult, self).__init__(**kwargs)
+        self.primary_access_key = None
+        self.secondary_access_key = None
+
+
+class ListStorageAccountKeysResult(msrest.serialization.Model):
+    """ListStorageAccountKeysResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar user_storage_key:
+    :vartype user_storage_key: str
+    """
+
+    _validation = {
+        'user_storage_key': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'user_storage_key': {'key': 'userStorageKey', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListStorageAccountKeysResult, self).__init__(**kwargs)
+        self.user_storage_key = None
+
+
+class ListUsagesResult(msrest.serialization.Model):
+    """The List Usages operation response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of AML resource usages.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Usage]
+    :ivar next_link: The URI to fetch the next page of AML resource usage information. Call
+     ListNext() with this to fetch the next page of AML resource usage information.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[Usage]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListUsagesResult, self).__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class ListWorkspaceKeysResult(msrest.serialization.Model):
+    """ListWorkspaceKeysResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar user_storage_key:
+    :vartype user_storage_key: str
+    :ivar user_storage_resource_id:
+    :vartype user_storage_resource_id: str
+    :ivar app_insights_instrumentation_key:
+    :vartype app_insights_instrumentation_key: str
+    :ivar container_registry_credentials:
+    :vartype container_registry_credentials:
+     ~azure.mgmt.machinelearningservices.models.RegistryListCredentialsResult
+    :ivar notebook_access_keys:
+    :vartype notebook_access_keys:
+     ~azure.mgmt.machinelearningservices.models.ListNotebookKeysResult
+    """
+
+    _validation = {
+        'user_storage_key': {'readonly': True},
+        'user_storage_resource_id': {'readonly': True},
+        'app_insights_instrumentation_key': {'readonly': True},
+        'container_registry_credentials': {'readonly': True},
+        'notebook_access_keys': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'user_storage_key': {'key': 'userStorageKey', 'type': 'str'},
+        'user_storage_resource_id': {'key': 'userStorageResourceId', 'type': 'str'},
+        'app_insights_instrumentation_key': {'key': 'appInsightsInstrumentationKey', 'type': 'str'},
+        'container_registry_credentials': {'key': 'containerRegistryCredentials', 'type': 'RegistryListCredentialsResult'},
+        'notebook_access_keys': {'key': 'notebookAccessKeys', 'type': 'ListNotebookKeysResult'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListWorkspaceKeysResult, self).__init__(**kwargs)
+        self.user_storage_key = None
+        self.user_storage_resource_id = None
+        self.app_insights_instrumentation_key = None
+        self.container_registry_credentials = None
+        self.notebook_access_keys = None
+
+
+class ListWorkspaceQuotas(msrest.serialization.Model):
+    """The List WorkspaceQuotasByVMFamily operation response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of Workspace Quotas by VM Family.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ResourceQuota]
+    :ivar next_link: The URI to fetch the next page of workspace quota information by VM Family.
+     Call ListNext() with this to fetch the next page of Workspace Quota information.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[ResourceQuota]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ListWorkspaceQuotas, self).__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class LiteralJobInput(JobInput):
+    """Literal input type.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    :ivar value: Required. [Required] Literal value for the input.
+    :vartype value: str
+    """
+
+    _validation = {
+        'job_input_type': {'required': True},
+        'value': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: str,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: Description for the input.
+        :paramtype description: str
+        :keyword value: Required. [Required] Literal value for the input.
+        :paramtype value: str
+        """
+        super(LiteralJobInput, self).__init__(description=description, **kwargs)
+        self.job_input_type = 'literal'  # type: str
+        self.value = value
+
+
+class ManagedIdentity(IdentityConfiguration):
+    """Managed identity configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar identity_type: Required. [Required] Specifies the type of identity framework.Constant
+     filled by server. Possible values include: "Managed", "AMLToken", "UserIdentity".
+    :vartype identity_type: str or
+     ~azure.mgmt.machinelearningservices.models.IdentityConfigurationType
+    :ivar client_id: Specifies a user-assigned identity by client ID. For system-assigned, do not
+     set this field.
+    :vartype client_id: str
+    :ivar object_id: Specifies a user-assigned identity by object ID. For system-assigned, do not
+     set this field.
+    :vartype object_id: str
+    :ivar resource_id: Specifies a user-assigned identity by ARM resource ID. For system-assigned,
+     do not set this field.
+    :vartype resource_id: str
+    """
+
+    _validation = {
+        'identity_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'identity_type': {'key': 'identityType', 'type': 'str'},
+        'client_id': {'key': 'clientId', 'type': 'str'},
+        'object_id': {'key': 'objectId', 'type': 'str'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        client_id: Optional[str] = None,
+        object_id: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword client_id: Specifies a user-assigned identity by client ID. For system-assigned, do
+         not set this field.
+        :paramtype client_id: str
+        :keyword object_id: Specifies a user-assigned identity by object ID. For system-assigned, do
+         not set this field.
+        :paramtype object_id: str
+        :keyword resource_id: Specifies a user-assigned identity by ARM resource ID. For
+         system-assigned, do not set this field.
+        :paramtype resource_id: str
+        """
+        super(ManagedIdentity, self).__init__(**kwargs)
+        self.identity_type = 'Managed'  # type: str
+        self.client_id = client_id
+        self.object_id = object_id
+        self.resource_id = resource_id
+
+
+class WorkspaceConnectionPropertiesV2(msrest.serialization.Model):
+    """WorkspaceConnectionPropertiesV2.
+
+    You probably want to use the sub-classes and not this class directly. Known
+    sub-classes are: ManagedIdentityAuthTypeWorkspaceConnectionProperties, NoneAuthTypeWorkspaceConnectionProperties, PATAuthTypeWorkspaceConnectionProperties, SASAuthTypeWorkspaceConnectionProperties, UsernamePasswordAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+    }
+
+    _subtype_map = {
+        'auth_type': {'ManagedIdentity': 'ManagedIdentityAuthTypeWorkspaceConnectionProperties', 'None': 'NoneAuthTypeWorkspaceConnectionProperties', 'PAT': 'PATAuthTypeWorkspaceConnectionProperties', 'SAS': 'SASAuthTypeWorkspaceConnectionProperties', 'UsernamePassword': 'UsernamePasswordAuthTypeWorkspaceConnectionProperties'}
+    }
+
+    def __init__(
+        self,
+        *,
+        category: Optional[Union[str, "ConnectionCategory"]] = None,
+        target: Optional[str] = None,
+        value: Optional[str] = None,
+        value_format: Optional[Union[str, "ValueFormat"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        """
+        super(WorkspaceConnectionPropertiesV2, self).__init__(**kwargs)
+        self.auth_type = None  # type: Optional[str]
+        self.category = category
+        self.target = target
+        self.value = value
+        self.value_format = value_format
+
+
+class ManagedIdentityAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """ManagedIdentityAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar credentials:
+    :vartype credentials:
+     ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionManagedIdentity
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+        'credentials': {'key': 'credentials', 'type': 'WorkspaceConnectionManagedIdentity'},
+    }
+
+    def __init__(
+        self,
+        *,
+        category: Optional[Union[str, "ConnectionCategory"]] = None,
+        target: Optional[str] = None,
+        value: Optional[str] = None,
+        value_format: Optional[Union[str, "ValueFormat"]] = None,
+        credentials: Optional["WorkspaceConnectionManagedIdentity"] = None,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword credentials:
+        :paramtype credentials:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionManagedIdentity
+        """
+        super(ManagedIdentityAuthTypeWorkspaceConnectionProperties, self).__init__(category=category, target=target, value=value, value_format=value_format, **kwargs)
+        self.auth_type = 'ManagedIdentity'  # type: str
+        self.credentials = credentials
+
+
+class ManagedOnlineDeployment(OnlineDeploymentProperties):
+    """Properties specific to a ManagedOnlineDeployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar code_configuration: Code configuration for the endpoint deployment.
+    :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
+     endpoint deployment.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables configuration for the deployment.
+    :vartype environment_variables: dict[str, str]
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar app_insights_enabled: If true, enables Application Insights logging.
+    :vartype app_insights_enabled: bool
+    :ivar egress_public_network_access: If Enabled, allow egress public network access. If
+     Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+     "Disabled".
+    :vartype egress_public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+    :ivar endpoint_compute_type: Required. [Required] The compute type of the endpoint.Constant
+     filled by server. Possible values include: "Managed", "Kubernetes", "AzureMLCompute".
+    :vartype endpoint_compute_type: str or
+     ~azure.mgmt.machinelearningservices.models.EndpointComputeType
+    :ivar instance_type: Compute instance type.
+    :vartype instance_type: str
+    :ivar liveness_probe: Liveness probe monitors the health of the container regularly.
+    :vartype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar model: The URI path to the model.
+    :vartype model: str
+    :ivar model_mount_path: The path to mount the model in custom container.
+    :vartype model_mount_path: str
+    :ivar provisioning_state: Provisioning state for the endpoint deployment. Possible values
+     include: "Creating", "Deleting", "Scaling", "Updating", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.DeploymentProvisioningState
+    :ivar readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+     The properties and defaults are the same as liveness probe.
+    :vartype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+    :ivar request_settings: Request settings for the deployment.
+    :vartype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+    :ivar scale_settings: Scale settings for the deployment.
+     If it is null or not provided,
+     it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+     and to DefaultScaleSettings for ManagedOnlineDeployment.
+    :vartype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+    """
+
+    _validation = {
+        'endpoint_compute_type': {'required': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'code_configuration': {'key': 'codeConfiguration', 'type': 'CodeConfiguration'},
+        'description': {'key': 'description', 'type': 'str'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
+        'egress_public_network_access': {'key': 'egressPublicNetworkAccess', 'type': 'str'},
+        'endpoint_compute_type': {'key': 'endpointComputeType', 'type': 'str'},
+        'instance_type': {'key': 'instanceType', 'type': 'str'},
+        'liveness_probe': {'key': 'livenessProbe', 'type': 'ProbeSettings'},
+        'model': {'key': 'model', 'type': 'str'},
+        'model_mount_path': {'key': 'modelMountPath', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'readiness_probe': {'key': 'readinessProbe', 'type': 'ProbeSettings'},
+        'request_settings': {'key': 'requestSettings', 'type': 'OnlineRequestSettings'},
+        'scale_settings': {'key': 'scaleSettings', 'type': 'OnlineScaleSettings'},
+    }
+
+    def __init__(
+        self,
+        *,
+        code_configuration: Optional["CodeConfiguration"] = None,
+        description: Optional[str] = None,
+        environment_id: Optional[str] = None,
+        environment_variables: Optional[Dict[str, str]] = None,
+        properties: Optional[Dict[str, str]] = None,
+        app_insights_enabled: Optional[bool] = False,
+        egress_public_network_access: Optional[Union[str, "EgressPublicNetworkAccessType"]] = None,
+        instance_type: Optional[str] = None,
+        liveness_probe: Optional["ProbeSettings"] = None,
+        model: Optional[str] = None,
+        model_mount_path: Optional[str] = None,
+        readiness_probe: Optional["ProbeSettings"] = None,
+        request_settings: Optional["OnlineRequestSettings"] = None,
+        scale_settings: Optional["OnlineScaleSettings"] = None,
+        **kwargs
+    ):
+        """
+        :keyword code_configuration: Code configuration for the endpoint deployment.
+        :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
+         endpoint deployment.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables configuration for the deployment.
+        :paramtype environment_variables: dict[str, str]
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword app_insights_enabled: If true, enables Application Insights logging.
+        :paramtype app_insights_enabled: bool
+        :keyword egress_public_network_access: If Enabled, allow egress public network access. If
+         Disabled, this will create secure egress. Default: Enabled. Possible values include: "Enabled",
+         "Disabled".
+        :paramtype egress_public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.EgressPublicNetworkAccessType
+        :keyword instance_type: Compute instance type.
+        :paramtype instance_type: str
+        :keyword liveness_probe: Liveness probe monitors the health of the container regularly.
+        :paramtype liveness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword model: The URI path to the model.
+        :paramtype model: str
+        :keyword model_mount_path: The path to mount the model in custom container.
+        :paramtype model_mount_path: str
+        :keyword readiness_probe: Readiness probe validates if the container is ready to serve traffic.
+         The properties and defaults are the same as liveness probe.
+        :paramtype readiness_probe: ~azure.mgmt.machinelearningservices.models.ProbeSettings
+        :keyword request_settings: Request settings for the deployment.
+        :paramtype request_settings: ~azure.mgmt.machinelearningservices.models.OnlineRequestSettings
+        :keyword scale_settings: Scale settings for the deployment.
+         If it is null or not provided,
+         it defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment
+         and to DefaultScaleSettings for ManagedOnlineDeployment.
+        :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.OnlineScaleSettings
+        """
+        super(ManagedOnlineDeployment, self).__init__(code_configuration=code_configuration, description=description, environment_id=environment_id, environment_variables=environment_variables, properties=properties, app_insights_enabled=app_insights_enabled, egress_public_network_access=egress_public_network_access, instance_type=instance_type, liveness_probe=liveness_probe, model=model, model_mount_path=model_mount_path, readiness_probe=readiness_probe, request_settings=request_settings, scale_settings=scale_settings, **kwargs)
+        self.endpoint_compute_type = 'Managed'  # type: str
+
+
+class ManagedServiceIdentity(msrest.serialization.Model):
+    """Managed service identity (system assigned and/or user assigned identities).
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar principal_id: The service principal ID of the system assigned identity. This property
+     will only be provided for a system assigned identity.
+    :vartype principal_id: str
+    :ivar tenant_id: The tenant ID of the system assigned identity. This property will only be
+     provided for a system assigned identity.
+    :vartype tenant_id: str
+    :ivar type: Required. Type of managed service identity (where both SystemAssigned and
+     UserAssigned types are allowed). Possible values include: "None", "SystemAssigned",
+     "UserAssigned", "SystemAssigned,UserAssigned".
+    :vartype type: str or ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentityType
+    :ivar user_assigned_identities: The set of user assigned identities associated with the
+     resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form:
+     '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}.
+     The dictionary values can be empty objects ({}) in requests.
+    :vartype user_assigned_identities: dict[str,
+     ~azure.mgmt.machinelearningservices.models.UserAssignedIdentity]
+    """
+
+    _validation = {
+        'principal_id': {'readonly': True},
+        'tenant_id': {'readonly': True},
+        'type': {'required': True},
+    }
+
+    _attribute_map = {
+        'principal_id': {'key': 'principalId', 'type': 'str'},
+        'tenant_id': {'key': 'tenantId', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'user_assigned_identities': {'key': 'userAssignedIdentities', 'type': '{UserAssignedIdentity}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        type: Union[str, "ManagedServiceIdentityType"],
+        user_assigned_identities: Optional[Dict[str, "UserAssignedIdentity"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword type: Required. Type of managed service identity (where both SystemAssigned and
+         UserAssigned types are allowed). Possible values include: "None", "SystemAssigned",
+         "UserAssigned", "SystemAssigned,UserAssigned".
+        :paramtype type: str or ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentityType
+        :keyword user_assigned_identities: The set of user assigned identities associated with the
+         resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form:
+         '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}.
+         The dictionary values can be empty objects ({}) in requests.
+        :paramtype user_assigned_identities: dict[str,
+         ~azure.mgmt.machinelearningservices.models.UserAssignedIdentity]
+        """
+        super(ManagedServiceIdentity, self).__init__(**kwargs)
+        self.principal_id = None
+        self.tenant_id = None
+        self.type = type
+        self.user_assigned_identities = user_assigned_identities
+
+
+class MedianStoppingPolicy(EarlyTerminationPolicy):
+    """Defines an early termination policy based on running averages of the primary metric of all runs.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar delay_evaluation: Number of intervals by which to delay the first evaluation.
+    :vartype delay_evaluation: int
+    :ivar evaluation_interval: Interval (number of runs) between policy evaluations.
+    :vartype evaluation_interval: int
+    :ivar policy_type: Required. [Required] Name of policy configuration.Constant filled by server.
+     Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
+    :vartype policy_type: str or
+     ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicyType
+    """
+
+    _validation = {
+        'policy_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
+        'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
+        'policy_type': {'key': 'policyType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        delay_evaluation: Optional[int] = 0,
+        evaluation_interval: Optional[int] = 0,
+        **kwargs
+    ):
+        """
+        :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
+        :paramtype delay_evaluation: int
+        :keyword evaluation_interval: Interval (number of runs) between policy evaluations.
+        :paramtype evaluation_interval: int
+        """
+        super(MedianStoppingPolicy, self).__init__(delay_evaluation=delay_evaluation, evaluation_interval=evaluation_interval, **kwargs)
+        self.policy_type = 'MedianStopping'  # type: str
+
+
+class MLFlowModelJobInput(JobInput, AssetJobInput):
+    """MLFlowModelJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        uri: str,
+        mode: Optional[Union[str, "InputDeliveryMode"]] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(MLFlowModelJobInput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_input_type = 'mlflow_model'  # type: str
+        self.description = description
+
+
+class MLFlowModelJobOutput(JobOutput, AssetJobOutput):
+    """MLFlowModelJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        mode: Optional[Union[str, "OutputDeliveryMode"]] = None,
+        uri: Optional[str] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(MLFlowModelJobOutput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_output_type = 'mlflow_model'  # type: str
+        self.description = description
+
+
+class MLTableData(DataVersionBaseProperties):
+    """MLTable data definition.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar data_type: Required. [Required] Specifies the type of data.Constant filled by server.
+     Possible values include: "uri_file", "uri_folder", "mltable".
+    :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+    :ivar data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+    :vartype data_uri: str
+    :ivar referenced_uris: Uris referenced in the MLTable definition (required for lineage).
+    :vartype referenced_uris: list[str]
+    """
+
+    _validation = {
+        'data_type': {'required': True},
+        'data_uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'data_type': {'key': 'dataType', 'type': 'str'},
+        'data_uri': {'key': 'dataUri', 'type': 'str'},
+        'referenced_uris': {'key': 'referencedUris', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        data_uri: str,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_anonymous: Optional[bool] = False,
+        is_archived: Optional[bool] = False,
+        referenced_uris: Optional[List[str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+        :paramtype data_uri: str
+        :keyword referenced_uris: Uris referenced in the MLTable definition (required for lineage).
+        :paramtype referenced_uris: list[str]
+        """
+        super(MLTableData, self).__init__(description=description, properties=properties, tags=tags, is_anonymous=is_anonymous, is_archived=is_archived, data_uri=data_uri, **kwargs)
+        self.data_type = 'mltable'  # type: str
+        self.referenced_uris = referenced_uris
+
+
+class MLTableJobInput(JobInput, AssetJobInput):
+    """MLTableJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        uri: str,
+        mode: Optional[Union[str, "InputDeliveryMode"]] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(MLTableJobInput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_input_type = 'mltable'  # type: str
+        self.description = description
+
+
+class MLTableJobOutput(JobOutput, AssetJobOutput):
+    """MLTableJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        mode: Optional[Union[str, "OutputDeliveryMode"]] = None,
+        uri: Optional[str] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(MLTableJobOutput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_output_type = 'mltable'  # type: str
+        self.description = description
+
+
+class ModelContainer(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ModelContainerProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'ModelContainerProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "ModelContainerProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ModelContainerProperties
+        """
+        super(ModelContainer, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class ModelContainerProperties(AssetContainer):
+    """ModelContainerProperties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    """
+
+    _validation = {
+        'latest_version': {'readonly': True},
+        'next_version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'latest_version': {'key': 'latestVersion', 'type': 'str'},
+        'next_version': {'key': 'nextVersion', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_archived: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        """
+        super(ModelContainerProperties, self).__init__(description=description, properties=properties, tags=tags, is_archived=is_archived, **kwargs)
+
+
+class ModelContainerResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of ModelContainer entities.
+
+    :ivar next_link: The link to the next page of ModelContainer objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type ModelContainer.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ModelContainer]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[ModelContainer]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["ModelContainer"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of ModelContainer objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type ModelContainer.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.ModelContainer]
+        """
+        super(ModelContainerResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class ModelVersion(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ModelVersionProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'ModelVersionProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "ModelVersionProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ModelVersionProperties
+        """
+        super(ModelVersion, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class ModelVersionProperties(AssetBase):
+    """Model asset version details.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar flavors: Mapping of model flavors to their properties.
+    :vartype flavors: dict[str, ~azure.mgmt.machinelearningservices.models.FlavorData]
+    :ivar job_name: Name of the training job which produced this model.
+    :vartype job_name: str
+    :ivar model_type: The storage format for this entity. Used for NCD.
+    :vartype model_type: str
+    :ivar model_uri: The URI path to the model contents.
+    :vartype model_uri: str
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'flavors': {'key': 'flavors', 'type': '{FlavorData}'},
+        'job_name': {'key': 'jobName', 'type': 'str'},
+        'model_type': {'key': 'modelType', 'type': 'str'},
+        'model_uri': {'key': 'modelUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_anonymous: Optional[bool] = False,
+        is_archived: Optional[bool] = False,
+        flavors: Optional[Dict[str, "FlavorData"]] = None,
+        job_name: Optional[str] = None,
+        model_type: Optional[str] = None,
+        model_uri: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword flavors: Mapping of model flavors to their properties.
+        :paramtype flavors: dict[str, ~azure.mgmt.machinelearningservices.models.FlavorData]
+        :keyword job_name: Name of the training job which produced this model.
+        :paramtype job_name: str
+        :keyword model_type: The storage format for this entity. Used for NCD.
+        :paramtype model_type: str
+        :keyword model_uri: The URI path to the model contents.
+        :paramtype model_uri: str
+        """
+        super(ModelVersionProperties, self).__init__(description=description, properties=properties, tags=tags, is_anonymous=is_anonymous, is_archived=is_archived, **kwargs)
+        self.flavors = flavors
+        self.job_name = job_name
+        self.model_type = model_type
+        self.model_uri = model_uri
+
+
+class ModelVersionResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of ModelVersion entities.
+
+    :ivar next_link: The link to the next page of ModelVersion objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type ModelVersion.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ModelVersion]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[ModelVersion]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["ModelVersion"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of ModelVersion objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type ModelVersion.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.ModelVersion]
+        """
+        super(ModelVersionResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class Mpi(DistributionConfiguration):
+    """MPI distribution configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar distribution_type: Required. [Required] Specifies the type of distribution
+     framework.Constant filled by server. Possible values include: "PyTorch", "TensorFlow", "Mpi".
+    :vartype distribution_type: str or ~azure.mgmt.machinelearningservices.models.DistributionType
+    :ivar process_count_per_instance: Number of processes per MPI node.
+    :vartype process_count_per_instance: int
+    """
+
+    _validation = {
+        'distribution_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'distribution_type': {'key': 'distributionType', 'type': 'str'},
+        'process_count_per_instance': {'key': 'processCountPerInstance', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        process_count_per_instance: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword process_count_per_instance: Number of processes per MPI node.
+        :paramtype process_count_per_instance: int
+        """
+        super(Mpi, self).__init__(**kwargs)
+        self.distribution_type = 'Mpi'  # type: str
+        self.process_count_per_instance = process_count_per_instance
+
+
+class NlpVertical(msrest.serialization.Model):
+    """Abstract class for NLP related AutoML tasks.
+NLP - Natural Language Processing.
+
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    """
+
+    _attribute_map = {
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'NlpVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'NlpVerticalLimitSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+    }
+
+    def __init__(
+        self,
+        *,
+        featurization_settings: Optional["NlpVerticalFeaturizationSettings"] = None,
+        limit_settings: Optional["NlpVerticalLimitSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        **kwargs
+    ):
+        """
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        """
+        super(NlpVertical, self).__init__(**kwargs)
+        self.featurization_settings = featurization_settings
+        self.limit_settings = limit_settings
+        self.validation_data = validation_data
+
+
+class NlpVerticalFeaturizationSettings(FeaturizationSettings):
+    """NlpVerticalFeaturizationSettings.
+
+    :ivar dataset_language: Dataset language, useful for the text data.
+    :vartype dataset_language: str
+    """
+
+    _attribute_map = {
+        'dataset_language': {'key': 'datasetLanguage', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        dataset_language: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword dataset_language: Dataset language, useful for the text data.
+        :paramtype dataset_language: str
+        """
+        super(NlpVerticalFeaturizationSettings, self).__init__(dataset_language=dataset_language, **kwargs)
+
+
+class NlpVerticalLimitSettings(msrest.serialization.Model):
+    """Job execution constraints.
+
+    :ivar max_concurrent_trials: Maximum Concurrent AutoML iterations.
+    :vartype max_concurrent_trials: int
+    :ivar max_trials: Number of AutoML iterations.
+    :vartype max_trials: int
+    :ivar timeout: AutoML job timeout.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
+        'max_trials': {'key': 'maxTrials', 'type': 'int'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        max_concurrent_trials: Optional[int] = 1,
+        max_trials: Optional[int] = 1,
+        timeout: Optional[datetime.timedelta] = None,
+        **kwargs
+    ):
+        """
+        :keyword max_concurrent_trials: Maximum Concurrent AutoML iterations.
+        :paramtype max_concurrent_trials: int
+        :keyword max_trials: Number of AutoML iterations.
+        :paramtype max_trials: int
+        :keyword timeout: AutoML job timeout.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(NlpVerticalLimitSettings, self).__init__(**kwargs)
+        self.max_concurrent_trials = max_concurrent_trials
+        self.max_trials = max_trials
+        self.timeout = timeout
+
+
+class NodeStateCounts(msrest.serialization.Model):
+    """Counts of various compute node states on the amlCompute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar idle_node_count: Number of compute nodes in idle state.
+    :vartype idle_node_count: int
+    :ivar running_node_count: Number of compute nodes which are running jobs.
+    :vartype running_node_count: int
+    :ivar preparing_node_count: Number of compute nodes which are being prepared.
+    :vartype preparing_node_count: int
+    :ivar unusable_node_count: Number of compute nodes which are in unusable state.
+    :vartype unusable_node_count: int
+    :ivar leaving_node_count: Number of compute nodes which are leaving the amlCompute.
+    :vartype leaving_node_count: int
+    :ivar preempted_node_count: Number of compute nodes which are in preempted state.
+    :vartype preempted_node_count: int
+    """
+
+    _validation = {
+        'idle_node_count': {'readonly': True},
+        'running_node_count': {'readonly': True},
+        'preparing_node_count': {'readonly': True},
+        'unusable_node_count': {'readonly': True},
+        'leaving_node_count': {'readonly': True},
+        'preempted_node_count': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'idle_node_count': {'key': 'idleNodeCount', 'type': 'int'},
+        'running_node_count': {'key': 'runningNodeCount', 'type': 'int'},
+        'preparing_node_count': {'key': 'preparingNodeCount', 'type': 'int'},
+        'unusable_node_count': {'key': 'unusableNodeCount', 'type': 'int'},
+        'leaving_node_count': {'key': 'leavingNodeCount', 'type': 'int'},
+        'preempted_node_count': {'key': 'preemptedNodeCount', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(NodeStateCounts, self).__init__(**kwargs)
+        self.idle_node_count = None
+        self.running_node_count = None
+        self.preparing_node_count = None
+        self.unusable_node_count = None
+        self.leaving_node_count = None
+        self.preempted_node_count = None
+
+
+class NoneAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """NoneAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        category: Optional[Union[str, "ConnectionCategory"]] = None,
+        target: Optional[str] = None,
+        value: Optional[str] = None,
+        value_format: Optional[Union[str, "ValueFormat"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        """
+        super(NoneAuthTypeWorkspaceConnectionProperties, self).__init__(category=category, target=target, value=value, value_format=value_format, **kwargs)
+        self.auth_type = 'None'  # type: str
+
+
+class NoneDatastoreCredentials(DatastoreCredentials):
+    """Empty/none datastore credentials.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(NoneDatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = 'None'  # type: str
+
+
+class NotebookAccessTokenResult(msrest.serialization.Model):
+    """NotebookAccessTokenResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar notebook_resource_id:
+    :vartype notebook_resource_id: str
+    :ivar host_name:
+    :vartype host_name: str
+    :ivar public_dns:
+    :vartype public_dns: str
+    :ivar access_token:
+    :vartype access_token: str
+    :ivar token_type:
+    :vartype token_type: str
+    :ivar expires_in:
+    :vartype expires_in: int
+    :ivar refresh_token:
+    :vartype refresh_token: str
+    :ivar scope:
+    :vartype scope: str
+    """
+
+    _validation = {
+        'notebook_resource_id': {'readonly': True},
+        'host_name': {'readonly': True},
+        'public_dns': {'readonly': True},
+        'access_token': {'readonly': True},
+        'token_type': {'readonly': True},
+        'expires_in': {'readonly': True},
+        'refresh_token': {'readonly': True},
+        'scope': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'notebook_resource_id': {'key': 'notebookResourceId', 'type': 'str'},
+        'host_name': {'key': 'hostName', 'type': 'str'},
+        'public_dns': {'key': 'publicDns', 'type': 'str'},
+        'access_token': {'key': 'accessToken', 'type': 'str'},
+        'token_type': {'key': 'tokenType', 'type': 'str'},
+        'expires_in': {'key': 'expiresIn', 'type': 'int'},
+        'refresh_token': {'key': 'refreshToken', 'type': 'str'},
+        'scope': {'key': 'scope', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(NotebookAccessTokenResult, self).__init__(**kwargs)
+        self.notebook_resource_id = None
+        self.host_name = None
+        self.public_dns = None
+        self.access_token = None
+        self.token_type = None
+        self.expires_in = None
+        self.refresh_token = None
+        self.scope = None
+
+
+class NotebookPreparationError(msrest.serialization.Model):
+    """NotebookPreparationError.
+
+    :ivar error_message:
+    :vartype error_message: str
+    :ivar status_code:
+    :vartype status_code: int
+    """
+
+    _attribute_map = {
+        'error_message': {'key': 'errorMessage', 'type': 'str'},
+        'status_code': {'key': 'statusCode', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        error_message: Optional[str] = None,
+        status_code: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword error_message:
+        :paramtype error_message: str
+        :keyword status_code:
+        :paramtype status_code: int
+        """
+        super(NotebookPreparationError, self).__init__(**kwargs)
+        self.error_message = error_message
+        self.status_code = status_code
+
+
+class NotebookResourceInfo(msrest.serialization.Model):
+    """NotebookResourceInfo.
+
+    :ivar fqdn:
+    :vartype fqdn: str
+    :ivar resource_id: the data plane resourceId that used to initialize notebook component.
+    :vartype resource_id: str
+    :ivar notebook_preparation_error: The error that occurs when preparing notebook.
+    :vartype notebook_preparation_error:
+     ~azure.mgmt.machinelearningservices.models.NotebookPreparationError
+    """
+
+    _attribute_map = {
+        'fqdn': {'key': 'fqdn', 'type': 'str'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'notebook_preparation_error': {'key': 'notebookPreparationError', 'type': 'NotebookPreparationError'},
+    }
+
+    def __init__(
+        self,
+        *,
+        fqdn: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        notebook_preparation_error: Optional["NotebookPreparationError"] = None,
+        **kwargs
+    ):
+        """
+        :keyword fqdn:
+        :paramtype fqdn: str
+        :keyword resource_id: the data plane resourceId that used to initialize notebook component.
+        :paramtype resource_id: str
+        :keyword notebook_preparation_error: The error that occurs when preparing notebook.
+        :paramtype notebook_preparation_error:
+         ~azure.mgmt.machinelearningservices.models.NotebookPreparationError
+        """
+        super(NotebookResourceInfo, self).__init__(**kwargs)
+        self.fqdn = fqdn
+        self.resource_id = resource_id
+        self.notebook_preparation_error = notebook_preparation_error
+
+
+class Objective(msrest.serialization.Model):
+    """Optimization objective.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar goal: Required. [Required] Defines supported metric goals for hyperparameter tuning.
+     Possible values include: "Minimize", "Maximize".
+    :vartype goal: str or ~azure.mgmt.machinelearningservices.models.Goal
+    :ivar primary_metric: Required. [Required] Name of the metric to optimize.
+    :vartype primary_metric: str
+    """
+
+    _validation = {
+        'goal': {'required': True},
+        'primary_metric': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'goal': {'key': 'goal', 'type': 'str'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        goal: Union[str, "Goal"],
+        primary_metric: str,
+        **kwargs
+    ):
+        """
+        :keyword goal: Required. [Required] Defines supported metric goals for hyperparameter tuning.
+         Possible values include: "Minimize", "Maximize".
+        :paramtype goal: str or ~azure.mgmt.machinelearningservices.models.Goal
+        :keyword primary_metric: Required. [Required] Name of the metric to optimize.
+        :paramtype primary_metric: str
+        """
+        super(Objective, self).__init__(**kwargs)
+        self.goal = goal
+        self.primary_metric = primary_metric
+
+
+class OnlineDeployment(TrackedResource):
+    """OnlineDeployment.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: Required. The geo-location where the resource lives.
+    :vartype location: str
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.OnlineDeploymentProperties
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'location': {'required': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'location': {'key': 'location', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'kind': {'key': 'kind', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': 'OnlineDeploymentProperties'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+    }
+
+    def __init__(
+        self,
+        *,
+        location: str,
+        properties: "OnlineDeploymentProperties",
+        tags: Optional[Dict[str, str]] = None,
+        identity: Optional["ManagedServiceIdentity"] = None,
+        kind: Optional[str] = None,
+        sku: Optional["Sku"] = None,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: Required. The geo-location where the resource lives.
+        :paramtype location: str
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.OnlineDeploymentProperties
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super(OnlineDeployment, self).__init__(tags=tags, location=location, **kwargs)
+        self.identity = identity
+        self.kind = kind
+        self.properties = properties
+        self.sku = sku
+
+
+class OnlineDeploymentTrackedResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of OnlineDeployment entities.
+
+    :ivar next_link: The link to the next page of OnlineDeployment objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type OnlineDeployment.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.OnlineDeployment]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[OnlineDeployment]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["OnlineDeployment"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of OnlineDeployment objects. If null, there are
+         no additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type OnlineDeployment.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.OnlineDeployment]
+        """
+        super(OnlineDeploymentTrackedResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class OnlineEndpoint(TrackedResource):
+    """OnlineEndpoint.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: Required. The geo-location where the resource lives.
+    :vartype location: str
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.OnlineEndpointProperties
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'location': {'required': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'location': {'key': 'location', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'kind': {'key': 'kind', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': 'OnlineEndpointProperties'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+    }
+
+    def __init__(
+        self,
+        *,
+        location: str,
+        properties: "OnlineEndpointProperties",
+        tags: Optional[Dict[str, str]] = None,
+        identity: Optional["ManagedServiceIdentity"] = None,
+        kind: Optional[str] = None,
+        sku: Optional["Sku"] = None,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: Required. The geo-location where the resource lives.
+        :paramtype location: str
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.OnlineEndpointProperties
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super(OnlineEndpoint, self).__init__(tags=tags, location=location, **kwargs)
+        self.identity = identity
+        self.kind = kind
+        self.properties = properties
+        self.sku = sku
+
+
+class OnlineEndpointProperties(EndpointPropertiesBase):
+    """Online endpoint configuration.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken' for
+     Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
+     Possible values include: "AMLToken", "Key", "AADToken".
+    :vartype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+    :ivar description: Description of the inference endpoint.
+    :vartype description: str
+    :ivar keys: EndpointAuthKeys to set initially on an Endpoint.
+     This property will always be returned as null. AuthKey values must be retrieved using the
+     ListKeys API.
+    :vartype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+    :ivar properties: Property dictionary. Properties can be added, but not removed or altered.
+    :vartype properties: dict[str, str]
+    :ivar scoring_uri: Endpoint URI.
+    :vartype scoring_uri: str
+    :ivar swagger_uri: Endpoint Swagger URI.
+    :vartype swagger_uri: str
+    :ivar compute: ARM resource ID of the compute if it exists.
+     optional.
+    :vartype compute: str
+    :ivar provisioning_state: Provisioning state for the endpoint. Possible values include:
+     "Creating", "Deleting", "Succeeded", "Failed", "Updating", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.EndpointProvisioningState
+    :ivar public_network_access: Set to "Enabled" for endpoints that should allow public access
+     when Private Link is enabled. Possible values include: "Enabled", "Disabled".
+    :vartype public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.PublicNetworkAccessType
+    :ivar traffic: Percentage of traffic from endpoint to divert to each deployment. Traffic values
+     need to sum to 100.
+    :vartype traffic: dict[str, int]
+    """
+
+    _validation = {
+        'auth_mode': {'required': True},
+        'scoring_uri': {'readonly': True},
+        'swagger_uri': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'auth_mode': {'key': 'authMode', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'keys': {'key': 'keys', 'type': 'EndpointAuthKeys'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'scoring_uri': {'key': 'scoringUri', 'type': 'str'},
+        'swagger_uri': {'key': 'swaggerUri', 'type': 'str'},
+        'compute': {'key': 'compute', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'public_network_access': {'key': 'publicNetworkAccess', 'type': 'str'},
+        'traffic': {'key': 'traffic', 'type': '{int}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        auth_mode: Union[str, "EndpointAuthMode"],
+        description: Optional[str] = None,
+        keys: Optional["EndpointAuthKeys"] = None,
+        properties: Optional[Dict[str, str]] = None,
+        compute: Optional[str] = None,
+        public_network_access: Optional[Union[str, "PublicNetworkAccessType"]] = None,
+        traffic: Optional[Dict[str, int]] = None,
+        **kwargs
+    ):
+        """
+        :keyword auth_mode: Required. [Required] Use 'Key' for key based authentication and 'AMLToken'
+         for Azure Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken'
+         does. Possible values include: "AMLToken", "Key", "AADToken".
+        :paramtype auth_mode: str or ~azure.mgmt.machinelearningservices.models.EndpointAuthMode
+        :keyword description: Description of the inference endpoint.
+        :paramtype description: str
+        :keyword keys: EndpointAuthKeys to set initially on an Endpoint.
+         This property will always be returned as null. AuthKey values must be retrieved using the
+         ListKeys API.
+        :paramtype keys: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+        :keyword properties: Property dictionary. Properties can be added, but not removed or altered.
+        :paramtype properties: dict[str, str]
+        :keyword compute: ARM resource ID of the compute if it exists.
+         optional.
+        :paramtype compute: str
+        :keyword public_network_access: Set to "Enabled" for endpoints that should allow public access
+         when Private Link is enabled. Possible values include: "Enabled", "Disabled".
+        :paramtype public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.PublicNetworkAccessType
+        :keyword traffic: Percentage of traffic from endpoint to divert to each deployment. Traffic
+         values need to sum to 100.
+        :paramtype traffic: dict[str, int]
+        """
+        super(OnlineEndpointProperties, self).__init__(auth_mode=auth_mode, description=description, keys=keys, properties=properties, **kwargs)
+        self.compute = compute
+        self.provisioning_state = None
+        self.public_network_access = public_network_access
+        self.traffic = traffic
+
+
+class OnlineEndpointTrackedResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of OnlineEndpoint entities.
+
+    :ivar next_link: The link to the next page of OnlineEndpoint objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type OnlineEndpoint.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.OnlineEndpoint]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[OnlineEndpoint]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["OnlineEndpoint"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of OnlineEndpoint objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type OnlineEndpoint.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.OnlineEndpoint]
+        """
+        super(OnlineEndpointTrackedResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class OnlineRequestSettings(msrest.serialization.Model):
+    """Online deployment scoring requests configuration.
+
+    :ivar max_concurrent_requests_per_instance: The number of maximum concurrent requests per node
+     allowed per deployment. Defaults to 1.
+    :vartype max_concurrent_requests_per_instance: int
+    :ivar max_queue_wait: The maximum amount of time a request will stay in the queue in ISO 8601
+     format.
+     Defaults to 500ms.
+    :vartype max_queue_wait: ~datetime.timedelta
+    :ivar request_timeout: The scoring timeout in ISO 8601 format.
+     Defaults to 5000ms.
+    :vartype request_timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'max_concurrent_requests_per_instance': {'key': 'maxConcurrentRequestsPerInstance', 'type': 'int'},
+        'max_queue_wait': {'key': 'maxQueueWait', 'type': 'duration'},
+        'request_timeout': {'key': 'requestTimeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        max_concurrent_requests_per_instance: Optional[int] = 1,
+        max_queue_wait: Optional[datetime.timedelta] = "PT0.5S",
+        request_timeout: Optional[datetime.timedelta] = "PT5S",
+        **kwargs
+    ):
+        """
+        :keyword max_concurrent_requests_per_instance: The number of maximum concurrent requests per
+         node allowed per deployment. Defaults to 1.
+        :paramtype max_concurrent_requests_per_instance: int
+        :keyword max_queue_wait: The maximum amount of time a request will stay in the queue in ISO
+         8601 format.
+         Defaults to 500ms.
+        :paramtype max_queue_wait: ~datetime.timedelta
+        :keyword request_timeout: The scoring timeout in ISO 8601 format.
+         Defaults to 5000ms.
+        :paramtype request_timeout: ~datetime.timedelta
+        """
+        super(OnlineRequestSettings, self).__init__(**kwargs)
+        self.max_concurrent_requests_per_instance = max_concurrent_requests_per_instance
+        self.max_queue_wait = max_queue_wait
+        self.request_timeout = request_timeout
+
+
+class OutputPathAssetReference(AssetReferenceBase):
+    """Reference to an asset via its path in a job output.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar reference_type: Required. [Required] Specifies the type of asset reference.Constant
+     filled by server. Possible values include: "Id", "DataPath", "OutputPath".
+    :vartype reference_type: str or ~azure.mgmt.machinelearningservices.models.ReferenceType
+    :ivar job_id: ARM resource ID of the job.
+    :vartype job_id: str
+    :ivar path: The path of the file/directory in the job output.
+    :vartype path: str
+    """
+
+    _validation = {
+        'reference_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'reference_type': {'key': 'referenceType', 'type': 'str'},
+        'job_id': {'key': 'jobId', 'type': 'str'},
+        'path': {'key': 'path', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        job_id: Optional[str] = None,
+        path: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword job_id: ARM resource ID of the job.
+        :paramtype job_id: str
+        :keyword path: The path of the file/directory in the job output.
+        :paramtype path: str
+        """
+        super(OutputPathAssetReference, self).__init__(**kwargs)
+        self.reference_type = 'OutputPath'  # type: str
+        self.job_id = job_id
+        self.path = path
+
+
+class PaginatedComputeResourcesList(msrest.serialization.Model):
+    """Paginated list of Machine Learning compute objects wrapped in ARM resource envelope.
+
+    :ivar value: An array of Machine Learning compute objects wrapped in ARM resource envelope.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ComputeResource]
+    :ivar next_link: A continuation link (absolute URI) to the next page of results in the list.
+    :vartype next_link: str
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[ComputeResource]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional[List["ComputeResource"]] = None,
+        next_link: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword value: An array of Machine Learning compute objects wrapped in ARM resource envelope.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.ComputeResource]
+        :keyword next_link: A continuation link (absolute URI) to the next page of results in the list.
+        :paramtype next_link: str
+        """
+        super(PaginatedComputeResourcesList, self).__init__(**kwargs)
+        self.value = value
+        self.next_link = next_link
+
+
+class PartialBatchDeployment(msrest.serialization.Model):
+    """Mutable batch inference settings per deployment.
+
+    :ivar description: Description of the endpoint deployment.
+    :vartype description: str
+    """
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: Description of the endpoint deployment.
+        :paramtype description: str
+        """
+        super(PartialBatchDeployment, self).__init__(**kwargs)
+        self.description = description
+
+
+class PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties(msrest.serialization.Model):
+    """Strictly used in update requests.
+
+    :ivar properties: Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.PartialBatchDeployment
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'PartialBatchDeployment'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["PartialBatchDeployment"] = None,
+        tags: Optional[Dict[str, str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties: Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.PartialBatchDeployment
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        """
+        super(PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties, self).__init__(**kwargs)
+        self.properties = properties
+        self.tags = tags
+
+
+class PartialManagedServiceIdentity(msrest.serialization.Model):
+    """Managed service identity (system assigned and/or user assigned identities).
+
+    :ivar type: Managed service identity (system assigned and/or user assigned identities).
+     Possible values include: "None", "SystemAssigned", "UserAssigned",
+     "SystemAssigned,UserAssigned".
+    :vartype type: str or ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentityType
+    :ivar user_assigned_identities: The set of user assigned identities associated with the
+     resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form:
+     '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}.
+     The dictionary values can be empty objects ({}) in requests.
+    :vartype user_assigned_identities: dict[str, any]
+    """
+
+    _attribute_map = {
+        'type': {'key': 'type', 'type': 'str'},
+        'user_assigned_identities': {'key': 'userAssignedIdentities', 'type': '{object}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        type: Optional[Union[str, "ManagedServiceIdentityType"]] = None,
+        user_assigned_identities: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ):
+        """
+        :keyword type: Managed service identity (system assigned and/or user assigned identities).
+         Possible values include: "None", "SystemAssigned", "UserAssigned",
+         "SystemAssigned,UserAssigned".
+        :paramtype type: str or ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentityType
+        :keyword user_assigned_identities: The set of user assigned identities associated with the
+         resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form:
+         '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}.
+         The dictionary values can be empty objects ({}) in requests.
+        :paramtype user_assigned_identities: dict[str, any]
+        """
+        super(PartialManagedServiceIdentity, self).__init__(**kwargs)
+        self.type = type
+        self.user_assigned_identities = user_assigned_identities
+
+
+class PartialMinimalTrackedResource(msrest.serialization.Model):
+    """Strictly used in update requests.
+
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    """
+
+    _attribute_map = {
+        'tags': {'key': 'tags', 'type': '{str}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        tags: Optional[Dict[str, str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        """
+        super(PartialMinimalTrackedResource, self).__init__(**kwargs)
+        self.tags = tags
+
+
+class PartialMinimalTrackedResourceWithIdentity(PartialMinimalTrackedResource):
+    """Strictly used in update requests.
+
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.PartialManagedServiceIdentity
+    """
+
+    _attribute_map = {
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'identity': {'key': 'identity', 'type': 'PartialManagedServiceIdentity'},
+    }
+
+    def __init__(
+        self,
+        *,
+        tags: Optional[Dict[str, str]] = None,
+        identity: Optional["PartialManagedServiceIdentity"] = None,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.PartialManagedServiceIdentity
+        """
+        super(PartialMinimalTrackedResourceWithIdentity, self).__init__(tags=tags, **kwargs)
+        self.identity = identity
+
+
+class PartialMinimalTrackedResourceWithSku(PartialMinimalTrackedResource):
+    """Strictly used in update requests.
+
+    :ivar tags: A set of tags. Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.PartialSku
+    """
+
+    _attribute_map = {
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'PartialSku'},
+    }
+
+    def __init__(
+        self,
+        *,
+        tags: Optional[Dict[str, str]] = None,
+        sku: Optional["PartialSku"] = None,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.PartialSku
+        """
+        super(PartialMinimalTrackedResourceWithSku, self).__init__(tags=tags, **kwargs)
+        self.sku = sku
+
+
+class PartialSku(msrest.serialization.Model):
+    """Common SKU definition.
+
+    :ivar capacity: If the SKU supports scale out/in then the capacity integer should be included.
+     If scale out/in is not possible for the resource this may be omitted.
+    :vartype capacity: int
+    :ivar family: If the service has different generations of hardware, for the same SKU, then that
+     can be captured here.
+    :vartype family: str
+    :ivar name: The name of the SKU. Ex - P3. It is typically a letter+number code.
+    :vartype name: str
+    :ivar size: The SKU size. When the name field is the combination of tier and some other value,
+     this would be the standalone code.
+    :vartype size: str
+    :ivar tier: This field is required to be implemented by the Resource Provider if the service
+     has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+     "Standard", "Premium".
+    :vartype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+    """
+
+    _attribute_map = {
+        'capacity': {'key': 'capacity', 'type': 'int'},
+        'family': {'key': 'family', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'size': {'key': 'size', 'type': 'str'},
+        'tier': {'key': 'tier', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        capacity: Optional[int] = None,
+        family: Optional[str] = None,
+        name: Optional[str] = None,
+        size: Optional[str] = None,
+        tier: Optional[Union[str, "SkuTier"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword capacity: If the SKU supports scale out/in then the capacity integer should be
+         included. If scale out/in is not possible for the resource this may be omitted.
+        :paramtype capacity: int
+        :keyword family: If the service has different generations of hardware, for the same SKU, then
+         that can be captured here.
+        :paramtype family: str
+        :keyword name: The name of the SKU. Ex - P3. It is typically a letter+number code.
+        :paramtype name: str
+        :keyword size: The SKU size. When the name field is the combination of tier and some other
+         value, this would be the standalone code.
+        :paramtype size: str
+        :keyword tier: This field is required to be implemented by the Resource Provider if the service
+         has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+         "Standard", "Premium".
+        :paramtype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+        """
+        super(PartialSku, self).__init__(**kwargs)
+        self.capacity = capacity
+        self.family = family
+        self.name = name
+        self.size = size
+        self.tier = tier
+
+
+class Password(msrest.serialization.Model):
+    """Password.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar name:
+    :vartype name: str
+    :ivar value:
+    :vartype value: str
+    """
+
+    _validation = {
+        'name': {'readonly': True},
+        'value': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(Password, self).__init__(**kwargs)
+        self.name = None
+        self.value = None
+
+
+class PATAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """PATAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar credentials:
+    :vartype credentials:
+     ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPersonalAccessToken
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+        'credentials': {'key': 'credentials', 'type': 'WorkspaceConnectionPersonalAccessToken'},
+    }
+
+    def __init__(
+        self,
+        *,
+        category: Optional[Union[str, "ConnectionCategory"]] = None,
+        target: Optional[str] = None,
+        value: Optional[str] = None,
+        value_format: Optional[Union[str, "ValueFormat"]] = None,
+        credentials: Optional["WorkspaceConnectionPersonalAccessToken"] = None,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword credentials:
+        :paramtype credentials:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPersonalAccessToken
+        """
+        super(PATAuthTypeWorkspaceConnectionProperties, self).__init__(category=category, target=target, value=value, value_format=value_format, **kwargs)
+        self.auth_type = 'PAT'  # type: str
+        self.credentials = credentials
+
+
+class PersonalComputeInstanceSettings(msrest.serialization.Model):
+    """Settings for a personal compute instance.
+
+    :ivar assigned_user: A user explicitly assigned to a personal compute instance.
+    :vartype assigned_user: ~azure.mgmt.machinelearningservices.models.AssignedUser
+    """
+
+    _attribute_map = {
+        'assigned_user': {'key': 'assignedUser', 'type': 'AssignedUser'},
+    }
+
+    def __init__(
+        self,
+        *,
+        assigned_user: Optional["AssignedUser"] = None,
+        **kwargs
+    ):
+        """
+        :keyword assigned_user: A user explicitly assigned to a personal compute instance.
+        :paramtype assigned_user: ~azure.mgmt.machinelearningservices.models.AssignedUser
+        """
+        super(PersonalComputeInstanceSettings, self).__init__(**kwargs)
+        self.assigned_user = assigned_user
+
+
+class PipelineJob(JobBaseProperties):
+    """Pipeline Job definition: defines generic to MFE attributes.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "AutoML", "Command", "Sweep", "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Possible values include: "NotStarted", "Starting",
+     "Provisioning", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
+     "Failed", "Canceled", "NotResponding", "Paused", "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar inputs: Inputs for the pipeline job.
+    :vartype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+    :ivar jobs: Jobs construct the Pipeline Job.
+    :vartype jobs: dict[str, any]
+    :ivar outputs: Outputs for the pipeline job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar settings: Pipeline settings, for things like ContinueRunOnStepFailure etc.
+    :vartype settings: any
+    :ivar source_job_id: ARM resource ID of source job.
+    :vartype source_job_id: str
+    """
+
+    _validation = {
+        'job_type': {'required': True},
+        'status': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'component_id': {'key': 'componentId', 'type': 'str'},
+        'compute_id': {'key': 'computeId', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'experiment_name': {'key': 'experimentName', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'job_type': {'key': 'jobType', 'type': 'str'},
+        'services': {'key': 'services', 'type': '{JobService}'},
+        'status': {'key': 'status', 'type': 'str'},
+        'inputs': {'key': 'inputs', 'type': '{JobInput}'},
+        'jobs': {'key': 'jobs', 'type': '{object}'},
+        'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
+        'settings': {'key': 'settings', 'type': 'object'},
+        'source_job_id': {'key': 'sourceJobId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        component_id: Optional[str] = None,
+        compute_id: Optional[str] = None,
+        display_name: Optional[str] = None,
+        experiment_name: Optional[str] = "Default",
+        identity: Optional["IdentityConfiguration"] = None,
+        is_archived: Optional[bool] = False,
+        services: Optional[Dict[str, "JobService"]] = None,
+        inputs: Optional[Dict[str, "JobInput"]] = None,
+        jobs: Optional[Dict[str, Any]] = None,
+        outputs: Optional[Dict[str, "JobOutput"]] = None,
+        settings: Optional[Any] = None,
+        source_job_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword inputs: Inputs for the pipeline job.
+        :paramtype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+        :keyword jobs: Jobs construct the Pipeline Job.
+        :paramtype jobs: dict[str, any]
+        :keyword outputs: Outputs for the pipeline job.
+        :paramtype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+        :keyword settings: Pipeline settings, for things like ContinueRunOnStepFailure etc.
+        :paramtype settings: any
+        :keyword source_job_id: ARM resource ID of source job.
+        :paramtype source_job_id: str
+        """
+        super(PipelineJob, self).__init__(description=description, properties=properties, tags=tags, component_id=component_id, compute_id=compute_id, display_name=display_name, experiment_name=experiment_name, identity=identity, is_archived=is_archived, services=services, **kwargs)
+        self.job_type = 'Pipeline'  # type: str
+        self.inputs = inputs
+        self.jobs = jobs
+        self.outputs = outputs
+        self.settings = settings
+        self.source_job_id = source_job_id
+
+
+class PrivateEndpoint(msrest.serialization.Model):
+    """The Private Endpoint resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: The ARM identifier for Private Endpoint.
+    :vartype id: str
+    :ivar subnet_arm_id: The ARM identifier for Subnet resource that private endpoint links to.
+    :vartype subnet_arm_id: str
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'subnet_arm_id': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'subnet_arm_id': {'key': 'subnetArmId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(PrivateEndpoint, self).__init__(**kwargs)
+        self.id = None
+        self.subnet_arm_id = None
+
+
+class PrivateEndpointConnection(Resource):
+    """The Private Endpoint Connection resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar identity: The identity of the resource.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar location: Specifies the location of the resource.
+    :vartype location: str
+    :ivar tags: A set of tags. Contains resource tags defined as key/value pairs.
+    :vartype tags: dict[str, str]
+    :ivar sku: The sku of the workspace.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    :ivar private_endpoint: The resource of private end point.
+    :vartype private_endpoint: ~azure.mgmt.machinelearningservices.models.PrivateEndpoint
+    :ivar private_link_service_connection_state: A collection of information about the state of the
+     connection between service consumer and provider.
+    :vartype private_link_service_connection_state:
+     ~azure.mgmt.machinelearningservices.models.PrivateLinkServiceConnectionState
+    :ivar provisioning_state: The provisioning state of the private endpoint connection resource.
+     Possible values include: "Succeeded", "Creating", "Deleting", "Failed".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.PrivateEndpointConnectionProvisioningState
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'location': {'key': 'location', 'type': 'str'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+        'private_endpoint': {'key': 'properties.privateEndpoint', 'type': 'PrivateEndpoint'},
+        'private_link_service_connection_state': {'key': 'properties.privateLinkServiceConnectionState', 'type': 'PrivateLinkServiceConnectionState'},
+        'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        identity: Optional["ManagedServiceIdentity"] = None,
+        location: Optional[str] = None,
+        tags: Optional[Dict[str, str]] = None,
+        sku: Optional["Sku"] = None,
+        private_endpoint: Optional["PrivateEndpoint"] = None,
+        private_link_service_connection_state: Optional["PrivateLinkServiceConnectionState"] = None,
+        **kwargs
+    ):
+        """
+        :keyword identity: The identity of the resource.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword location: Specifies the location of the resource.
+        :paramtype location: str
+        :keyword tags: A set of tags. Contains resource tags defined as key/value pairs.
+        :paramtype tags: dict[str, str]
+        :keyword sku: The sku of the workspace.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        :keyword private_endpoint: The resource of private end point.
+        :paramtype private_endpoint: ~azure.mgmt.machinelearningservices.models.PrivateEndpoint
+        :keyword private_link_service_connection_state: A collection of information about the state of
+         the connection between service consumer and provider.
+        :paramtype private_link_service_connection_state:
+         ~azure.mgmt.machinelearningservices.models.PrivateLinkServiceConnectionState
+        """
+        super(PrivateEndpointConnection, self).__init__(**kwargs)
+        self.identity = identity
+        self.location = location
+        self.tags = tags
+        self.sku = sku
+        self.private_endpoint = private_endpoint
+        self.private_link_service_connection_state = private_link_service_connection_state
+        self.provisioning_state = None
+
+
+class PrivateEndpointConnectionListResult(msrest.serialization.Model):
+    """List of private endpoint connection associated with the specified workspace.
+
+    :ivar value: Array of private endpoint connections.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection]
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[PrivateEndpointConnection]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional[List["PrivateEndpointConnection"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword value: Array of private endpoint connections.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection]
+        """
+        super(PrivateEndpointConnectionListResult, self).__init__(**kwargs)
+        self.value = value
+
+
+class PrivateLinkResource(Resource):
+    """A private link resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar identity: The identity of the resource.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar location: Specifies the location of the resource.
+    :vartype location: str
+    :ivar tags: A set of tags. Contains resource tags defined as key/value pairs.
+    :vartype tags: dict[str, str]
+    :ivar sku: The sku of the workspace.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    :ivar group_id: The private link resource group id.
+    :vartype group_id: str
+    :ivar required_members: The private link resource required member names.
+    :vartype required_members: list[str]
+    :ivar required_zone_names: The private link resource Private link DNS zone name.
+    :vartype required_zone_names: list[str]
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'group_id': {'readonly': True},
+        'required_members': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'location': {'key': 'location', 'type': 'str'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+        'group_id': {'key': 'properties.groupId', 'type': 'str'},
+        'required_members': {'key': 'properties.requiredMembers', 'type': '[str]'},
+        'required_zone_names': {'key': 'properties.requiredZoneNames', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        identity: Optional["ManagedServiceIdentity"] = None,
+        location: Optional[str] = None,
+        tags: Optional[Dict[str, str]] = None,
+        sku: Optional["Sku"] = None,
+        required_zone_names: Optional[List[str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword identity: The identity of the resource.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword location: Specifies the location of the resource.
+        :paramtype location: str
+        :keyword tags: A set of tags. Contains resource tags defined as key/value pairs.
+        :paramtype tags: dict[str, str]
+        :keyword sku: The sku of the workspace.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        :keyword required_zone_names: The private link resource Private link DNS zone name.
+        :paramtype required_zone_names: list[str]
+        """
+        super(PrivateLinkResource, self).__init__(**kwargs)
+        self.identity = identity
+        self.location = location
+        self.tags = tags
+        self.sku = sku
+        self.group_id = None
+        self.required_members = None
+        self.required_zone_names = required_zone_names
+
+
+class PrivateLinkResourceListResult(msrest.serialization.Model):
+    """A list of private link resources.
+
+    :ivar value: Array of private link resources.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.PrivateLinkResource]
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[PrivateLinkResource]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional[List["PrivateLinkResource"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword value: Array of private link resources.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.PrivateLinkResource]
+        """
+        super(PrivateLinkResourceListResult, self).__init__(**kwargs)
+        self.value = value
+
+
+class PrivateLinkServiceConnectionState(msrest.serialization.Model):
+    """A collection of information about the state of the connection between service consumer and provider.
+
+    :ivar status: Indicates whether the connection has been Approved/Rejected/Removed by the owner
+     of the service. Possible values include: "Pending", "Approved", "Rejected", "Disconnected",
+     "Timeout".
+    :vartype status: str or
+     ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+    :ivar description: The reason for approval/rejection of the connection.
+    :vartype description: str
+    :ivar actions_required: A message indicating if changes on the service provider require any
+     updates on the consumer.
+    :vartype actions_required: str
+    """
+
+    _attribute_map = {
+        'status': {'key': 'status', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'actions_required': {'key': 'actionsRequired', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        status: Optional[Union[str, "PrivateEndpointServiceConnectionStatus"]] = None,
+        description: Optional[str] = None,
+        actions_required: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword status: Indicates whether the connection has been Approved/Rejected/Removed by the
+         owner of the service. Possible values include: "Pending", "Approved", "Rejected",
+         "Disconnected", "Timeout".
+        :paramtype status: str or
+         ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+        :keyword description: The reason for approval/rejection of the connection.
+        :paramtype description: str
+        :keyword actions_required: A message indicating if changes on the service provider require any
+         updates on the consumer.
+        :paramtype actions_required: str
+        """
+        super(PrivateLinkServiceConnectionState, self).__init__(**kwargs)
+        self.status = status
+        self.description = description
+        self.actions_required = actions_required
+
+
+class ProbeSettings(msrest.serialization.Model):
+    """Deployment container liveness/readiness probe configuration.
+
+    :ivar failure_threshold: The number of failures to allow before returning an unhealthy status.
+    :vartype failure_threshold: int
+    :ivar initial_delay: The delay before the first probe in ISO 8601 format.
+    :vartype initial_delay: ~datetime.timedelta
+    :ivar period: The length of time between probes in ISO 8601 format.
+    :vartype period: ~datetime.timedelta
+    :ivar success_threshold: The number of successful probes before returning a healthy status.
+    :vartype success_threshold: int
+    :ivar timeout: The probe timeout in ISO 8601 format.
+    :vartype timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'failure_threshold': {'key': 'failureThreshold', 'type': 'int'},
+        'initial_delay': {'key': 'initialDelay', 'type': 'duration'},
+        'period': {'key': 'period', 'type': 'duration'},
+        'success_threshold': {'key': 'successThreshold', 'type': 'int'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        failure_threshold: Optional[int] = 30,
+        initial_delay: Optional[datetime.timedelta] = None,
+        period: Optional[datetime.timedelta] = "PT10S",
+        success_threshold: Optional[int] = 1,
+        timeout: Optional[datetime.timedelta] = "PT2S",
+        **kwargs
+    ):
+        """
+        :keyword failure_threshold: The number of failures to allow before returning an unhealthy
+         status.
+        :paramtype failure_threshold: int
+        :keyword initial_delay: The delay before the first probe in ISO 8601 format.
+        :paramtype initial_delay: ~datetime.timedelta
+        :keyword period: The length of time between probes in ISO 8601 format.
+        :paramtype period: ~datetime.timedelta
+        :keyword success_threshold: The number of successful probes before returning a healthy status.
+        :paramtype success_threshold: int
+        :keyword timeout: The probe timeout in ISO 8601 format.
+        :paramtype timeout: ~datetime.timedelta
+        """
+        super(ProbeSettings, self).__init__(**kwargs)
+        self.failure_threshold = failure_threshold
+        self.initial_delay = initial_delay
+        self.period = period
+        self.success_threshold = success_threshold
+        self.timeout = timeout
+
+
+class PyTorch(DistributionConfiguration):
+    """PyTorch distribution configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar distribution_type: Required. [Required] Specifies the type of distribution
+     framework.Constant filled by server. Possible values include: "PyTorch", "TensorFlow", "Mpi".
+    :vartype distribution_type: str or ~azure.mgmt.machinelearningservices.models.DistributionType
+    :ivar process_count_per_instance: Number of processes per node.
+    :vartype process_count_per_instance: int
+    """
+
+    _validation = {
+        'distribution_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'distribution_type': {'key': 'distributionType', 'type': 'str'},
+        'process_count_per_instance': {'key': 'processCountPerInstance', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        process_count_per_instance: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword process_count_per_instance: Number of processes per node.
+        :paramtype process_count_per_instance: int
+        """
+        super(PyTorch, self).__init__(**kwargs)
+        self.distribution_type = 'PyTorch'  # type: str
+        self.process_count_per_instance = process_count_per_instance
+
+
+class QuotaBaseProperties(msrest.serialization.Model):
+    """The properties for Quota update or retrieval.
+
+    :ivar id: Specifies the resource ID.
+    :vartype id: str
+    :ivar type: Specifies the resource type.
+    :vartype type: str
+    :ivar limit: The maximum permitted quota of the resource.
+    :vartype limit: long
+    :ivar unit: An enum describing the unit of quota measurement. Possible values include: "Count".
+    :vartype unit: str or ~azure.mgmt.machinelearningservices.models.QuotaUnit
+    """
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'limit': {'key': 'limit', 'type': 'long'},
+        'unit': {'key': 'unit', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        id: Optional[str] = None,
+        type: Optional[str] = None,
+        limit: Optional[int] = None,
+        unit: Optional[Union[str, "QuotaUnit"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword id: Specifies the resource ID.
+        :paramtype id: str
+        :keyword type: Specifies the resource type.
+        :paramtype type: str
+        :keyword limit: The maximum permitted quota of the resource.
+        :paramtype limit: long
+        :keyword unit: An enum describing the unit of quota measurement. Possible values include:
+         "Count".
+        :paramtype unit: str or ~azure.mgmt.machinelearningservices.models.QuotaUnit
+        """
+        super(QuotaBaseProperties, self).__init__(**kwargs)
+        self.id = id
+        self.type = type
+        self.limit = limit
+        self.unit = unit
+
+
+class QuotaUpdateParameters(msrest.serialization.Model):
+    """Quota update parameters.
+
+    :ivar value: The list for update quota.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.QuotaBaseProperties]
+    :ivar location: Region of workspace quota to be updated.
+    :vartype location: str
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[QuotaBaseProperties]'},
+        'location': {'key': 'location', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional[List["QuotaBaseProperties"]] = None,
+        location: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword value: The list for update quota.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.QuotaBaseProperties]
+        :keyword location: Region of workspace quota to be updated.
+        :paramtype location: str
+        """
+        super(QuotaUpdateParameters, self).__init__(**kwargs)
+        self.value = value
+        self.location = location
+
+
+class RandomSamplingAlgorithm(SamplingAlgorithm):
+    """Defines a Sampling Algorithm that generates values randomly.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar sampling_algorithm_type: Required. [Required] The algorithm used for generating
+     hyperparameter values, along with configuration properties.Constant filled by server. Possible
+     values include: "Grid", "Random", "Bayesian".
+    :vartype sampling_algorithm_type: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    :ivar rule: The specific type of random algorithm. Possible values include: "Random", "Sobol".
+    :vartype rule: str or ~azure.mgmt.machinelearningservices.models.RandomSamplingAlgorithmRule
+    :ivar seed: An optional integer to use as the seed for random number generation.
+    :vartype seed: int
+    """
+
+    _validation = {
+        'sampling_algorithm_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'sampling_algorithm_type': {'key': 'samplingAlgorithmType', 'type': 'str'},
+        'rule': {'key': 'rule', 'type': 'str'},
+        'seed': {'key': 'seed', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        rule: Optional[Union[str, "RandomSamplingAlgorithmRule"]] = None,
+        seed: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword rule: The specific type of random algorithm. Possible values include: "Random",
+         "Sobol".
+        :paramtype rule: str or ~azure.mgmt.machinelearningservices.models.RandomSamplingAlgorithmRule
+        :keyword seed: An optional integer to use as the seed for random number generation.
+        :paramtype seed: int
+        """
+        super(RandomSamplingAlgorithm, self).__init__(**kwargs)
+        self.sampling_algorithm_type = 'Random'  # type: str
+        self.rule = rule
+        self.seed = seed
+
+
+class RecurrenceSchedule(msrest.serialization.Model):
+    """RecurrenceSchedule.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar hours: Required. [Required] List of hours for the schedule.
+    :vartype hours: list[int]
+    :ivar minutes: Required. [Required] List of minutes for the schedule.
+    :vartype minutes: list[int]
+    :ivar month_days: List of month days for the schedule.
+    :vartype month_days: list[int]
+    :ivar week_days: List of days for the schedule.
+    :vartype week_days: list[str or ~azure.mgmt.machinelearningservices.models.WeekDay]
+    """
+
+    _validation = {
+        'hours': {'required': True},
+        'minutes': {'required': True},
+    }
+
+    _attribute_map = {
+        'hours': {'key': 'hours', 'type': '[int]'},
+        'minutes': {'key': 'minutes', 'type': '[int]'},
+        'month_days': {'key': 'monthDays', 'type': '[int]'},
+        'week_days': {'key': 'weekDays', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        hours: List[int],
+        minutes: List[int],
+        month_days: Optional[List[int]] = None,
+        week_days: Optional[List[Union[str, "WeekDay"]]] = None,
+        **kwargs
+    ):
+        """
+        :keyword hours: Required. [Required] List of hours for the schedule.
+        :paramtype hours: list[int]
+        :keyword minutes: Required. [Required] List of minutes for the schedule.
+        :paramtype minutes: list[int]
+        :keyword month_days: List of month days for the schedule.
+        :paramtype month_days: list[int]
+        :keyword week_days: List of days for the schedule.
+        :paramtype week_days: list[str or ~azure.mgmt.machinelearningservices.models.WeekDay]
+        """
+        super(RecurrenceSchedule, self).__init__(**kwargs)
+        self.hours = hours
+        self.minutes = minutes
+        self.month_days = month_days
+        self.week_days = week_days
+
+
+class RecurrenceTrigger(TriggerBase):
+    """RecurrenceTrigger.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+     https://en.wikipedia.org/wiki/ISO_8601.
+     Recommented format would be "2022-06-01T00:00:01"
+     If not present, the schedule will run indefinitely.
+    :vartype end_time: str
+    :ivar start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+     offset.
+    :vartype start_time: str
+    :ivar time_zone: Specifies time zone in which the schedule runs.
+     TimeZone should follow Windows time zone format. Refer:
+     https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+    :vartype time_zone: str
+    :ivar trigger_type: Required. [Required].Constant filled by server. Possible values include:
+     "Recurrence", "Cron".
+    :vartype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
+    :ivar frequency: Required. [Required] The frequency to trigger schedule. Possible values
+     include: "Minute", "Hour", "Day", "Week", "Month".
+    :vartype frequency: str or ~azure.mgmt.machinelearningservices.models.RecurrenceFrequency
+    :ivar interval: Required. [Required] Specifies schedule interval in conjunction with frequency.
+    :vartype interval: int
+    :ivar schedule: The recurrence schedule.
+    :vartype schedule: ~azure.mgmt.machinelearningservices.models.RecurrenceSchedule
+    """
+
+    _validation = {
+        'trigger_type': {'required': True},
+        'frequency': {'required': True},
+        'interval': {'required': True},
+    }
+
+    _attribute_map = {
+        'end_time': {'key': 'endTime', 'type': 'str'},
+        'start_time': {'key': 'startTime', 'type': 'str'},
+        'time_zone': {'key': 'timeZone', 'type': 'str'},
+        'trigger_type': {'key': 'triggerType', 'type': 'str'},
+        'frequency': {'key': 'frequency', 'type': 'str'},
+        'interval': {'key': 'interval', 'type': 'int'},
+        'schedule': {'key': 'schedule', 'type': 'RecurrenceSchedule'},
+    }
+
+    def __init__(
+        self,
+        *,
+        frequency: Union[str, "RecurrenceFrequency"],
+        interval: int,
+        end_time: Optional[str] = None,
+        start_time: Optional[str] = None,
+        time_zone: Optional[str] = "UTC",
+        schedule: Optional["RecurrenceSchedule"] = None,
+        **kwargs
+    ):
+        """
+        :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
+         https://en.wikipedia.org/wiki/ISO_8601.
+         Recommented format would be "2022-06-01T00:00:01"
+         If not present, the schedule will run indefinitely.
+        :paramtype end_time: str
+        :keyword start_time: Specifies start time of schedule in ISO 8601 format, but without a UTC
+         offset.
+        :paramtype start_time: str
+        :keyword time_zone: Specifies time zone in which the schedule runs.
+         TimeZone should follow Windows time zone format. Refer:
+         https://learn.microsoft.com/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+        :paramtype time_zone: str
+        :keyword frequency: Required. [Required] The frequency to trigger schedule. Possible values
+         include: "Minute", "Hour", "Day", "Week", "Month".
+        :paramtype frequency: str or ~azure.mgmt.machinelearningservices.models.RecurrenceFrequency
+        :keyword interval: Required. [Required] Specifies schedule interval in conjunction with
+         frequency.
+        :paramtype interval: int
+        :keyword schedule: The recurrence schedule.
+        :paramtype schedule: ~azure.mgmt.machinelearningservices.models.RecurrenceSchedule
+        """
+        super(RecurrenceTrigger, self).__init__(end_time=end_time, start_time=start_time, time_zone=time_zone, **kwargs)
+        self.trigger_type = 'Recurrence'  # type: str
+        self.frequency = frequency
+        self.interval = interval
+        self.schedule = schedule
+
+
+class RegenerateEndpointKeysRequest(msrest.serialization.Model):
+    """RegenerateEndpointKeysRequest.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar key_type: Required. [Required] Specification for which type of key to generate. Primary
+     or Secondary. Possible values include: "Primary", "Secondary".
+    :vartype key_type: str or ~azure.mgmt.machinelearningservices.models.KeyType
+    :ivar key_value: The value the key is set to.
+    :vartype key_value: str
+    """
+
+    _validation = {
+        'key_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'key_type': {'key': 'keyType', 'type': 'str'},
+        'key_value': {'key': 'keyValue', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        key_type: Union[str, "KeyType"],
+        key_value: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword key_type: Required. [Required] Specification for which type of key to generate.
+         Primary or Secondary. Possible values include: "Primary", "Secondary".
+        :paramtype key_type: str or ~azure.mgmt.machinelearningservices.models.KeyType
+        :keyword key_value: The value the key is set to.
+        :paramtype key_value: str
+        """
+        super(RegenerateEndpointKeysRequest, self).__init__(**kwargs)
+        self.key_type = key_type
+        self.key_value = key_value
+
+
+class RegistryListCredentialsResult(msrest.serialization.Model):
+    """RegistryListCredentialsResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar location:
+    :vartype location: str
+    :ivar username:
+    :vartype username: str
+    :ivar passwords:
+    :vartype passwords: list[~azure.mgmt.machinelearningservices.models.Password]
+    """
+
+    _validation = {
+        'location': {'readonly': True},
+        'username': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'location': {'key': 'location', 'type': 'str'},
+        'username': {'key': 'username', 'type': 'str'},
+        'passwords': {'key': 'passwords', 'type': '[Password]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        passwords: Optional[List["Password"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword passwords:
+        :paramtype passwords: list[~azure.mgmt.machinelearningservices.models.Password]
+        """
+        super(RegistryListCredentialsResult, self).__init__(**kwargs)
+        self.location = None
+        self.username = None
+        self.passwords = passwords
+
+
+class Regression(AutoMLVertical, TableVertical):
+    """Regression task in AutoML Table vertical.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar cv_split_column_names: Columns to use for CVSplit data.
+    :vartype cv_split_column_names: list[str]
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+    :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
+     when validation dataset is not provided.
+    :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar test_data: Test data input.
+    :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
+     purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype test_data_size: float
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar weight_column_name: The name of the sample weight column. Automated ML supports a
+     weighted column as an input, causing rows in the data to be weighted up or down.
+    :vartype weight_column_name: str
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for regression task. Possible values include:
+     "SpearmanCorrelation", "NormalizedRootMeanSquaredError", "R2Score",
+     "NormalizedMeanAbsoluteError".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.RegressionPrimaryMetrics
+    :ivar training_settings: Inputs for training phase for an AutoML Job.
+    :vartype training_settings:
+     ~azure.mgmt.machinelearningservices.models.RegressionTrainingSettings
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'TableVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'TableVerticalLimitSettings'},
+        'n_cross_validations': {'key': 'nCrossValidations', 'type': 'NCrossValidations'},
+        'test_data': {'key': 'testData', 'type': 'MLTableJobInput'},
+        'test_data_size': {'key': 'testDataSize', 'type': 'float'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
+        'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+        'training_settings': {'key': 'trainingSettings', 'type': 'RegressionTrainingSettings'},
+    }
+
+    def __init__(
+        self,
+        *,
+        training_data: "MLTableJobInput",
+        cv_split_column_names: Optional[List[str]] = None,
+        featurization_settings: Optional["TableVerticalFeaturizationSettings"] = None,
+        limit_settings: Optional["TableVerticalLimitSettings"] = None,
+        n_cross_validations: Optional["NCrossValidations"] = None,
+        test_data: Optional["MLTableJobInput"] = None,
+        test_data_size: Optional[float] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        validation_data_size: Optional[float] = None,
+        weight_column_name: Optional[str] = None,
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        primary_metric: Optional[Union[str, "RegressionPrimaryMetrics"]] = None,
+        training_settings: Optional["RegressionTrainingSettings"] = None,
+        **kwargs
+    ):
+        """
+        :keyword cv_split_column_names: Columns to use for CVSplit data.
+        :paramtype cv_split_column_names: list[str]
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings:
+         ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
+        :keyword n_cross_validations: Number of cross validation folds to be applied on training
+         dataset
+         when validation dataset is not provided.
+        :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword test_data: Test data input.
+        :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
+         purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype test_data_size: float
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword validation_data_size: The fraction of training dataset that needs to be set aside for
+         validation purpose.
+         Values between (0.0 , 1.0)
+         Applied when validation dataset is not provided.
+        :paramtype validation_data_size: float
+        :keyword weight_column_name: The name of the sample weight column. Automated ML supports a
+         weighted column as an input, causing rows in the data to be weighted up or down.
+        :paramtype weight_column_name: str
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric for regression task. Possible values include:
+         "SpearmanCorrelation", "NormalizedRootMeanSquaredError", "R2Score",
+         "NormalizedMeanAbsoluteError".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.RegressionPrimaryMetrics
+        :keyword training_settings: Inputs for training phase for an AutoML Job.
+        :paramtype training_settings:
+         ~azure.mgmt.machinelearningservices.models.RegressionTrainingSettings
+        """
+        super(Regression, self).__init__(log_verbosity=log_verbosity, target_column_name=target_column_name, training_data=training_data, cv_split_column_names=cv_split_column_names, featurization_settings=featurization_settings, limit_settings=limit_settings, n_cross_validations=n_cross_validations, test_data=test_data, test_data_size=test_data_size, validation_data=validation_data, validation_data_size=validation_data_size, weight_column_name=weight_column_name, **kwargs)
+        self.cv_split_column_names = cv_split_column_names
+        self.featurization_settings = featurization_settings
+        self.limit_settings = limit_settings
+        self.n_cross_validations = n_cross_validations
+        self.test_data = test_data
+        self.test_data_size = test_data_size
+        self.validation_data = validation_data
+        self.validation_data_size = validation_data_size
+        self.weight_column_name = weight_column_name
+        self.task_type = 'Regression'  # type: str
+        self.primary_metric = primary_metric
+        self.training_settings = training_settings
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+
+
+class RegressionTrainingSettings(TrainingSettings):
+    """Regression Training related configuration.
+
+    :ivar enable_dnn_training: Enable recommendation of DNN models.
+    :vartype enable_dnn_training: bool
+    :ivar enable_model_explainability: Flag to turn on explainability on best model.
+    :vartype enable_model_explainability: bool
+    :ivar enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+    :vartype enable_onnx_compatible_models: bool
+    :ivar enable_stack_ensemble: Enable stack ensemble run.
+    :vartype enable_stack_ensemble: bool
+    :ivar enable_vote_ensemble: Enable voting ensemble run.
+    :vartype enable_vote_ensemble: bool
+    :ivar ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+     generation, multiple fitted models from the previous child runs are downloaded.
+     Configure this parameter with a higher value than 300 secs, if more time is needed.
+    :vartype ensemble_model_download_timeout: ~datetime.timedelta
+    :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+    :vartype stack_ensemble_settings:
+     ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    :ivar allowed_training_algorithms: Allowed models for regression task.
+    :vartype allowed_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.RegressionModels]
+    :ivar blocked_training_algorithms: Blocked models for regression task.
+    :vartype blocked_training_algorithms: list[str or
+     ~azure.mgmt.machinelearningservices.models.RegressionModels]
+    """
+
+    _attribute_map = {
+        'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
+        'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
+        'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
+        'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
+        'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
+        'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'duration'},
+        'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'StackEnsembleSettings'},
+        'allowed_training_algorithms': {'key': 'allowedTrainingAlgorithms', 'type': '[str]'},
+        'blocked_training_algorithms': {'key': 'blockedTrainingAlgorithms', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        enable_dnn_training: Optional[bool] = False,
+        enable_model_explainability: Optional[bool] = True,
+        enable_onnx_compatible_models: Optional[bool] = False,
+        enable_stack_ensemble: Optional[bool] = True,
+        enable_vote_ensemble: Optional[bool] = True,
+        ensemble_model_download_timeout: Optional[datetime.timedelta] = "PT5M",
+        stack_ensemble_settings: Optional["StackEnsembleSettings"] = None,
+        allowed_training_algorithms: Optional[List[Union[str, "RegressionModels"]]] = None,
+        blocked_training_algorithms: Optional[List[Union[str, "RegressionModels"]]] = None,
+        **kwargs
+    ):
+        """
+        :keyword enable_dnn_training: Enable recommendation of DNN models.
+        :paramtype enable_dnn_training: bool
+        :keyword enable_model_explainability: Flag to turn on explainability on best model.
+        :paramtype enable_model_explainability: bool
+        :keyword enable_onnx_compatible_models: Flag for enabling onnx compatible models.
+        :paramtype enable_onnx_compatible_models: bool
+        :keyword enable_stack_ensemble: Enable stack ensemble run.
+        :paramtype enable_stack_ensemble: bool
+        :keyword enable_vote_ensemble: Enable voting ensemble run.
+        :paramtype enable_vote_ensemble: bool
+        :keyword ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model
+         generation, multiple fitted models from the previous child runs are downloaded.
+         Configure this parameter with a higher value than 300 secs, if more time is needed.
+        :paramtype ensemble_model_download_timeout: ~datetime.timedelta
+        :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
+        :paramtype stack_ensemble_settings:
+         ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        :keyword allowed_training_algorithms: Allowed models for regression task.
+        :paramtype allowed_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.RegressionModels]
+        :keyword blocked_training_algorithms: Blocked models for regression task.
+        :paramtype blocked_training_algorithms: list[str or
+         ~azure.mgmt.machinelearningservices.models.RegressionModels]
+        """
+        super(RegressionTrainingSettings, self).__init__(enable_dnn_training=enable_dnn_training, enable_model_explainability=enable_model_explainability, enable_onnx_compatible_models=enable_onnx_compatible_models, enable_stack_ensemble=enable_stack_ensemble, enable_vote_ensemble=enable_vote_ensemble, ensemble_model_download_timeout=ensemble_model_download_timeout, stack_ensemble_settings=stack_ensemble_settings, **kwargs)
+        self.allowed_training_algorithms = allowed_training_algorithms
+        self.blocked_training_algorithms = blocked_training_algorithms
+
+
+class ResourceId(msrest.serialization.Model):
+    """Represents a resource ID. For example, for a subnet, it is the resource URL for the subnet.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Required. The ID of the resource.
+    :vartype id: str
+    """
+
+    _validation = {
+        'id': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        id: str,
+        **kwargs
+    ):
+        """
+        :keyword id: Required. The ID of the resource.
+        :paramtype id: str
+        """
+        super(ResourceId, self).__init__(**kwargs)
+        self.id = id
+
+
+class ResourceName(msrest.serialization.Model):
+    """The Resource Name.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The name of the resource.
+    :vartype value: str
+    :ivar localized_value: The localized name of the resource.
+    :vartype localized_value: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'localized_value': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': 'str'},
+        'localized_value': {'key': 'localizedValue', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ResourceName, self).__init__(**kwargs)
+        self.value = None
+        self.localized_value = None
+
+
+class ResourceQuota(msrest.serialization.Model):
+    """The quota assigned to a resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Specifies the resource ID.
+    :vartype id: str
+    :ivar aml_workspace_location: Region of the AML workspace in the id.
+    :vartype aml_workspace_location: str
+    :ivar type: Specifies the resource type.
+    :vartype type: str
+    :ivar name: Name of the resource.
+    :vartype name: ~azure.mgmt.machinelearningservices.models.ResourceName
+    :ivar limit: The maximum permitted quota of the resource.
+    :vartype limit: long
+    :ivar unit: An enum describing the unit of quota measurement. Possible values include: "Count".
+    :vartype unit: str or ~azure.mgmt.machinelearningservices.models.QuotaUnit
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'aml_workspace_location': {'readonly': True},
+        'type': {'readonly': True},
+        'name': {'readonly': True},
+        'limit': {'readonly': True},
+        'unit': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'aml_workspace_location': {'key': 'amlWorkspaceLocation', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'ResourceName'},
+        'limit': {'key': 'limit', 'type': 'long'},
+        'unit': {'key': 'unit', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(ResourceQuota, self).__init__(**kwargs)
+        self.id = None
+        self.aml_workspace_location = None
+        self.type = None
+        self.name = None
+        self.limit = None
+        self.unit = None
+
+
+class Route(msrest.serialization.Model):
+    """Route.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar path: Required. [Required] The path for the route.
+    :vartype path: str
+    :ivar port: Required. [Required] The port for the route.
+    :vartype port: int
+    """
+
+    _validation = {
+        'path': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'port': {'required': True},
+    }
+
+    _attribute_map = {
+        'path': {'key': 'path', 'type': 'str'},
+        'port': {'key': 'port', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        path: str,
+        port: int,
+        **kwargs
+    ):
+        """
+        :keyword path: Required. [Required] The path for the route.
+        :paramtype path: str
+        :keyword port: Required. [Required] The port for the route.
+        :paramtype port: int
+        """
+        super(Route, self).__init__(**kwargs)
+        self.path = path
+        self.port = port
+
+
+class SASAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """SASAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar credentials:
+    :vartype credentials:
+     ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionSharedAccessSignature
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+        'credentials': {'key': 'credentials', 'type': 'WorkspaceConnectionSharedAccessSignature'},
+    }
+
+    def __init__(
+        self,
+        *,
+        category: Optional[Union[str, "ConnectionCategory"]] = None,
+        target: Optional[str] = None,
+        value: Optional[str] = None,
+        value_format: Optional[Union[str, "ValueFormat"]] = None,
+        credentials: Optional["WorkspaceConnectionSharedAccessSignature"] = None,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword credentials:
+        :paramtype credentials:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionSharedAccessSignature
+        """
+        super(SASAuthTypeWorkspaceConnectionProperties, self).__init__(category=category, target=target, value=value, value_format=value_format, **kwargs)
+        self.auth_type = 'SAS'  # type: str
+        self.credentials = credentials
+
+
+class SasDatastoreCredentials(DatastoreCredentials):
+    """SAS datastore credentials configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    :ivar secrets: Required. [Required] Storage container secrets.
+    :vartype secrets: ~azure.mgmt.machinelearningservices.models.SasDatastoreSecrets
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+        'secrets': {'required': True},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+        'secrets': {'key': 'secrets', 'type': 'SasDatastoreSecrets'},
+    }
+
+    def __init__(
+        self,
+        *,
+        secrets: "SasDatastoreSecrets",
+        **kwargs
+    ):
+        """
+        :keyword secrets: Required. [Required] Storage container secrets.
+        :paramtype secrets: ~azure.mgmt.machinelearningservices.models.SasDatastoreSecrets
+        """
+        super(SasDatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = 'Sas'  # type: str
+        self.secrets = secrets
+
+
+class SasDatastoreSecrets(DatastoreSecrets):
+    """Datastore SAS secrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate", "Sas",
+     "ServicePrincipal".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    :ivar sas_token: Storage container SAS token.
+    :vartype sas_token: str
+    """
+
+    _validation = {
+        'secrets_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'secrets_type': {'key': 'secretsType', 'type': 'str'},
+        'sas_token': {'key': 'sasToken', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        sas_token: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword sas_token: Storage container SAS token.
+        :paramtype sas_token: str
+        """
+        super(SasDatastoreSecrets, self).__init__(**kwargs)
+        self.secrets_type = 'Sas'  # type: str
+        self.sas_token = sas_token
+
+
+class ScaleSettings(msrest.serialization.Model):
+    """scale settings for AML Compute.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar max_node_count: Required. Max number of nodes to use.
+    :vartype max_node_count: int
+    :ivar min_node_count: Min number of nodes to use.
+    :vartype min_node_count: int
+    :ivar node_idle_time_before_scale_down: Node Idle Time before scaling down amlCompute. This
+     string needs to be in the RFC Format.
+    :vartype node_idle_time_before_scale_down: ~datetime.timedelta
+    """
+
+    _validation = {
+        'max_node_count': {'required': True},
+    }
+
+    _attribute_map = {
+        'max_node_count': {'key': 'maxNodeCount', 'type': 'int'},
+        'min_node_count': {'key': 'minNodeCount', 'type': 'int'},
+        'node_idle_time_before_scale_down': {'key': 'nodeIdleTimeBeforeScaleDown', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        max_node_count: int,
+        min_node_count: Optional[int] = 0,
+        node_idle_time_before_scale_down: Optional[datetime.timedelta] = None,
+        **kwargs
+    ):
+        """
+        :keyword max_node_count: Required. Max number of nodes to use.
+        :paramtype max_node_count: int
+        :keyword min_node_count: Min number of nodes to use.
+        :paramtype min_node_count: int
+        :keyword node_idle_time_before_scale_down: Node Idle Time before scaling down amlCompute. This
+         string needs to be in the RFC Format.
+        :paramtype node_idle_time_before_scale_down: ~datetime.timedelta
+        """
+        super(ScaleSettings, self).__init__(**kwargs)
+        self.max_node_count = max_node_count
+        self.min_node_count = min_node_count
+        self.node_idle_time_before_scale_down = node_idle_time_before_scale_down
+
+
+class ScaleSettingsInformation(msrest.serialization.Model):
+    """Desired scale settings for the amlCompute.
+
+    :ivar scale_settings: scale settings for AML Compute.
+    :vartype scale_settings: ~azure.mgmt.machinelearningservices.models.ScaleSettings
+    """
+
+    _attribute_map = {
+        'scale_settings': {'key': 'scaleSettings', 'type': 'ScaleSettings'},
+    }
+
+    def __init__(
+        self,
+        *,
+        scale_settings: Optional["ScaleSettings"] = None,
+        **kwargs
+    ):
+        """
+        :keyword scale_settings: scale settings for AML Compute.
+        :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.ScaleSettings
+        """
+        super(ScaleSettingsInformation, self).__init__(**kwargs)
+        self.scale_settings = scale_settings
+
+
+class Schedule(Resource):
+    """Azure Resource Manager resource envelope.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required. [Required] Additional attributes of the entity.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.ScheduleProperties
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'ScheduleProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "ScheduleProperties",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required. [Required] Additional attributes of the entity.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.ScheduleProperties
+        """
+        super(Schedule, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class ScheduleBase(msrest.serialization.Model):
+    """ScheduleBase.
+
+    :ivar id: A system assigned id for the schedule.
+    :vartype id: str
+    :ivar provisioning_status: The current deployment state of schedule. Possible values include:
+     "Completed", "Provisioning", "Failed".
+    :vartype provisioning_status: str or
+     ~azure.mgmt.machinelearningservices.models.ScheduleProvisioningState
+    :ivar status: Is the schedule enabled or disabled?. Possible values include: "Enabled",
+     "Disabled".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.ScheduleStatus
+    """
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'provisioning_status': {'key': 'provisioningStatus', 'type': 'str'},
+        'status': {'key': 'status', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        id: Optional[str] = None,
+        provisioning_status: Optional[Union[str, "ScheduleProvisioningState"]] = None,
+        status: Optional[Union[str, "ScheduleStatus"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword id: A system assigned id for the schedule.
+        :paramtype id: str
+        :keyword provisioning_status: The current deployment state of schedule. Possible values
+         include: "Completed", "Provisioning", "Failed".
+        :paramtype provisioning_status: str or
+         ~azure.mgmt.machinelearningservices.models.ScheduleProvisioningState
+        :keyword status: Is the schedule enabled or disabled?. Possible values include: "Enabled",
+         "Disabled".
+        :paramtype status: str or ~azure.mgmt.machinelearningservices.models.ScheduleStatus
+        """
+        super(ScheduleBase, self).__init__(**kwargs)
+        self.id = id
+        self.provisioning_status = provisioning_status
+        self.status = status
+
+
+class ScheduleProperties(ResourceBase):
+    """Base definition of a schedule.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar action: Required. [Required] Specifies the action of the schedule.
+    :vartype action: ~azure.mgmt.machinelearningservices.models.ScheduleActionBase
+    :ivar display_name: Display name of schedule.
+    :vartype display_name: str
+    :ivar is_enabled: Is the schedule enabled?.
+    :vartype is_enabled: bool
+    :ivar provisioning_state: Provisioning state for the schedule. Possible values include:
+     "Creating", "Updating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ScheduleProvisioningStatus
+    :ivar trigger: Required. [Required] Specifies the trigger details.
+    :vartype trigger: ~azure.mgmt.machinelearningservices.models.TriggerBase
+    """
+
+    _validation = {
+        'action': {'required': True},
+        'provisioning_state': {'readonly': True},
+        'trigger': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'action': {'key': 'action', 'type': 'ScheduleActionBase'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'is_enabled': {'key': 'isEnabled', 'type': 'bool'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'trigger': {'key': 'trigger', 'type': 'TriggerBase'},
+    }
+
+    def __init__(
+        self,
+        *,
+        action: "ScheduleActionBase",
+        trigger: "TriggerBase",
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        display_name: Optional[str] = None,
+        is_enabled: Optional[bool] = True,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword action: Required. [Required] Specifies the action of the schedule.
+        :paramtype action: ~azure.mgmt.machinelearningservices.models.ScheduleActionBase
+        :keyword display_name: Display name of schedule.
+        :paramtype display_name: str
+        :keyword is_enabled: Is the schedule enabled?.
+        :paramtype is_enabled: bool
+        :keyword trigger: Required. [Required] Specifies the trigger details.
+        :paramtype trigger: ~azure.mgmt.machinelearningservices.models.TriggerBase
+        """
+        super(ScheduleProperties, self).__init__(description=description, properties=properties, tags=tags, **kwargs)
+        self.action = action
+        self.display_name = display_name
+        self.is_enabled = is_enabled
+        self.provisioning_state = None
+        self.trigger = trigger
+
+
+class ScheduleResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of Schedule entities.
+
+    :ivar next_link: The link to the next page of Schedule objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type Schedule.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Schedule]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[Schedule]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["Schedule"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of Schedule objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type Schedule.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.Schedule]
+        """
+        super(ScheduleResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class ScriptReference(msrest.serialization.Model):
+    """Script reference.
+
+    :ivar script_source: The storage source of the script: inline, workspace.
+    :vartype script_source: str
+    :ivar script_data: The location of scripts in the mounted volume.
+    :vartype script_data: str
+    :ivar script_arguments: Optional command line arguments passed to the script to run.
+    :vartype script_arguments: str
+    :ivar timeout: Optional time period passed to timeout command.
+    :vartype timeout: str
+    """
+
+    _attribute_map = {
+        'script_source': {'key': 'scriptSource', 'type': 'str'},
+        'script_data': {'key': 'scriptData', 'type': 'str'},
+        'script_arguments': {'key': 'scriptArguments', 'type': 'str'},
+        'timeout': {'key': 'timeout', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        script_source: Optional[str] = None,
+        script_data: Optional[str] = None,
+        script_arguments: Optional[str] = None,
+        timeout: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword script_source: The storage source of the script: inline, workspace.
+        :paramtype script_source: str
+        :keyword script_data: The location of scripts in the mounted volume.
+        :paramtype script_data: str
+        :keyword script_arguments: Optional command line arguments passed to the script to run.
+        :paramtype script_arguments: str
+        :keyword timeout: Optional time period passed to timeout command.
+        :paramtype timeout: str
+        """
+        super(ScriptReference, self).__init__(**kwargs)
+        self.script_source = script_source
+        self.script_data = script_data
+        self.script_arguments = script_arguments
+        self.timeout = timeout
+
+
+class ScriptsToExecute(msrest.serialization.Model):
+    """Customized setup scripts.
+
+    :ivar startup_script: Script that's run every time the machine starts.
+    :vartype startup_script: ~azure.mgmt.machinelearningservices.models.ScriptReference
+    :ivar creation_script: Script that's run only once during provision of the compute.
+    :vartype creation_script: ~azure.mgmt.machinelearningservices.models.ScriptReference
+    """
+
+    _attribute_map = {
+        'startup_script': {'key': 'startupScript', 'type': 'ScriptReference'},
+        'creation_script': {'key': 'creationScript', 'type': 'ScriptReference'},
+    }
+
+    def __init__(
+        self,
+        *,
+        startup_script: Optional["ScriptReference"] = None,
+        creation_script: Optional["ScriptReference"] = None,
+        **kwargs
+    ):
+        """
+        :keyword startup_script: Script that's run every time the machine starts.
+        :paramtype startup_script: ~azure.mgmt.machinelearningservices.models.ScriptReference
+        :keyword creation_script: Script that's run only once during provision of the compute.
+        :paramtype creation_script: ~azure.mgmt.machinelearningservices.models.ScriptReference
+        """
+        super(ScriptsToExecute, self).__init__(**kwargs)
+        self.startup_script = startup_script
+        self.creation_script = creation_script
+
+
+class ServiceManagedResourcesSettings(msrest.serialization.Model):
+    """ServiceManagedResourcesSettings.
+
+    :ivar cosmos_db: The settings for the service managed cosmosdb account.
+    :vartype cosmos_db: ~azure.mgmt.machinelearningservices.models.CosmosDbSettings
+    """
+
+    _attribute_map = {
+        'cosmos_db': {'key': 'cosmosDb', 'type': 'CosmosDbSettings'},
+    }
+
+    def __init__(
+        self,
+        *,
+        cosmos_db: Optional["CosmosDbSettings"] = None,
+        **kwargs
+    ):
+        """
+        :keyword cosmos_db: The settings for the service managed cosmosdb account.
+        :paramtype cosmos_db: ~azure.mgmt.machinelearningservices.models.CosmosDbSettings
+        """
+        super(ServiceManagedResourcesSettings, self).__init__(**kwargs)
+        self.cosmos_db = cosmos_db
+
+
+class ServicePrincipalDatastoreCredentials(DatastoreCredentials):
+    """Service Principal datastore credentials configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate",
+     "None", "Sas", "ServicePrincipal".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    :ivar authority_url: Authority URL used for authentication.
+    :vartype authority_url: str
+    :ivar client_id: Required. [Required] Service principal client ID.
+    :vartype client_id: str
+    :ivar resource_url: Resource the service principal has access to.
+    :vartype resource_url: str
+    :ivar secrets: Required. [Required] Service principal secrets.
+    :vartype secrets: ~azure.mgmt.machinelearningservices.models.ServicePrincipalDatastoreSecrets
+    :ivar tenant_id: Required. [Required] ID of the tenant to which the service principal belongs.
+    :vartype tenant_id: str
+    """
+
+    _validation = {
+        'credentials_type': {'required': True},
+        'client_id': {'required': True},
+        'secrets': {'required': True},
+        'tenant_id': {'required': True},
+    }
+
+    _attribute_map = {
+        'credentials_type': {'key': 'credentialsType', 'type': 'str'},
+        'authority_url': {'key': 'authorityUrl', 'type': 'str'},
+        'client_id': {'key': 'clientId', 'type': 'str'},
+        'resource_url': {'key': 'resourceUrl', 'type': 'str'},
+        'secrets': {'key': 'secrets', 'type': 'ServicePrincipalDatastoreSecrets'},
+        'tenant_id': {'key': 'tenantId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        client_id: str,
+        secrets: "ServicePrincipalDatastoreSecrets",
+        tenant_id: str,
+        authority_url: Optional[str] = None,
+        resource_url: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword authority_url: Authority URL used for authentication.
+        :paramtype authority_url: str
+        :keyword client_id: Required. [Required] Service principal client ID.
+        :paramtype client_id: str
+        :keyword resource_url: Resource the service principal has access to.
+        :paramtype resource_url: str
+        :keyword secrets: Required. [Required] Service principal secrets.
+        :paramtype secrets: ~azure.mgmt.machinelearningservices.models.ServicePrincipalDatastoreSecrets
+        :keyword tenant_id: Required. [Required] ID of the tenant to which the service principal
+         belongs.
+        :paramtype tenant_id: str
+        """
+        super(ServicePrincipalDatastoreCredentials, self).__init__(**kwargs)
+        self.credentials_type = 'ServicePrincipal'  # type: str
+        self.authority_url = authority_url
+        self.client_id = client_id
+        self.resource_url = resource_url
+        self.secrets = secrets
+        self.tenant_id = tenant_id
+
+
+class ServicePrincipalDatastoreSecrets(DatastoreSecrets):
+    """Datastore Service Principal secrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: Required. [Required] Credential type used to authentication with
+     storage.Constant filled by server. Possible values include: "AccountKey", "Certificate", "Sas",
+     "ServicePrincipal".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    :ivar client_secret: Service principal secret.
+    :vartype client_secret: str
+    """
+
+    _validation = {
+        'secrets_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'secrets_type': {'key': 'secretsType', 'type': 'str'},
+        'client_secret': {'key': 'clientSecret', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        client_secret: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword client_secret: Service principal secret.
+        :paramtype client_secret: str
+        """
+        super(ServicePrincipalDatastoreSecrets, self).__init__(**kwargs)
+        self.secrets_type = 'ServicePrincipal'  # type: str
+        self.client_secret = client_secret
+
+
+class SetupScripts(msrest.serialization.Model):
+    """Details of customized scripts to execute for setting up the cluster.
+
+    :ivar scripts: Customized setup scripts.
+    :vartype scripts: ~azure.mgmt.machinelearningservices.models.ScriptsToExecute
+    """
+
+    _attribute_map = {
+        'scripts': {'key': 'scripts', 'type': 'ScriptsToExecute'},
+    }
+
+    def __init__(
+        self,
+        *,
+        scripts: Optional["ScriptsToExecute"] = None,
+        **kwargs
+    ):
+        """
+        :keyword scripts: Customized setup scripts.
+        :paramtype scripts: ~azure.mgmt.machinelearningservices.models.ScriptsToExecute
+        """
+        super(SetupScripts, self).__init__(**kwargs)
+        self.scripts = scripts
+
+
+class SharedPrivateLinkResource(msrest.serialization.Model):
+    """SharedPrivateLinkResource.
+
+    :ivar name: Unique name of the private link.
+    :vartype name: str
+    :ivar private_link_resource_id: The resource id that private link links to.
+    :vartype private_link_resource_id: str
+    :ivar group_id: The private link resource group id.
+    :vartype group_id: str
+    :ivar request_message: Request message.
+    :vartype request_message: str
+    :ivar status: Indicates whether the connection has been Approved/Rejected/Removed by the owner
+     of the service. Possible values include: "Pending", "Approved", "Rejected", "Disconnected",
+     "Timeout".
+    :vartype status: str or
+     ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+    """
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'private_link_resource_id': {'key': 'properties.privateLinkResourceId', 'type': 'str'},
+        'group_id': {'key': 'properties.groupId', 'type': 'str'},
+        'request_message': {'key': 'properties.requestMessage', 'type': 'str'},
+        'status': {'key': 'properties.status', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        name: Optional[str] = None,
+        private_link_resource_id: Optional[str] = None,
+        group_id: Optional[str] = None,
+        request_message: Optional[str] = None,
+        status: Optional[Union[str, "PrivateEndpointServiceConnectionStatus"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword name: Unique name of the private link.
+        :paramtype name: str
+        :keyword private_link_resource_id: The resource id that private link links to.
+        :paramtype private_link_resource_id: str
+        :keyword group_id: The private link resource group id.
+        :paramtype group_id: str
+        :keyword request_message: Request message.
+        :paramtype request_message: str
+        :keyword status: Indicates whether the connection has been Approved/Rejected/Removed by the
+         owner of the service. Possible values include: "Pending", "Approved", "Rejected",
+         "Disconnected", "Timeout".
+        :paramtype status: str or
+         ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+        """
+        super(SharedPrivateLinkResource, self).__init__(**kwargs)
+        self.name = name
+        self.private_link_resource_id = private_link_resource_id
+        self.group_id = group_id
+        self.request_message = request_message
+        self.status = status
+
+
+class Sku(msrest.serialization.Model):
+    """The resource model definition representing SKU.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar name: Required. The name of the SKU. Ex - P3. It is typically a letter+number code.
+    :vartype name: str
+    :ivar tier: This field is required to be implemented by the Resource Provider if the service
+     has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+     "Standard", "Premium".
+    :vartype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+    :ivar size: The SKU size. When the name field is the combination of tier and some other value,
+     this would be the standalone code.
+    :vartype size: str
+    :ivar family: If the service has different generations of hardware, for the same SKU, then that
+     can be captured here.
+    :vartype family: str
+    :ivar capacity: If the SKU supports scale out/in then the capacity integer should be included.
+     If scale out/in is not possible for the resource this may be omitted.
+    :vartype capacity: int
+    """
+
+    _validation = {
+        'name': {'required': True},
+    }
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'tier': {'key': 'tier', 'type': 'str'},
+        'size': {'key': 'size', 'type': 'str'},
+        'family': {'key': 'family', 'type': 'str'},
+        'capacity': {'key': 'capacity', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        tier: Optional[Union[str, "SkuTier"]] = None,
+        size: Optional[str] = None,
+        family: Optional[str] = None,
+        capacity: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword name: Required. The name of the SKU. Ex - P3. It is typically a letter+number code.
+        :paramtype name: str
+        :keyword tier: This field is required to be implemented by the Resource Provider if the service
+         has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+         "Standard", "Premium".
+        :paramtype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+        :keyword size: The SKU size. When the name field is the combination of tier and some other
+         value, this would be the standalone code.
+        :paramtype size: str
+        :keyword family: If the service has different generations of hardware, for the same SKU, then
+         that can be captured here.
+        :paramtype family: str
+        :keyword capacity: If the SKU supports scale out/in then the capacity integer should be
+         included. If scale out/in is not possible for the resource this may be omitted.
+        :paramtype capacity: int
+        """
+        super(Sku, self).__init__(**kwargs)
+        self.name = name
+        self.tier = tier
+        self.size = size
+        self.family = family
+        self.capacity = capacity
+
+
+class SkuCapacity(msrest.serialization.Model):
+    """SKU capacity information.
+
+    :ivar default: Gets or sets the default capacity.
+    :vartype default: int
+    :ivar maximum: Gets or sets the maximum.
+    :vartype maximum: int
+    :ivar minimum: Gets or sets the minimum.
+    :vartype minimum: int
+    :ivar scale_type: Gets or sets the type of the scale. Possible values include: "Automatic",
+     "Manual", "None".
+    :vartype scale_type: str or ~azure.mgmt.machinelearningservices.models.SkuScaleType
+    """
+
+    _attribute_map = {
+        'default': {'key': 'default', 'type': 'int'},
+        'maximum': {'key': 'maximum', 'type': 'int'},
+        'minimum': {'key': 'minimum', 'type': 'int'},
+        'scale_type': {'key': 'scaleType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        default: Optional[int] = 0,
+        maximum: Optional[int] = 0,
+        minimum: Optional[int] = 0,
+        scale_type: Optional[Union[str, "SkuScaleType"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword default: Gets or sets the default capacity.
+        :paramtype default: int
+        :keyword maximum: Gets or sets the maximum.
+        :paramtype maximum: int
+        :keyword minimum: Gets or sets the minimum.
+        :paramtype minimum: int
+        :keyword scale_type: Gets or sets the type of the scale. Possible values include: "Automatic",
+         "Manual", "None".
+        :paramtype scale_type: str or ~azure.mgmt.machinelearningservices.models.SkuScaleType
+        """
+        super(SkuCapacity, self).__init__(**kwargs)
+        self.default = default
+        self.maximum = maximum
+        self.minimum = minimum
+        self.scale_type = scale_type
+
+
+class SkuResource(msrest.serialization.Model):
+    """Fulfills ARM Contract requirement to list all available SKUS for a resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar capacity: Gets or sets the Sku Capacity.
+    :vartype capacity: ~azure.mgmt.machinelearningservices.models.SkuCapacity
+    :ivar resource_type: The resource type name.
+    :vartype resource_type: str
+    :ivar sku: Gets or sets the Sku.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.SkuSetting
+    """
+
+    _validation = {
+        'resource_type': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'capacity': {'key': 'capacity', 'type': 'SkuCapacity'},
+        'resource_type': {'key': 'resourceType', 'type': 'str'},
+        'sku': {'key': 'sku', 'type': 'SkuSetting'},
+    }
+
+    def __init__(
+        self,
+        *,
+        capacity: Optional["SkuCapacity"] = None,
+        sku: Optional["SkuSetting"] = None,
+        **kwargs
+    ):
+        """
+        :keyword capacity: Gets or sets the Sku Capacity.
+        :paramtype capacity: ~azure.mgmt.machinelearningservices.models.SkuCapacity
+        :keyword sku: Gets or sets the Sku.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.SkuSetting
+        """
+        super(SkuResource, self).__init__(**kwargs)
+        self.capacity = capacity
+        self.resource_type = None
+        self.sku = sku
+
+
+class SkuResourceArmPaginatedResult(msrest.serialization.Model):
+    """A paginated list of SkuResource entities.
+
+    :ivar next_link: The link to the next page of SkuResource objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type SkuResource.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.SkuResource]
+    """
+
+    _attribute_map = {
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+        'value': {'key': 'value', 'type': '[SkuResource]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["SkuResource"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword next_link: The link to the next page of SkuResource objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type SkuResource.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.SkuResource]
+        """
+        super(SkuResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class SkuSetting(msrest.serialization.Model):
+    """SkuSetting fulfills the need for stripped down SKU info in ARM contract.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar name: Required. [Required] The name of the SKU. Ex - P3. It is typically a letter+number
+     code.
+    :vartype name: str
+    :ivar tier: This field is required to be implemented by the Resource Provider if the service
+     has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+     "Standard", "Premium".
+    :vartype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+    """
+
+    _validation = {
+        'name': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'tier': {'key': 'tier', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        tier: Optional[Union[str, "SkuTier"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword name: Required. [Required] The name of the SKU. Ex - P3. It is typically a
+         letter+number code.
+        :paramtype name: str
+        :keyword tier: This field is required to be implemented by the Resource Provider if the service
+         has more than one tier, but is not required on a PUT. Possible values include: "Free", "Basic",
+         "Standard", "Premium".
+        :paramtype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+        """
+        super(SkuSetting, self).__init__(**kwargs)
+        self.name = name
+        self.tier = tier
+
+
+class SslConfiguration(msrest.serialization.Model):
+    """The ssl configuration for scoring.
+
+    :ivar status: Enable or disable ssl for scoring. Possible values include: "Disabled",
+     "Enabled", "Auto".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.SslConfigStatus
+    :ivar cert: Cert data.
+    :vartype cert: str
+    :ivar key: Key data.
+    :vartype key: str
+    :ivar cname: CNAME of the cert.
+    :vartype cname: str
+    :ivar leaf_domain_label: Leaf domain label of public endpoint.
+    :vartype leaf_domain_label: str
+    :ivar overwrite_existing_domain: Indicates whether to overwrite existing domain label.
+    :vartype overwrite_existing_domain: bool
+    """
+
+    _attribute_map = {
+        'status': {'key': 'status', 'type': 'str'},
+        'cert': {'key': 'cert', 'type': 'str'},
+        'key': {'key': 'key', 'type': 'str'},
+        'cname': {'key': 'cname', 'type': 'str'},
+        'leaf_domain_label': {'key': 'leafDomainLabel', 'type': 'str'},
+        'overwrite_existing_domain': {'key': 'overwriteExistingDomain', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        status: Optional[Union[str, "SslConfigStatus"]] = None,
+        cert: Optional[str] = None,
+        key: Optional[str] = None,
+        cname: Optional[str] = None,
+        leaf_domain_label: Optional[str] = None,
+        overwrite_existing_domain: Optional[bool] = None,
+        **kwargs
+    ):
+        """
+        :keyword status: Enable or disable ssl for scoring. Possible values include: "Disabled",
+         "Enabled", "Auto".
+        :paramtype status: str or ~azure.mgmt.machinelearningservices.models.SslConfigStatus
+        :keyword cert: Cert data.
+        :paramtype cert: str
+        :keyword key: Key data.
+        :paramtype key: str
+        :keyword cname: CNAME of the cert.
+        :paramtype cname: str
+        :keyword leaf_domain_label: Leaf domain label of public endpoint.
+        :paramtype leaf_domain_label: str
+        :keyword overwrite_existing_domain: Indicates whether to overwrite existing domain label.
+        :paramtype overwrite_existing_domain: bool
+        """
+        super(SslConfiguration, self).__init__(**kwargs)
+        self.status = status
+        self.cert = cert
+        self.key = key
+        self.cname = cname
+        self.leaf_domain_label = leaf_domain_label
+        self.overwrite_existing_domain = overwrite_existing_domain
+
+
+class StackEnsembleSettings(msrest.serialization.Model):
+    """Advances setting to customize StackEnsemble run.
+
+    :ivar stack_meta_learner_k_wargs: Optional parameters to pass to the initializer of the
+     meta-learner.
+    :vartype stack_meta_learner_k_wargs: any
+    :ivar stack_meta_learner_train_percentage: Specifies the proportion of the training set (when
+     choosing train and validation type of training) to be reserved for training the meta-learner.
+     Default value is 0.2.
+    :vartype stack_meta_learner_train_percentage: float
+    :ivar stack_meta_learner_type: The meta-learner is a model trained on the output of the
+     individual heterogeneous models. Possible values include: "None", "LogisticRegression",
+     "LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
+     "LightGBMRegressor", "LinearRegression".
+    :vartype stack_meta_learner_type: str or
+     ~azure.mgmt.machinelearningservices.models.StackMetaLearnerType
+    """
+
+    _attribute_map = {
+        'stack_meta_learner_k_wargs': {'key': 'stackMetaLearnerKWargs', 'type': 'object'},
+        'stack_meta_learner_train_percentage': {'key': 'stackMetaLearnerTrainPercentage', 'type': 'float'},
+        'stack_meta_learner_type': {'key': 'stackMetaLearnerType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        stack_meta_learner_k_wargs: Optional[Any] = None,
+        stack_meta_learner_train_percentage: Optional[float] = 0.2,
+        stack_meta_learner_type: Optional[Union[str, "StackMetaLearnerType"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword stack_meta_learner_k_wargs: Optional parameters to pass to the initializer of the
+         meta-learner.
+        :paramtype stack_meta_learner_k_wargs: any
+        :keyword stack_meta_learner_train_percentage: Specifies the proportion of the training set
+         (when choosing train and validation type of training) to be reserved for training the
+         meta-learner. Default value is 0.2.
+        :paramtype stack_meta_learner_train_percentage: float
+        :keyword stack_meta_learner_type: The meta-learner is a model trained on the output of the
+         individual heterogeneous models. Possible values include: "None", "LogisticRegression",
+         "LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
+         "LightGBMRegressor", "LinearRegression".
+        :paramtype stack_meta_learner_type: str or
+         ~azure.mgmt.machinelearningservices.models.StackMetaLearnerType
+        """
+        super(StackEnsembleSettings, self).__init__(**kwargs)
+        self.stack_meta_learner_k_wargs = stack_meta_learner_k_wargs
+        self.stack_meta_learner_train_percentage = stack_meta_learner_train_percentage
+        self.stack_meta_learner_type = stack_meta_learner_type
+
+
+class SweepJob(JobBaseProperties):
+    """Sweep job definition.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "AutoML", "Command", "Sweep", "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Possible values include: "NotStarted", "Starting",
+     "Provisioning", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
+     "Failed", "Canceled", "NotResponding", "Paused", "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar early_termination: Early termination policies enable canceling poor-performing runs
+     before they complete.
+    :vartype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+    :ivar inputs: Mapping of input data bindings used in the job.
+    :vartype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+    :ivar limits: Sweep Job limit.
+    :vartype limits: ~azure.mgmt.machinelearningservices.models.SweepJobLimits
+    :ivar objective: Required. [Required] Optimization objective.
+    :vartype objective: ~azure.mgmt.machinelearningservices.models.Objective
+    :ivar outputs: Mapping of output data bindings used in the job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar sampling_algorithm: Required. [Required] The hyperparameter sampling algorithm.
+    :vartype sampling_algorithm: ~azure.mgmt.machinelearningservices.models.SamplingAlgorithm
+    :ivar search_space: Required. [Required] A dictionary containing each parameter and its
+     distribution. The dictionary key is the name of the parameter.
+    :vartype search_space: any
+    :ivar trial: Required. [Required] Trial component definition.
+    :vartype trial: ~azure.mgmt.machinelearningservices.models.TrialComponent
+    """
+
+    _validation = {
+        'job_type': {'required': True},
+        'status': {'readonly': True},
+        'objective': {'required': True},
+        'sampling_algorithm': {'required': True},
+        'search_space': {'required': True},
+        'trial': {'required': True},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'component_id': {'key': 'componentId', 'type': 'str'},
+        'compute_id': {'key': 'computeId', 'type': 'str'},
+        'display_name': {'key': 'displayName', 'type': 'str'},
+        'experiment_name': {'key': 'experimentName', 'type': 'str'},
+        'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'job_type': {'key': 'jobType', 'type': 'str'},
+        'services': {'key': 'services', 'type': '{JobService}'},
+        'status': {'key': 'status', 'type': 'str'},
+        'early_termination': {'key': 'earlyTermination', 'type': 'EarlyTerminationPolicy'},
+        'inputs': {'key': 'inputs', 'type': '{JobInput}'},
+        'limits': {'key': 'limits', 'type': 'SweepJobLimits'},
+        'objective': {'key': 'objective', 'type': 'Objective'},
+        'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
+        'sampling_algorithm': {'key': 'samplingAlgorithm', 'type': 'SamplingAlgorithm'},
+        'search_space': {'key': 'searchSpace', 'type': 'object'},
+        'trial': {'key': 'trial', 'type': 'TrialComponent'},
+    }
+
+    def __init__(
+        self,
+        *,
+        objective: "Objective",
+        sampling_algorithm: "SamplingAlgorithm",
+        search_space: Any,
+        trial: "TrialComponent",
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        component_id: Optional[str] = None,
+        compute_id: Optional[str] = None,
+        display_name: Optional[str] = None,
+        experiment_name: Optional[str] = "Default",
+        identity: Optional["IdentityConfiguration"] = None,
+        is_archived: Optional[bool] = False,
+        services: Optional[Dict[str, "JobService"]] = None,
+        early_termination: Optional["EarlyTerminationPolicy"] = None,
+        inputs: Optional[Dict[str, "JobInput"]] = None,
+        limits: Optional["SweepJobLimits"] = None,
+        outputs: Optional[Dict[str, "JobOutput"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword early_termination: Early termination policies enable canceling poor-performing runs
+         before they complete.
+        :paramtype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+        :keyword inputs: Mapping of input data bindings used in the job.
+        :paramtype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+        :keyword limits: Sweep Job limit.
+        :paramtype limits: ~azure.mgmt.machinelearningservices.models.SweepJobLimits
+        :keyword objective: Required. [Required] Optimization objective.
+        :paramtype objective: ~azure.mgmt.machinelearningservices.models.Objective
+        :keyword outputs: Mapping of output data bindings used in the job.
+        :paramtype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+        :keyword sampling_algorithm: Required. [Required] The hyperparameter sampling algorithm.
+        :paramtype sampling_algorithm: ~azure.mgmt.machinelearningservices.models.SamplingAlgorithm
+        :keyword search_space: Required. [Required] A dictionary containing each parameter and its
+         distribution. The dictionary key is the name of the parameter.
+        :paramtype search_space: any
+        :keyword trial: Required. [Required] Trial component definition.
+        :paramtype trial: ~azure.mgmt.machinelearningservices.models.TrialComponent
+        """
+        super(SweepJob, self).__init__(description=description, properties=properties, tags=tags, component_id=component_id, compute_id=compute_id, display_name=display_name, experiment_name=experiment_name, identity=identity, is_archived=is_archived, services=services, **kwargs)
+        self.job_type = 'Sweep'  # type: str
+        self.early_termination = early_termination
+        self.inputs = inputs
+        self.limits = limits
+        self.objective = objective
+        self.outputs = outputs
+        self.sampling_algorithm = sampling_algorithm
+        self.search_space = search_space
+        self.trial = trial
+
+
+class SweepJobLimits(JobLimits):
+    """Sweep Job limit class.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar job_limits_type: Required. [Required] JobLimit type.Constant filled by server. Possible
+     values include: "Command", "Sweep".
+    :vartype job_limits_type: str or ~azure.mgmt.machinelearningservices.models.JobLimitsType
+    :ivar timeout: The max run duration in ISO 8601 format, after which the job will be cancelled.
+     Only supports duration with precision as low as Seconds.
+    :vartype timeout: ~datetime.timedelta
+    :ivar max_concurrent_trials: Sweep Job max concurrent trials.
+    :vartype max_concurrent_trials: int
+    :ivar max_total_trials: Sweep Job max total trials.
+    :vartype max_total_trials: int
+    :ivar trial_timeout: Sweep Job Trial timeout value.
+    :vartype trial_timeout: ~datetime.timedelta
+    """
+
+    _validation = {
+        'job_limits_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'job_limits_type': {'key': 'jobLimitsType', 'type': 'str'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+        'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
+        'max_total_trials': {'key': 'maxTotalTrials', 'type': 'int'},
+        'trial_timeout': {'key': 'trialTimeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        timeout: Optional[datetime.timedelta] = None,
+        max_concurrent_trials: Optional[int] = None,
+        max_total_trials: Optional[int] = None,
+        trial_timeout: Optional[datetime.timedelta] = None,
+        **kwargs
+    ):
+        """
+        :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
+         cancelled. Only supports duration with precision as low as Seconds.
+        :paramtype timeout: ~datetime.timedelta
+        :keyword max_concurrent_trials: Sweep Job max concurrent trials.
+        :paramtype max_concurrent_trials: int
+        :keyword max_total_trials: Sweep Job max total trials.
+        :paramtype max_total_trials: int
+        :keyword trial_timeout: Sweep Job Trial timeout value.
+        :paramtype trial_timeout: ~datetime.timedelta
+        """
+        super(SweepJobLimits, self).__init__(timeout=timeout, **kwargs)
+        self.job_limits_type = 'Sweep'  # type: str
+        self.max_concurrent_trials = max_concurrent_trials
+        self.max_total_trials = max_total_trials
+        self.trial_timeout = trial_timeout
+
+
+class SynapseSpark(Compute):
+    """A SynapseSpark compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    :ivar properties:
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.SynapseSparkProperties
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+        'properties': {'key': 'properties', 'type': 'SynapseSparkProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        properties: Optional["SynapseSparkProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        :keyword properties:
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.SynapseSparkProperties
+        """
+        super(SynapseSpark, self).__init__(description=description, resource_id=resource_id, **kwargs)
+        self.compute_type = 'SynapseSpark'  # type: str
+        self.properties = properties
+
+
+class SynapseSparkProperties(msrest.serialization.Model):
+    """SynapseSparkProperties.
+
+    :ivar auto_scale_properties: Auto scale properties.
+    :vartype auto_scale_properties: ~azure.mgmt.machinelearningservices.models.AutoScaleProperties
+    :ivar auto_pause_properties: Auto pause properties.
+    :vartype auto_pause_properties: ~azure.mgmt.machinelearningservices.models.AutoPauseProperties
+    :ivar spark_version: Spark version.
+    :vartype spark_version: str
+    :ivar node_count: The number of compute nodes currently assigned to the compute.
+    :vartype node_count: int
+    :ivar node_size: Node size.
+    :vartype node_size: str
+    :ivar node_size_family: Node size family.
+    :vartype node_size_family: str
+    :ivar subscription_id: Azure subscription identifier.
+    :vartype subscription_id: str
+    :ivar resource_group: Name of the resource group in which workspace is located.
+    :vartype resource_group: str
+    :ivar workspace_name: Name of Azure Machine Learning workspace.
+    :vartype workspace_name: str
+    :ivar pool_name: Pool name.
+    :vartype pool_name: str
+    """
+
+    _attribute_map = {
+        'auto_scale_properties': {'key': 'autoScaleProperties', 'type': 'AutoScaleProperties'},
+        'auto_pause_properties': {'key': 'autoPauseProperties', 'type': 'AutoPauseProperties'},
+        'spark_version': {'key': 'sparkVersion', 'type': 'str'},
+        'node_count': {'key': 'nodeCount', 'type': 'int'},
+        'node_size': {'key': 'nodeSize', 'type': 'str'},
+        'node_size_family': {'key': 'nodeSizeFamily', 'type': 'str'},
+        'subscription_id': {'key': 'subscriptionId', 'type': 'str'},
+        'resource_group': {'key': 'resourceGroup', 'type': 'str'},
+        'workspace_name': {'key': 'workspaceName', 'type': 'str'},
+        'pool_name': {'key': 'poolName', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        auto_scale_properties: Optional["AutoScaleProperties"] = None,
+        auto_pause_properties: Optional["AutoPauseProperties"] = None,
+        spark_version: Optional[str] = None,
+        node_count: Optional[int] = None,
+        node_size: Optional[str] = None,
+        node_size_family: Optional[str] = None,
+        subscription_id: Optional[str] = None,
+        resource_group: Optional[str] = None,
+        workspace_name: Optional[str] = None,
+        pool_name: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword auto_scale_properties: Auto scale properties.
+        :paramtype auto_scale_properties:
+         ~azure.mgmt.machinelearningservices.models.AutoScaleProperties
+        :keyword auto_pause_properties: Auto pause properties.
+        :paramtype auto_pause_properties:
+         ~azure.mgmt.machinelearningservices.models.AutoPauseProperties
+        :keyword spark_version: Spark version.
+        :paramtype spark_version: str
+        :keyword node_count: The number of compute nodes currently assigned to the compute.
+        :paramtype node_count: int
+        :keyword node_size: Node size.
+        :paramtype node_size: str
+        :keyword node_size_family: Node size family.
+        :paramtype node_size_family: str
+        :keyword subscription_id: Azure subscription identifier.
+        :paramtype subscription_id: str
+        :keyword resource_group: Name of the resource group in which workspace is located.
+        :paramtype resource_group: str
+        :keyword workspace_name: Name of Azure Machine Learning workspace.
+        :paramtype workspace_name: str
+        :keyword pool_name: Pool name.
+        :paramtype pool_name: str
+        """
+        super(SynapseSparkProperties, self).__init__(**kwargs)
+        self.auto_scale_properties = auto_scale_properties
+        self.auto_pause_properties = auto_pause_properties
+        self.spark_version = spark_version
+        self.node_count = node_count
+        self.node_size = node_size
+        self.node_size_family = node_size_family
+        self.subscription_id = subscription_id
+        self.resource_group = resource_group
+        self.workspace_name = workspace_name
+        self.pool_name = pool_name
+
+
+class SystemData(msrest.serialization.Model):
+    """Metadata pertaining to creation and last modification of the resource.
+
+    :ivar created_by: The identity that created the resource.
+    :vartype created_by: str
+    :ivar created_by_type: The type of identity that created the resource. Possible values include:
+     "User", "Application", "ManagedIdentity", "Key".
+    :vartype created_by_type: str or ~azure.mgmt.machinelearningservices.models.CreatedByType
+    :ivar created_at: The timestamp of resource creation (UTC).
+    :vartype created_at: ~datetime.datetime
+    :ivar last_modified_by: The identity that last modified the resource.
+    :vartype last_modified_by: str
+    :ivar last_modified_by_type: The type of identity that last modified the resource. Possible
+     values include: "User", "Application", "ManagedIdentity", "Key".
+    :vartype last_modified_by_type: str or ~azure.mgmt.machinelearningservices.models.CreatedByType
+    :ivar last_modified_at: The timestamp of resource last modification (UTC).
+    :vartype last_modified_at: ~datetime.datetime
+    """
+
+    _attribute_map = {
+        'created_by': {'key': 'createdBy', 'type': 'str'},
+        'created_by_type': {'key': 'createdByType', 'type': 'str'},
+        'created_at': {'key': 'createdAt', 'type': 'iso-8601'},
+        'last_modified_by': {'key': 'lastModifiedBy', 'type': 'str'},
+        'last_modified_by_type': {'key': 'lastModifiedByType', 'type': 'str'},
+        'last_modified_at': {'key': 'lastModifiedAt', 'type': 'iso-8601'},
+    }
+
+    def __init__(
+        self,
+        *,
+        created_by: Optional[str] = None,
+        created_by_type: Optional[Union[str, "CreatedByType"]] = None,
+        created_at: Optional[datetime.datetime] = None,
+        last_modified_by: Optional[str] = None,
+        last_modified_by_type: Optional[Union[str, "CreatedByType"]] = None,
+        last_modified_at: Optional[datetime.datetime] = None,
+        **kwargs
+    ):
+        """
+        :keyword created_by: The identity that created the resource.
+        :paramtype created_by: str
+        :keyword created_by_type: The type of identity that created the resource. Possible values
+         include: "User", "Application", "ManagedIdentity", "Key".
+        :paramtype created_by_type: str or ~azure.mgmt.machinelearningservices.models.CreatedByType
+        :keyword created_at: The timestamp of resource creation (UTC).
+        :paramtype created_at: ~datetime.datetime
+        :keyword last_modified_by: The identity that last modified the resource.
+        :paramtype last_modified_by: str
+        :keyword last_modified_by_type: The type of identity that last modified the resource. Possible
+         values include: "User", "Application", "ManagedIdentity", "Key".
+        :paramtype last_modified_by_type: str or
+         ~azure.mgmt.machinelearningservices.models.CreatedByType
+        :keyword last_modified_at: The timestamp of resource last modification (UTC).
+        :paramtype last_modified_at: ~datetime.datetime
+        """
+        super(SystemData, self).__init__(**kwargs)
+        self.created_by = created_by
+        self.created_by_type = created_by_type
+        self.created_at = created_at
+        self.last_modified_by = last_modified_by
+        self.last_modified_by_type = last_modified_by_type
+        self.last_modified_at = last_modified_at
+
+
+class SystemService(msrest.serialization.Model):
+    """A system service running on a compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar system_service_type: The type of this system service.
+    :vartype system_service_type: str
+    :ivar public_ip_address: Public IP address.
+    :vartype public_ip_address: str
+    :ivar version: The version for this type.
+    :vartype version: str
+    """
+
+    _validation = {
+        'system_service_type': {'readonly': True},
+        'public_ip_address': {'readonly': True},
+        'version': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'system_service_type': {'key': 'systemServiceType', 'type': 'str'},
+        'public_ip_address': {'key': 'publicIpAddress', 'type': 'str'},
+        'version': {'key': 'version', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(SystemService, self).__init__(**kwargs)
+        self.system_service_type = None
+        self.public_ip_address = None
+        self.version = None
+
+
+class TableVerticalFeaturizationSettings(FeaturizationSettings):
+    """Featurization Configuration.
+
+    :ivar dataset_language: Dataset language, useful for the text data.
+    :vartype dataset_language: str
+    :ivar blocked_transformers: These transformers shall not be used in featurization.
+    :vartype blocked_transformers: list[str or
+     ~azure.mgmt.machinelearningservices.models.BlockedTransformers]
+    :ivar column_name_and_types: Dictionary of column name and its type (int, float, string,
+     datetime etc).
+    :vartype column_name_and_types: dict[str, str]
+    :ivar enable_dnn_featurization: Determines whether to use Dnn based featurizers for data
+     featurization.
+    :vartype enable_dnn_featurization: bool
+    :ivar mode: Featurization mode - User can keep the default 'Auto' mode and AutoML will take
+     care of necessary transformation of the data in featurization phase.
+     If 'Off' is selected then no featurization is done.
+     If 'Custom' is selected then user can specify additional inputs to customize how featurization
+     is done. Possible values include: "Auto", "Custom", "Off".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.FeaturizationMode
+    :ivar transformer_params: User can specify additional transformers to be used along with the
+     columns to which it would be applied and parameters for the transformer constructor.
+    :vartype transformer_params: dict[str,
+     list[~azure.mgmt.machinelearningservices.models.ColumnTransformer]]
+    """
+
+    _attribute_map = {
+        'dataset_language': {'key': 'datasetLanguage', 'type': 'str'},
+        'blocked_transformers': {'key': 'blockedTransformers', 'type': '[str]'},
+        'column_name_and_types': {'key': 'columnNameAndTypes', 'type': '{str}'},
+        'enable_dnn_featurization': {'key': 'enableDnnFeaturization', 'type': 'bool'},
+        'mode': {'key': 'mode', 'type': 'str'},
+        'transformer_params': {'key': 'transformerParams', 'type': '{[ColumnTransformer]}'},
+    }
+
+    def __init__(
+        self,
+        *,
+        dataset_language: Optional[str] = None,
+        blocked_transformers: Optional[List[Union[str, "BlockedTransformers"]]] = None,
+        column_name_and_types: Optional[Dict[str, str]] = None,
+        enable_dnn_featurization: Optional[bool] = False,
+        mode: Optional[Union[str, "FeaturizationMode"]] = None,
+        transformer_params: Optional[Dict[str, List["ColumnTransformer"]]] = None,
+        **kwargs
+    ):
+        """
+        :keyword dataset_language: Dataset language, useful for the text data.
+        :paramtype dataset_language: str
+        :keyword blocked_transformers: These transformers shall not be used in featurization.
+        :paramtype blocked_transformers: list[str or
+         ~azure.mgmt.machinelearningservices.models.BlockedTransformers]
+        :keyword column_name_and_types: Dictionary of column name and its type (int, float, string,
+         datetime etc).
+        :paramtype column_name_and_types: dict[str, str]
+        :keyword enable_dnn_featurization: Determines whether to use Dnn based featurizers for data
+         featurization.
+        :paramtype enable_dnn_featurization: bool
+        :keyword mode: Featurization mode - User can keep the default 'Auto' mode and AutoML will take
+         care of necessary transformation of the data in featurization phase.
+         If 'Off' is selected then no featurization is done.
+         If 'Custom' is selected then user can specify additional inputs to customize how featurization
+         is done. Possible values include: "Auto", "Custom", "Off".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.FeaturizationMode
+        :keyword transformer_params: User can specify additional transformers to be used along with the
+         columns to which it would be applied and parameters for the transformer constructor.
+        :paramtype transformer_params: dict[str,
+         list[~azure.mgmt.machinelearningservices.models.ColumnTransformer]]
+        """
+        super(TableVerticalFeaturizationSettings, self).__init__(dataset_language=dataset_language, **kwargs)
+        self.blocked_transformers = blocked_transformers
+        self.column_name_and_types = column_name_and_types
+        self.enable_dnn_featurization = enable_dnn_featurization
+        self.mode = mode
+        self.transformer_params = transformer_params
+
+
+class TableVerticalLimitSettings(msrest.serialization.Model):
+    """Job execution constraints.
+
+    :ivar enable_early_termination: Enable early termination, determines whether or not if
+     AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
+    :vartype enable_early_termination: bool
+    :ivar exit_score: Exit score for the AutoML job.
+    :vartype exit_score: float
+    :ivar max_concurrent_trials: Maximum Concurrent iterations.
+    :vartype max_concurrent_trials: int
+    :ivar max_cores_per_trial: Max cores per iteration.
+    :vartype max_cores_per_trial: int
+    :ivar max_trials: Number of iterations.
+    :vartype max_trials: int
+    :ivar timeout: AutoML job timeout.
+    :vartype timeout: ~datetime.timedelta
+    :ivar trial_timeout: Iteration timeout.
+    :vartype trial_timeout: ~datetime.timedelta
+    """
+
+    _attribute_map = {
+        'enable_early_termination': {'key': 'enableEarlyTermination', 'type': 'bool'},
+        'exit_score': {'key': 'exitScore', 'type': 'float'},
+        'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
+        'max_cores_per_trial': {'key': 'maxCoresPerTrial', 'type': 'int'},
+        'max_trials': {'key': 'maxTrials', 'type': 'int'},
+        'timeout': {'key': 'timeout', 'type': 'duration'},
+        'trial_timeout': {'key': 'trialTimeout', 'type': 'duration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        enable_early_termination: Optional[bool] = True,
+        exit_score: Optional[float] = None,
+        max_concurrent_trials: Optional[int] = 1,
+        max_cores_per_trial: Optional[int] = -1,
+        max_trials: Optional[int] = 1000,
+        timeout: Optional[datetime.timedelta] = "PT6H",
+        trial_timeout: Optional[datetime.timedelta] = "PT30M",
+        **kwargs
+    ):
+        """
+        :keyword enable_early_termination: Enable early termination, determines whether or not if
+         AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
+        :paramtype enable_early_termination: bool
+        :keyword exit_score: Exit score for the AutoML job.
+        :paramtype exit_score: float
+        :keyword max_concurrent_trials: Maximum Concurrent iterations.
+        :paramtype max_concurrent_trials: int
+        :keyword max_cores_per_trial: Max cores per iteration.
+        :paramtype max_cores_per_trial: int
+        :keyword max_trials: Number of iterations.
+        :paramtype max_trials: int
+        :keyword timeout: AutoML job timeout.
+        :paramtype timeout: ~datetime.timedelta
+        :keyword trial_timeout: Iteration timeout.
+        :paramtype trial_timeout: ~datetime.timedelta
+        """
+        super(TableVerticalLimitSettings, self).__init__(**kwargs)
+        self.enable_early_termination = enable_early_termination
+        self.exit_score = exit_score
+        self.max_concurrent_trials = max_concurrent_trials
+        self.max_cores_per_trial = max_cores_per_trial
+        self.max_trials = max_trials
+        self.timeout = timeout
+        self.trial_timeout = trial_timeout
+
+
+class TargetUtilizationScaleSettings(OnlineScaleSettings):
+    """TargetUtilizationScaleSettings.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar scale_type: Required. [Required] Type of deployment scaling algorithm.Constant filled by
+     server. Possible values include: "Default", "TargetUtilization".
+    :vartype scale_type: str or ~azure.mgmt.machinelearningservices.models.ScaleType
+    :ivar max_instances: The maximum number of instances that the deployment can scale to. The
+     quota will be reserved for max_instances.
+    :vartype max_instances: int
+    :ivar min_instances: The minimum number of instances to always be present.
+    :vartype min_instances: int
+    :ivar polling_interval: The polling interval in ISO 8691 format. Only supports duration with
+     precision as low as Seconds.
+    :vartype polling_interval: ~datetime.timedelta
+    :ivar target_utilization_percentage: Target CPU usage for the autoscaler.
+    :vartype target_utilization_percentage: int
+    """
+
+    _validation = {
+        'scale_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'scale_type': {'key': 'scaleType', 'type': 'str'},
+        'max_instances': {'key': 'maxInstances', 'type': 'int'},
+        'min_instances': {'key': 'minInstances', 'type': 'int'},
+        'polling_interval': {'key': 'pollingInterval', 'type': 'duration'},
+        'target_utilization_percentage': {'key': 'targetUtilizationPercentage', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        max_instances: Optional[int] = 1,
+        min_instances: Optional[int] = 1,
+        polling_interval: Optional[datetime.timedelta] = "PT1S",
+        target_utilization_percentage: Optional[int] = 70,
+        **kwargs
+    ):
+        """
+        :keyword max_instances: The maximum number of instances that the deployment can scale to. The
+         quota will be reserved for max_instances.
+        :paramtype max_instances: int
+        :keyword min_instances: The minimum number of instances to always be present.
+        :paramtype min_instances: int
+        :keyword polling_interval: The polling interval in ISO 8691 format. Only supports duration with
+         precision as low as Seconds.
+        :paramtype polling_interval: ~datetime.timedelta
+        :keyword target_utilization_percentage: Target CPU usage for the autoscaler.
+        :paramtype target_utilization_percentage: int
+        """
+        super(TargetUtilizationScaleSettings, self).__init__(**kwargs)
+        self.scale_type = 'TargetUtilization'  # type: str
+        self.max_instances = max_instances
+        self.min_instances = min_instances
+        self.polling_interval = polling_interval
+        self.target_utilization_percentage = target_utilization_percentage
+
+
+class TensorFlow(DistributionConfiguration):
+    """TensorFlow distribution configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar distribution_type: Required. [Required] Specifies the type of distribution
+     framework.Constant filled by server. Possible values include: "PyTorch", "TensorFlow", "Mpi".
+    :vartype distribution_type: str or ~azure.mgmt.machinelearningservices.models.DistributionType
+    :ivar parameter_server_count: Number of parameter server tasks.
+    :vartype parameter_server_count: int
+    :ivar worker_count: Number of workers. If not specified, will default to the instance count.
+    :vartype worker_count: int
+    """
+
+    _validation = {
+        'distribution_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'distribution_type': {'key': 'distributionType', 'type': 'str'},
+        'parameter_server_count': {'key': 'parameterServerCount', 'type': 'int'},
+        'worker_count': {'key': 'workerCount', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        parameter_server_count: Optional[int] = 0,
+        worker_count: Optional[int] = None,
+        **kwargs
+    ):
+        """
+        :keyword parameter_server_count: Number of parameter server tasks.
+        :paramtype parameter_server_count: int
+        :keyword worker_count: Number of workers. If not specified, will default to the instance count.
+        :paramtype worker_count: int
+        """
+        super(TensorFlow, self).__init__(**kwargs)
+        self.distribution_type = 'TensorFlow'  # type: str
+        self.parameter_server_count = parameter_server_count
+        self.worker_count = worker_count
+
+
+class TextClassification(AutoMLVertical, NlpVertical):
+    """Text Classification task in AutoML NLP vertical.
+NLP - Natural Language Processing.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-Classification task. Possible values include:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+    }
+
+    _attribute_map = {
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'NlpVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'NlpVerticalLimitSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        training_data: "MLTableJobInput",
+        featurization_settings: Optional["NlpVerticalFeaturizationSettings"] = None,
+        limit_settings: Optional["NlpVerticalLimitSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        primary_metric: Optional[Union[str, "ClassificationPrimaryMetrics"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword primary_metric: Primary metric for Text-Classification task. Possible values include:
+         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+         "PrecisionScoreWeighted".
+        :paramtype primary_metric: str or
+         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+        """
+        super(TextClassification, self).__init__(log_verbosity=log_verbosity, target_column_name=target_column_name, training_data=training_data, featurization_settings=featurization_settings, limit_settings=limit_settings, validation_data=validation_data, **kwargs)
+        self.featurization_settings = featurization_settings
+        self.limit_settings = limit_settings
+        self.validation_data = validation_data
+        self.task_type = 'TextClassification'  # type: str
+        self.primary_metric = primary_metric
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+
+
+class TextClassificationMultilabel(AutoMLVertical, NlpVertical):
+    """Text Classification Multilabel task in AutoML NLP vertical.
+NLP - Natural Language Processing.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-Classification-Multilabel task.
+     Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
+     Possible values include: "AUCWeighted", "Accuracy", "NormMacroRecall",
+     "AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", "IOU".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+        'primary_metric': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'NlpVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'NlpVerticalLimitSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        training_data: "MLTableJobInput",
+        featurization_settings: Optional["NlpVerticalFeaturizationSettings"] = None,
+        limit_settings: Optional["NlpVerticalLimitSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        """
+        super(TextClassificationMultilabel, self).__init__(log_verbosity=log_verbosity, target_column_name=target_column_name, training_data=training_data, featurization_settings=featurization_settings, limit_settings=limit_settings, validation_data=validation_data, **kwargs)
+        self.featurization_settings = featurization_settings
+        self.limit_settings = limit_settings
+        self.validation_data = validation_data
+        self.task_type = 'TextClassificationMultilabel'  # type: str
+        self.primary_metric = None
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+
+
+class TextNer(AutoMLVertical, NlpVertical):
+    """Text-NER task in AutoML NLP vertical.
+NER - Named Entity Recognition.
+NLP - Natural Language Processing.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+     "Info", "Warning", "Error", "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: Required. [Required] Task type for AutoMLJob.Constant filled by server.
+     Possible values include: "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: Required. [Required] Training data input.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-NER task.
+     Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly. Possible
+     values include: "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    """
+
+    _validation = {
+        'task_type': {'required': True},
+        'training_data': {'required': True},
+        'primary_metric': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'featurization_settings': {'key': 'featurizationSettings', 'type': 'NlpVerticalFeaturizationSettings'},
+        'limit_settings': {'key': 'limitSettings', 'type': 'NlpVerticalLimitSettings'},
+        'validation_data': {'key': 'validationData', 'type': 'MLTableJobInput'},
+        'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
+        'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
+        'task_type': {'key': 'taskType', 'type': 'str'},
+        'training_data': {'key': 'trainingData', 'type': 'MLTableJobInput'},
+        'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        training_data: "MLTableJobInput",
+        featurization_settings: Optional["NlpVerticalFeaturizationSettings"] = None,
+        limit_settings: Optional["NlpVerticalLimitSettings"] = None,
+        validation_data: Optional["MLTableJobInput"] = None,
+        log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
+        target_column_name: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword featurization_settings: Featurization inputs needed for AutoML job.
+        :paramtype featurization_settings:
+         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword limit_settings: Execution constraints for AutoMLJob.
+        :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword validation_data: Validation data inputs.
+        :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        :keyword log_verbosity: Log verbosity for the job. Possible values include: "NotSet", "Debug",
+         "Info", "Warning", "Error", "Critical".
+        :paramtype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        :keyword target_column_name: Target column name: This is prediction values column.
+         Also known as label column name in context of classification tasks.
+        :paramtype target_column_name: str
+        :keyword training_data: Required. [Required] Training data input.
+        :paramtype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+        """
+        super(TextNer, self).__init__(log_verbosity=log_verbosity, target_column_name=target_column_name, training_data=training_data, featurization_settings=featurization_settings, limit_settings=limit_settings, validation_data=validation_data, **kwargs)
+        self.featurization_settings = featurization_settings
+        self.limit_settings = limit_settings
+        self.validation_data = validation_data
+        self.task_type = 'TextNER'  # type: str
+        self.primary_metric = None
+        self.log_verbosity = log_verbosity
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+
+
+class TrialComponent(msrest.serialization.Model):
+    """Trial component definition.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar code_id: ARM resource ID of the code asset.
+    :vartype code_id: str
+    :ivar command: Required. [Required] The command to execute on startup of the job. eg. "python
+     train.py".
+    :vartype command: str
+    :ivar distribution: Distribution configuration of the job. If set, this should be one of Mpi,
+     Tensorflow, PyTorch, or null.
+    :vartype distribution: ~azure.mgmt.machinelearningservices.models.DistributionConfiguration
+    :ivar environment_id: Required. [Required] The ARM resource ID of the Environment specification
+     for the job.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables included in the job.
+    :vartype environment_variables: dict[str, str]
+    :ivar resources: Compute Resource configuration for the job.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+    """
+
+    _validation = {
+        'command': {'required': True, 'min_length': 1, 'pattern': r'[a-zA-Z0-9_]'},
+        'environment_id': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'code_id': {'key': 'codeId', 'type': 'str'},
+        'command': {'key': 'command', 'type': 'str'},
+        'distribution': {'key': 'distribution', 'type': 'DistributionConfiguration'},
+        'environment_id': {'key': 'environmentId', 'type': 'str'},
+        'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
+        'resources': {'key': 'resources', 'type': 'JobResourceConfiguration'},
+    }
+
+    def __init__(
+        self,
+        *,
+        command: str,
+        environment_id: str,
+        code_id: Optional[str] = None,
+        distribution: Optional["DistributionConfiguration"] = None,
+        environment_variables: Optional[Dict[str, str]] = None,
+        resources: Optional["JobResourceConfiguration"] = None,
+        **kwargs
+    ):
+        """
+        :keyword code_id: ARM resource ID of the code asset.
+        :paramtype code_id: str
+        :keyword command: Required. [Required] The command to execute on startup of the job. eg.
+         "python train.py".
+        :paramtype command: str
+        :keyword distribution: Distribution configuration of the job. If set, this should be one of
+         Mpi, Tensorflow, PyTorch, or null.
+        :paramtype distribution: ~azure.mgmt.machinelearningservices.models.DistributionConfiguration
+        :keyword environment_id: Required. [Required] The ARM resource ID of the Environment
+         specification for the job.
+        :paramtype environment_id: str
+        :keyword environment_variables: Environment variables included in the job.
+        :paramtype environment_variables: dict[str, str]
+        :keyword resources: Compute Resource configuration for the job.
+        :paramtype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+        """
+        super(TrialComponent, self).__init__(**kwargs)
+        self.code_id = code_id
+        self.command = command
+        self.distribution = distribution
+        self.environment_id = environment_id
+        self.environment_variables = environment_variables
+        self.resources = resources
+
+
+class TritonModelJobInput(JobInput, AssetJobInput):
+    """TritonModelJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        uri: str,
+        mode: Optional[Union[str, "InputDeliveryMode"]] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(TritonModelJobInput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_input_type = 'triton_model'  # type: str
+        self.description = description
+
+
+class TritonModelJobOutput(JobOutput, AssetJobOutput):
+    """TritonModelJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        mode: Optional[Union[str, "OutputDeliveryMode"]] = None,
+        uri: Optional[str] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(TritonModelJobOutput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_output_type = 'triton_model'  # type: str
+        self.description = description
+
+
+class TruncationSelectionPolicy(EarlyTerminationPolicy):
+    """Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar delay_evaluation: Number of intervals by which to delay the first evaluation.
+    :vartype delay_evaluation: int
+    :ivar evaluation_interval: Interval (number of runs) between policy evaluations.
+    :vartype evaluation_interval: int
+    :ivar policy_type: Required. [Required] Name of policy configuration.Constant filled by server.
+     Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
+    :vartype policy_type: str or
+     ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicyType
+    :ivar truncation_percentage: The percentage of runs to cancel at each evaluation interval.
+    :vartype truncation_percentage: int
+    """
+
+    _validation = {
+        'policy_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
+        'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
+        'policy_type': {'key': 'policyType', 'type': 'str'},
+        'truncation_percentage': {'key': 'truncationPercentage', 'type': 'int'},
+    }
+
+    def __init__(
+        self,
+        *,
+        delay_evaluation: Optional[int] = 0,
+        evaluation_interval: Optional[int] = 0,
+        truncation_percentage: Optional[int] = 0,
+        **kwargs
+    ):
+        """
+        :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
+        :paramtype delay_evaluation: int
+        :keyword evaluation_interval: Interval (number of runs) between policy evaluations.
+        :paramtype evaluation_interval: int
+        :keyword truncation_percentage: The percentage of runs to cancel at each evaluation interval.
+        :paramtype truncation_percentage: int
+        """
+        super(TruncationSelectionPolicy, self).__init__(delay_evaluation=delay_evaluation, evaluation_interval=evaluation_interval, **kwargs)
+        self.policy_type = 'TruncationSelection'  # type: str
+        self.truncation_percentage = truncation_percentage
+
+
+class UpdateWorkspaceQuotas(msrest.serialization.Model):
+    """The properties for update Quota response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Specifies the resource ID.
+    :vartype id: str
+    :ivar type: Specifies the resource type.
+    :vartype type: str
+    :ivar limit: The maximum permitted quota of the resource.
+    :vartype limit: long
+    :ivar unit: An enum describing the unit of quota measurement. Possible values include: "Count".
+    :vartype unit: str or ~azure.mgmt.machinelearningservices.models.QuotaUnit
+    :ivar status: Status of update workspace quota. Possible values include: "Undefined",
+     "Success", "Failure", "InvalidQuotaBelowClusterMinimum",
+     "InvalidQuotaExceedsSubscriptionLimit", "InvalidVMFamilyName", "OperationNotSupportedForSku",
+     "OperationNotEnabledForRegion".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.Status
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'type': {'readonly': True},
+        'unit': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'limit': {'key': 'limit', 'type': 'long'},
+        'unit': {'key': 'unit', 'type': 'str'},
+        'status': {'key': 'status', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        limit: Optional[int] = None,
+        status: Optional[Union[str, "Status"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword limit: The maximum permitted quota of the resource.
+        :paramtype limit: long
+        :keyword status: Status of update workspace quota. Possible values include: "Undefined",
+         "Success", "Failure", "InvalidQuotaBelowClusterMinimum",
+         "InvalidQuotaExceedsSubscriptionLimit", "InvalidVMFamilyName", "OperationNotSupportedForSku",
+         "OperationNotEnabledForRegion".
+        :paramtype status: str or ~azure.mgmt.machinelearningservices.models.Status
+        """
+        super(UpdateWorkspaceQuotas, self).__init__(**kwargs)
+        self.id = None
+        self.type = None
+        self.limit = limit
+        self.unit = None
+        self.status = status
+
+
+class UpdateWorkspaceQuotasResult(msrest.serialization.Model):
+    """The result of update workspace quota.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of workspace quota update result.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.UpdateWorkspaceQuotas]
+    :ivar next_link: The URI to fetch the next page of workspace quota update result. Call
+     ListNext() with this to fetch the next page of Workspace Quota update result.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[UpdateWorkspaceQuotas]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(UpdateWorkspaceQuotasResult, self).__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class UriFileDataVersion(DataVersionBaseProperties):
+    """uri-file data version entity.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar data_type: Required. [Required] Specifies the type of data.Constant filled by server.
+     Possible values include: "uri_file", "uri_folder", "mltable".
+    :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+    :ivar data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+    :vartype data_uri: str
+    """
+
+    _validation = {
+        'data_type': {'required': True},
+        'data_uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'data_type': {'key': 'dataType', 'type': 'str'},
+        'data_uri': {'key': 'dataUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        data_uri: str,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_anonymous: Optional[bool] = False,
+        is_archived: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+        :paramtype data_uri: str
+        """
+        super(UriFileDataVersion, self).__init__(description=description, properties=properties, tags=tags, is_anonymous=is_anonymous, is_archived=is_archived, data_uri=data_uri, **kwargs)
+        self.data_type = 'uri_file'  # type: str
+
+
+class UriFileJobInput(JobInput, AssetJobInput):
+    """UriFileJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        uri: str,
+        mode: Optional[Union[str, "InputDeliveryMode"]] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(UriFileJobInput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_input_type = 'uri_file'  # type: str
+        self.description = description
+
+
+class UriFileJobOutput(JobOutput, AssetJobOutput):
+    """UriFileJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        mode: Optional[Union[str, "OutputDeliveryMode"]] = None,
+        uri: Optional[str] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(UriFileJobOutput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_output_type = 'uri_file'  # type: str
+        self.description = description
+
+
+class UriFolderDataVersion(DataVersionBaseProperties):
+    """uri-folder data version entity.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_anonymous: If the name version are system generated (anonymous registration).
+    :vartype is_anonymous: bool
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar data_type: Required. [Required] Specifies the type of data.Constant filled by server.
+     Possible values include: "uri_file", "uri_folder", "mltable".
+    :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
+    :ivar data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+    :vartype data_uri: str
+    """
+
+    _validation = {
+        'data_type': {'required': True},
+        'data_uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+    }
+
+    _attribute_map = {
+        'description': {'key': 'description', 'type': 'str'},
+        'properties': {'key': 'properties', 'type': '{str}'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
+        'is_archived': {'key': 'isArchived', 'type': 'bool'},
+        'data_type': {'key': 'dataType', 'type': 'str'},
+        'data_uri': {'key': 'dataUri', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        data_uri: str,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        is_anonymous: Optional[bool] = False,
+        is_archived: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: A set of tags. Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword is_anonymous: If the name version are system generated (anonymous registration).
+        :paramtype is_anonymous: bool
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword data_uri: Required. [Required] Uri of the data. Usage/meaning depends on
+         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
+        :paramtype data_uri: str
+        """
+        super(UriFolderDataVersion, self).__init__(description=description, properties=properties, tags=tags, is_anonymous=is_anonymous, is_archived=is_archived, data_uri=data_uri, **kwargs)
+        self.data_type = 'uri_folder'  # type: str
+
+
+class UriFolderJobInput(JobInput, AssetJobInput):
+    """UriFolderJobInput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+     "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+    :ivar uri: Required. [Required] Input Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: Required. [Required] Specifies the type of job.Constant filled by server.
+     Possible values include: "literal", "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    """
+
+    _validation = {
+        'uri': {'required': True, 'pattern': r'[a-zA-Z0-9_]'},
+        'job_input_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_input_type': {'key': 'jobInputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        uri: str,
+        mode: Optional[Union[str, "InputDeliveryMode"]] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Input Asset Delivery Mode. Possible values include: "ReadOnlyMount",
+         "ReadWriteMount", "Download", "Direct", "EvalMount", "EvalDownload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.InputDeliveryMode
+        :keyword uri: Required. [Required] Input Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the input.
+        :paramtype description: str
+        """
+        super(UriFolderJobInput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_input_type = 'uri_folder'  # type: str
+        self.description = description
+
+
+class UriFolderJobOutput(JobOutput, AssetJobOutput):
+    """UriFolderJobOutput.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+    :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+    :ivar uri: Output Asset URI.
+    :vartype uri: str
+    :ivar description: Description for the output.
+    :vartype description: str
+    :ivar job_output_type: Required. [Required] Specifies the type of job.Constant filled by
+     server. Possible values include: "uri_file", "uri_folder", "mltable", "custom_model",
+     "mlflow_model", "triton_model".
+    :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
+    """
+
+    _validation = {
+        'job_output_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'mode': {'key': 'mode', 'type': 'str'},
+        'uri': {'key': 'uri', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        mode: Optional[Union[str, "OutputDeliveryMode"]] = None,
+        uri: Optional[str] = None,
+        description: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword mode: Output Asset Delivery Mode. Possible values include: "ReadWriteMount", "Upload".
+        :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
+        :keyword uri: Output Asset URI.
+        :paramtype uri: str
+        :keyword description: Description for the output.
+        :paramtype description: str
+        """
+        super(UriFolderJobOutput, self).__init__(description=description, mode=mode, uri=uri, **kwargs)
+        self.mode = mode
+        self.uri = uri
+        self.job_output_type = 'uri_folder'  # type: str
+        self.description = description
+
+
+class Usage(msrest.serialization.Model):
+    """Describes AML Resource Usage.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Specifies the resource ID.
+    :vartype id: str
+    :ivar aml_workspace_location: Region of the AML workspace in the id.
+    :vartype aml_workspace_location: str
+    :ivar type: Specifies the resource type.
+    :vartype type: str
+    :ivar unit: An enum describing the unit of usage measurement. Possible values include: "Count".
+    :vartype unit: str or ~azure.mgmt.machinelearningservices.models.UsageUnit
+    :ivar current_value: The current usage of the resource.
+    :vartype current_value: long
+    :ivar limit: The maximum permitted usage of the resource.
+    :vartype limit: long
+    :ivar name: The name of the type of usage.
+    :vartype name: ~azure.mgmt.machinelearningservices.models.UsageName
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'aml_workspace_location': {'readonly': True},
+        'type': {'readonly': True},
+        'unit': {'readonly': True},
+        'current_value': {'readonly': True},
+        'limit': {'readonly': True},
+        'name': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'aml_workspace_location': {'key': 'amlWorkspaceLocation', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'unit': {'key': 'unit', 'type': 'str'},
+        'current_value': {'key': 'currentValue', 'type': 'long'},
+        'limit': {'key': 'limit', 'type': 'long'},
+        'name': {'key': 'name', 'type': 'UsageName'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(Usage, self).__init__(**kwargs)
+        self.id = None
+        self.aml_workspace_location = None
+        self.type = None
+        self.unit = None
+        self.current_value = None
+        self.limit = None
+        self.name = None
+
+
+class UsageName(msrest.serialization.Model):
+    """The Usage Names.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The name of the resource.
+    :vartype value: str
+    :ivar localized_value: The localized name of the resource.
+    :vartype localized_value: str
+    """
+
+    _validation = {
+        'value': {'readonly': True},
+        'localized_value': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': 'str'},
+        'localized_value': {'key': 'localizedValue', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(UsageName, self).__init__(**kwargs)
+        self.value = None
+        self.localized_value = None
+
+
+class UserAccountCredentials(msrest.serialization.Model):
+    """Settings for user account that gets created on each on the nodes of a compute.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar admin_user_name: Required. Name of the administrator user account which can be used to
+     SSH to nodes.
+    :vartype admin_user_name: str
+    :ivar admin_user_ssh_public_key: SSH public key of the administrator user account.
+    :vartype admin_user_ssh_public_key: str
+    :ivar admin_user_password: Password of the administrator user account.
+    :vartype admin_user_password: str
+    """
+
+    _validation = {
+        'admin_user_name': {'required': True},
+    }
+
+    _attribute_map = {
+        'admin_user_name': {'key': 'adminUserName', 'type': 'str'},
+        'admin_user_ssh_public_key': {'key': 'adminUserSshPublicKey', 'type': 'str'},
+        'admin_user_password': {'key': 'adminUserPassword', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        admin_user_name: str,
+        admin_user_ssh_public_key: Optional[str] = None,
+        admin_user_password: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword admin_user_name: Required. Name of the administrator user account which can be used to
+         SSH to nodes.
+        :paramtype admin_user_name: str
+        :keyword admin_user_ssh_public_key: SSH public key of the administrator user account.
+        :paramtype admin_user_ssh_public_key: str
+        :keyword admin_user_password: Password of the administrator user account.
+        :paramtype admin_user_password: str
+        """
+        super(UserAccountCredentials, self).__init__(**kwargs)
+        self.admin_user_name = admin_user_name
+        self.admin_user_ssh_public_key = admin_user_ssh_public_key
+        self.admin_user_password = admin_user_password
+
+
+class UserAssignedIdentity(msrest.serialization.Model):
+    """User assigned identity properties.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar principal_id: The principal ID of the assigned identity.
+    :vartype principal_id: str
+    :ivar client_id: The client ID of the assigned identity.
+    :vartype client_id: str
+    """
+
+    _validation = {
+        'principal_id': {'readonly': True},
+        'client_id': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'principal_id': {'key': 'principalId', 'type': 'str'},
+        'client_id': {'key': 'clientId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(UserAssignedIdentity, self).__init__(**kwargs)
+        self.principal_id = None
+        self.client_id = None
+
+
+class UserIdentity(IdentityConfiguration):
+    """User identity configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar identity_type: Required. [Required] Specifies the type of identity framework.Constant
+     filled by server. Possible values include: "Managed", "AMLToken", "UserIdentity".
+    :vartype identity_type: str or
+     ~azure.mgmt.machinelearningservices.models.IdentityConfigurationType
+    """
+
+    _validation = {
+        'identity_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'identity_type': {'key': 'identityType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        """
+        super(UserIdentity, self).__init__(**kwargs)
+        self.identity_type = 'UserIdentity'  # type: str
+
+
+class UsernamePasswordAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """UsernamePasswordAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Required. Authentication type of the connection target.Constant filled by
+     server. Possible values include: "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Possible values include: "PythonFeed",
+     "ContainerRegistry", "Git".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. Possible values include: "JSON".
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar credentials:
+    :vartype credentials:
+     ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionUsernamePassword
+    """
+
+    _validation = {
+        'auth_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'auth_type': {'key': 'authType', 'type': 'str'},
+        'category': {'key': 'category', 'type': 'str'},
+        'target': {'key': 'target', 'type': 'str'},
+        'value': {'key': 'value', 'type': 'str'},
+        'value_format': {'key': 'valueFormat', 'type': 'str'},
+        'credentials': {'key': 'credentials', 'type': 'WorkspaceConnectionUsernamePassword'},
+    }
+
+    def __init__(
+        self,
+        *,
+        category: Optional[Union[str, "ConnectionCategory"]] = None,
+        target: Optional[str] = None,
+        value: Optional[str] = None,
+        value_format: Optional[Union[str, "ValueFormat"]] = None,
+        credentials: Optional["WorkspaceConnectionUsernamePassword"] = None,
+        **kwargs
+    ):
+        """
+        :keyword category: Category of the connection. Possible values include: "PythonFeed",
+         "ContainerRegistry", "Git".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. Possible values include:
+         "JSON".
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword credentials:
+        :paramtype credentials:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionUsernamePassword
+        """
+        super(UsernamePasswordAuthTypeWorkspaceConnectionProperties, self).__init__(category=category, target=target, value=value, value_format=value_format, **kwargs)
+        self.auth_type = 'UsernamePassword'  # type: str
+        self.credentials = credentials
+
+
+class VirtualMachineSchema(msrest.serialization.Model):
+    """VirtualMachineSchema.
+
+    :ivar properties:
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.VirtualMachineSchemaProperties
+    """
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'VirtualMachineSchemaProperties'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["VirtualMachineSchemaProperties"] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties:
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSchemaProperties
+        """
+        super(VirtualMachineSchema, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class VirtualMachine(Compute, VirtualMachineSchema):
+    """A Machine Learning compute based on Azure Virtual Machines.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties:
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.VirtualMachineSchemaProperties
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Possible values include: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+        'compute_location': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'created_on': {'readonly': True},
+        'modified_on': {'readonly': True},
+        'provisioning_errors': {'readonly': True},
+        'is_attached_compute': {'readonly': True},
+        'disable_local_auth': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'properties': {'key': 'properties', 'type': 'VirtualMachineSchemaProperties'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+        'compute_location': {'key': 'computeLocation', 'type': 'str'},
+        'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
+        'description': {'key': 'description', 'type': 'str'},
+        'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
+        'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ErrorResponse]'},
+        'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
+        'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["VirtualMachineSchemaProperties"] = None,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword properties:
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSchemaProperties
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        """
+        super(VirtualMachine, self).__init__(description=description, resource_id=resource_id, properties=properties, **kwargs)
+        self.properties = properties
+        self.compute_type = 'VirtualMachine'  # type: str
+        self.compute_location = None
+        self.provisioning_state = None
+        self.description = description
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = resource_id
+        self.provisioning_errors = None
+        self.is_attached_compute = None
+        self.disable_local_auth = None
+
+
+class VirtualMachineImage(msrest.serialization.Model):
+    """Virtual Machine image for Windows AML Compute.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Required. Virtual Machine image path.
+    :vartype id: str
+    """
+
+    _validation = {
+        'id': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        id: str,
+        **kwargs
+    ):
+        """
+        :keyword id: Required. Virtual Machine image path.
+        :paramtype id: str
+        """
+        super(VirtualMachineImage, self).__init__(**kwargs)
+        self.id = id
+
+
+class VirtualMachineSchemaProperties(msrest.serialization.Model):
+    """VirtualMachineSchemaProperties.
+
+    :ivar virtual_machine_size: Virtual Machine size.
+    :vartype virtual_machine_size: str
+    :ivar ssh_port: Port open for ssh connections.
+    :vartype ssh_port: int
+    :ivar notebook_server_port: Notebook server port open for ssh connections.
+    :vartype notebook_server_port: int
+    :ivar address: Public IP address of the virtual machine.
+    :vartype address: str
+    :ivar administrator_account: Admin credentials for virtual machine.
+    :vartype administrator_account:
+     ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+    :ivar is_notebook_instance_compute: Indicates whether this compute will be used for running
+     notebooks.
+    :vartype is_notebook_instance_compute: bool
+    """
+
+    _attribute_map = {
+        'virtual_machine_size': {'key': 'virtualMachineSize', 'type': 'str'},
+        'ssh_port': {'key': 'sshPort', 'type': 'int'},
+        'notebook_server_port': {'key': 'notebookServerPort', 'type': 'int'},
+        'address': {'key': 'address', 'type': 'str'},
+        'administrator_account': {'key': 'administratorAccount', 'type': 'VirtualMachineSshCredentials'},
+        'is_notebook_instance_compute': {'key': 'isNotebookInstanceCompute', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        virtual_machine_size: Optional[str] = None,
+        ssh_port: Optional[int] = None,
+        notebook_server_port: Optional[int] = None,
+        address: Optional[str] = None,
+        administrator_account: Optional["VirtualMachineSshCredentials"] = None,
+        is_notebook_instance_compute: Optional[bool] = None,
+        **kwargs
+    ):
+        """
+        :keyword virtual_machine_size: Virtual Machine size.
+        :paramtype virtual_machine_size: str
+        :keyword ssh_port: Port open for ssh connections.
+        :paramtype ssh_port: int
+        :keyword notebook_server_port: Notebook server port open for ssh connections.
+        :paramtype notebook_server_port: int
+        :keyword address: Public IP address of the virtual machine.
+        :paramtype address: str
+        :keyword administrator_account: Admin credentials for virtual machine.
+        :paramtype administrator_account:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+        :keyword is_notebook_instance_compute: Indicates whether this compute will be used for running
+         notebooks.
+        :paramtype is_notebook_instance_compute: bool
+        """
+        super(VirtualMachineSchemaProperties, self).__init__(**kwargs)
+        self.virtual_machine_size = virtual_machine_size
+        self.ssh_port = ssh_port
+        self.notebook_server_port = notebook_server_port
+        self.address = address
+        self.administrator_account = administrator_account
+        self.is_notebook_instance_compute = is_notebook_instance_compute
+
+
+class VirtualMachineSecretsSchema(msrest.serialization.Model):
+    """VirtualMachineSecretsSchema.
+
+    :ivar administrator_account: Admin credentials for virtual machine.
+    :vartype administrator_account:
+     ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+    """
+
+    _attribute_map = {
+        'administrator_account': {'key': 'administratorAccount', 'type': 'VirtualMachineSshCredentials'},
+    }
+
+    def __init__(
+        self,
+        *,
+        administrator_account: Optional["VirtualMachineSshCredentials"] = None,
+        **kwargs
+    ):
+        """
+        :keyword administrator_account: Admin credentials for virtual machine.
+        :paramtype administrator_account:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+        """
+        super(VirtualMachineSecretsSchema, self).__init__(**kwargs)
+        self.administrator_account = administrator_account
+
+
+class VirtualMachineSecrets(ComputeSecrets, VirtualMachineSecretsSchema):
+    """Secrets related to a Machine Learning compute based on AKS.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar administrator_account: Admin credentials for virtual machine.
+    :vartype administrator_account:
+     ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+    :ivar compute_type: Required. The type of compute.Constant filled by server. Possible values
+     include: "AKS", "Kubernetes", "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine",
+     "HDInsight", "Databricks", "DataLakeAnalytics", "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    """
+
+    _validation = {
+        'compute_type': {'required': True},
+    }
+
+    _attribute_map = {
+        'administrator_account': {'key': 'administratorAccount', 'type': 'VirtualMachineSshCredentials'},
+        'compute_type': {'key': 'computeType', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        administrator_account: Optional["VirtualMachineSshCredentials"] = None,
+        **kwargs
+    ):
+        """
+        :keyword administrator_account: Admin credentials for virtual machine.
+        :paramtype administrator_account:
+         ~azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials
+        """
+        super(VirtualMachineSecrets, self).__init__(administrator_account=administrator_account, **kwargs)
+        self.administrator_account = administrator_account
+        self.compute_type = 'VirtualMachine'  # type: str
+
+
+class VirtualMachineSize(msrest.serialization.Model):
+    """Describes the properties of a VM size.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar name: The name of the virtual machine size.
+    :vartype name: str
+    :ivar family: The family name of the virtual machine size.
+    :vartype family: str
+    :ivar v_cp_us: The number of vCPUs supported by the virtual machine size.
+    :vartype v_cp_us: int
+    :ivar gpus: The number of gPUs supported by the virtual machine size.
+    :vartype gpus: int
+    :ivar os_vhd_size_mb: The OS VHD disk size, in MB, allowed by the virtual machine size.
+    :vartype os_vhd_size_mb: int
+    :ivar max_resource_volume_mb: The resource volume size, in MB, allowed by the virtual machine
+     size.
+    :vartype max_resource_volume_mb: int
+    :ivar memory_gb: The amount of memory, in GB, supported by the virtual machine size.
+    :vartype memory_gb: float
+    :ivar low_priority_capable: Specifies if the virtual machine size supports low priority VMs.
+    :vartype low_priority_capable: bool
+    :ivar premium_io: Specifies if the virtual machine size supports premium IO.
+    :vartype premium_io: bool
+    :ivar estimated_vm_prices: The estimated price information for using a VM.
+    :vartype estimated_vm_prices: ~azure.mgmt.machinelearningservices.models.EstimatedVMPrices
+    :ivar supported_compute_types: Specifies the compute types supported by the virtual machine
+     size.
+    :vartype supported_compute_types: list[str]
+    """
+
+    _validation = {
+        'name': {'readonly': True},
+        'family': {'readonly': True},
+        'v_cp_us': {'readonly': True},
+        'gpus': {'readonly': True},
+        'os_vhd_size_mb': {'readonly': True},
+        'max_resource_volume_mb': {'readonly': True},
+        'memory_gb': {'readonly': True},
+        'low_priority_capable': {'readonly': True},
+        'premium_io': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'name': {'key': 'name', 'type': 'str'},
+        'family': {'key': 'family', 'type': 'str'},
+        'v_cp_us': {'key': 'vCPUs', 'type': 'int'},
+        'gpus': {'key': 'gpus', 'type': 'int'},
+        'os_vhd_size_mb': {'key': 'osVhdSizeMB', 'type': 'int'},
+        'max_resource_volume_mb': {'key': 'maxResourceVolumeMB', 'type': 'int'},
+        'memory_gb': {'key': 'memoryGB', 'type': 'float'},
+        'low_priority_capable': {'key': 'lowPriorityCapable', 'type': 'bool'},
+        'premium_io': {'key': 'premiumIO', 'type': 'bool'},
+        'estimated_vm_prices': {'key': 'estimatedVMPrices', 'type': 'EstimatedVMPrices'},
+        'supported_compute_types': {'key': 'supportedComputeTypes', 'type': '[str]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        estimated_vm_prices: Optional["EstimatedVMPrices"] = None,
+        supported_compute_types: Optional[List[str]] = None,
+        **kwargs
+    ):
+        """
+        :keyword estimated_vm_prices: The estimated price information for using a VM.
+        :paramtype estimated_vm_prices: ~azure.mgmt.machinelearningservices.models.EstimatedVMPrices
+        :keyword supported_compute_types: Specifies the compute types supported by the virtual machine
+         size.
+        :paramtype supported_compute_types: list[str]
+        """
+        super(VirtualMachineSize, self).__init__(**kwargs)
+        self.name = None
+        self.family = None
+        self.v_cp_us = None
+        self.gpus = None
+        self.os_vhd_size_mb = None
+        self.max_resource_volume_mb = None
+        self.memory_gb = None
+        self.low_priority_capable = None
+        self.premium_io = None
+        self.estimated_vm_prices = estimated_vm_prices
+        self.supported_compute_types = supported_compute_types
+
+
+class VirtualMachineSizeListResult(msrest.serialization.Model):
+    """The List Virtual Machine size operation response.
+
+    :ivar value: The list of virtual machine sizes supported by AmlCompute.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.VirtualMachineSize]
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[VirtualMachineSize]'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional[List["VirtualMachineSize"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword value: The list of virtual machine sizes supported by AmlCompute.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.VirtualMachineSize]
+        """
+        super(VirtualMachineSizeListResult, self).__init__(**kwargs)
+        self.value = value
+
+
+class VirtualMachineSshCredentials(msrest.serialization.Model):
+    """Admin credentials for virtual machine.
+
+    :ivar username: Username of admin account.
+    :vartype username: str
+    :ivar password: Password of admin account.
+    :vartype password: str
+    :ivar public_key_data: Public key data.
+    :vartype public_key_data: str
+    :ivar private_key_data: Private key data.
+    :vartype private_key_data: str
+    """
+
+    _attribute_map = {
+        'username': {'key': 'username', 'type': 'str'},
+        'password': {'key': 'password', 'type': 'str'},
+        'public_key_data': {'key': 'publicKeyData', 'type': 'str'},
+        'private_key_data': {'key': 'privateKeyData', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        username: Optional[str] = None,
+        password: Optional[str] = None,
+        public_key_data: Optional[str] = None,
+        private_key_data: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword username: Username of admin account.
+        :paramtype username: str
+        :keyword password: Password of admin account.
+        :paramtype password: str
+        :keyword public_key_data: Public key data.
+        :paramtype public_key_data: str
+        :keyword private_key_data: Private key data.
+        :paramtype private_key_data: str
+        """
+        super(VirtualMachineSshCredentials, self).__init__(**kwargs)
+        self.username = username
+        self.password = password
+        self.public_key_data = public_key_data
+        self.private_key_data = private_key_data
+
+
+class Workspace(Resource):
+    """An object that represents a machine learning workspace.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar identity: The identity of the resource.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar location: Specifies the location of the resource.
+    :vartype location: str
+    :ivar tags: A set of tags. Contains resource tags defined as key/value pairs.
+    :vartype tags: dict[str, str]
+    :ivar sku: The sku of the workspace.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    :ivar workspace_id: The immutable id associated with this workspace.
+    :vartype workspace_id: str
+    :ivar description: The description of this workspace.
+    :vartype description: str
+    :ivar friendly_name: The friendly name for this workspace. This name in mutable.
+    :vartype friendly_name: str
+    :ivar key_vault: ARM id of the key vault associated with this workspace. This cannot be changed
+     once the workspace has been created.
+    :vartype key_vault: str
+    :ivar application_insights: ARM id of the application insights associated with this workspace.
+    :vartype application_insights: str
+    :ivar container_registry: ARM id of the container registry associated with this workspace.
+    :vartype container_registry: str
+    :ivar storage_account: ARM id of the storage account associated with this workspace. This
+     cannot be changed once the workspace has been created.
+    :vartype storage_account: str
+    :ivar discovery_url: Url for the discovery service to identify regional endpoints for machine
+     learning experimentation services.
+    :vartype discovery_url: str
+    :ivar provisioning_state: The current deployment state of workspace resource. The
+     provisioningState is to indicate states for resource provisioning. Possible values include:
+     "Unknown", "Updating", "Creating", "Deleting", "Succeeded", "Failed", "Canceled".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar encryption: The encryption settings of Azure ML workspace.
+    :vartype encryption: ~azure.mgmt.machinelearningservices.models.EncryptionProperty
+    :ivar hbi_workspace: The flag to signal HBI data in the workspace and reduce diagnostic data
+     collected by the service.
+    :vartype hbi_workspace: bool
+    :ivar service_provisioned_resource_group: The name of the managed resource group created by
+     workspace RP in customer subscription if the workspace is CMK workspace.
+    :vartype service_provisioned_resource_group: str
+    :ivar private_link_count: Count of private connections in the workspace.
+    :vartype private_link_count: int
+    :ivar image_build_compute: The compute name for image build.
+    :vartype image_build_compute: str
+    :ivar allow_public_access_when_behind_vnet: The flag to indicate whether to allow public access
+     when behind VNet.
+    :vartype allow_public_access_when_behind_vnet: bool
+    :ivar public_network_access: Whether requests from Public Network are allowed. Possible values
+     include: "Enabled", "Disabled".
+    :vartype public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.PublicNetworkAccess
+    :ivar private_endpoint_connections: The list of private endpoint connections in the workspace.
+    :vartype private_endpoint_connections:
+     list[~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection]
+    :ivar shared_private_link_resources: The list of shared private link resources in this
+     workspace.
+    :vartype shared_private_link_resources:
+     list[~azure.mgmt.machinelearningservices.models.SharedPrivateLinkResource]
+    :ivar notebook_info: The notebook info of Azure ML workspace.
+    :vartype notebook_info: ~azure.mgmt.machinelearningservices.models.NotebookResourceInfo
+    :ivar service_managed_resources_settings: The service managed resource settings.
+    :vartype service_managed_resources_settings:
+     ~azure.mgmt.machinelearningservices.models.ServiceManagedResourcesSettings
+    :ivar primary_user_assigned_identity: The user assigned identity resource id that represents
+     the workspace identity.
+    :vartype primary_user_assigned_identity: str
+    :ivar tenant_id: The tenant id associated with this workspace.
+    :vartype tenant_id: str
+    :ivar storage_hns_enabled: If the storage associated with the workspace has hierarchical
+     namespace(HNS) enabled.
+    :vartype storage_hns_enabled: bool
+    :ivar ml_flow_tracking_uri: The URI associated with this workspace that machine learning flow
+     must point at to set up tracking.
+    :vartype ml_flow_tracking_uri: str
+    :ivar v1_legacy_mode: Enabling v1_legacy_mode may prevent you from using features provided by
+     the v2 API.
+    :vartype v1_legacy_mode: bool
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'workspace_id': {'readonly': True},
+        'provisioning_state': {'readonly': True},
+        'service_provisioned_resource_group': {'readonly': True},
+        'private_link_count': {'readonly': True},
+        'private_endpoint_connections': {'readonly': True},
+        'notebook_info': {'readonly': True},
+        'tenant_id': {'readonly': True},
+        'storage_hns_enabled': {'readonly': True},
+        'ml_flow_tracking_uri': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'location': {'key': 'location', 'type': 'str'},
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+        'workspace_id': {'key': 'properties.workspaceId', 'type': 'str'},
+        'description': {'key': 'properties.description', 'type': 'str'},
+        'friendly_name': {'key': 'properties.friendlyName', 'type': 'str'},
+        'key_vault': {'key': 'properties.keyVault', 'type': 'str'},
+        'application_insights': {'key': 'properties.applicationInsights', 'type': 'str'},
+        'container_registry': {'key': 'properties.containerRegistry', 'type': 'str'},
+        'storage_account': {'key': 'properties.storageAccount', 'type': 'str'},
+        'discovery_url': {'key': 'properties.discoveryUrl', 'type': 'str'},
+        'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'},
+        'encryption': {'key': 'properties.encryption', 'type': 'EncryptionProperty'},
+        'hbi_workspace': {'key': 'properties.hbiWorkspace', 'type': 'bool'},
+        'service_provisioned_resource_group': {'key': 'properties.serviceProvisionedResourceGroup', 'type': 'str'},
+        'private_link_count': {'key': 'properties.privateLinkCount', 'type': 'int'},
+        'image_build_compute': {'key': 'properties.imageBuildCompute', 'type': 'str'},
+        'allow_public_access_when_behind_vnet': {'key': 'properties.allowPublicAccessWhenBehindVnet', 'type': 'bool'},
+        'public_network_access': {'key': 'properties.publicNetworkAccess', 'type': 'str'},
+        'private_endpoint_connections': {'key': 'properties.privateEndpointConnections', 'type': '[PrivateEndpointConnection]'},
+        'shared_private_link_resources': {'key': 'properties.sharedPrivateLinkResources', 'type': '[SharedPrivateLinkResource]'},
+        'notebook_info': {'key': 'properties.notebookInfo', 'type': 'NotebookResourceInfo'},
+        'service_managed_resources_settings': {'key': 'properties.serviceManagedResourcesSettings', 'type': 'ServiceManagedResourcesSettings'},
+        'primary_user_assigned_identity': {'key': 'properties.primaryUserAssignedIdentity', 'type': 'str'},
+        'tenant_id': {'key': 'properties.tenantId', 'type': 'str'},
+        'storage_hns_enabled': {'key': 'properties.storageHnsEnabled', 'type': 'bool'},
+        'ml_flow_tracking_uri': {'key': 'properties.mlFlowTrackingUri', 'type': 'str'},
+        'v1_legacy_mode': {'key': 'properties.v1LegacyMode', 'type': 'bool'},
+    }
+
+    def __init__(
+        self,
+        *,
+        identity: Optional["ManagedServiceIdentity"] = None,
+        location: Optional[str] = None,
+        tags: Optional[Dict[str, str]] = None,
+        sku: Optional["Sku"] = None,
+        description: Optional[str] = None,
+        friendly_name: Optional[str] = None,
+        key_vault: Optional[str] = None,
+        application_insights: Optional[str] = None,
+        container_registry: Optional[str] = None,
+        storage_account: Optional[str] = None,
+        discovery_url: Optional[str] = None,
+        encryption: Optional["EncryptionProperty"] = None,
+        hbi_workspace: Optional[bool] = False,
+        image_build_compute: Optional[str] = None,
+        allow_public_access_when_behind_vnet: Optional[bool] = False,
+        public_network_access: Optional[Union[str, "PublicNetworkAccess"]] = None,
+        shared_private_link_resources: Optional[List["SharedPrivateLinkResource"]] = None,
+        service_managed_resources_settings: Optional["ServiceManagedResourcesSettings"] = None,
+        primary_user_assigned_identity: Optional[str] = None,
+        v1_legacy_mode: Optional[bool] = False,
+        **kwargs
+    ):
+        """
+        :keyword identity: The identity of the resource.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword location: Specifies the location of the resource.
+        :paramtype location: str
+        :keyword tags: A set of tags. Contains resource tags defined as key/value pairs.
+        :paramtype tags: dict[str, str]
+        :keyword sku: The sku of the workspace.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        :keyword description: The description of this workspace.
+        :paramtype description: str
+        :keyword friendly_name: The friendly name for this workspace. This name in mutable.
+        :paramtype friendly_name: str
+        :keyword key_vault: ARM id of the key vault associated with this workspace. This cannot be
+         changed once the workspace has been created.
+        :paramtype key_vault: str
+        :keyword application_insights: ARM id of the application insights associated with this
+         workspace.
+        :paramtype application_insights: str
+        :keyword container_registry: ARM id of the container registry associated with this workspace.
+        :paramtype container_registry: str
+        :keyword storage_account: ARM id of the storage account associated with this workspace. This
+         cannot be changed once the workspace has been created.
+        :paramtype storage_account: str
+        :keyword discovery_url: Url for the discovery service to identify regional endpoints for
+         machine learning experimentation services.
+        :paramtype discovery_url: str
+        :keyword encryption: The encryption settings of Azure ML workspace.
+        :paramtype encryption: ~azure.mgmt.machinelearningservices.models.EncryptionProperty
+        :keyword hbi_workspace: The flag to signal HBI data in the workspace and reduce diagnostic data
+         collected by the service.
+        :paramtype hbi_workspace: bool
+        :keyword image_build_compute: The compute name for image build.
+        :paramtype image_build_compute: str
+        :keyword allow_public_access_when_behind_vnet: The flag to indicate whether to allow public
+         access when behind VNet.
+        :paramtype allow_public_access_when_behind_vnet: bool
+        :keyword public_network_access: Whether requests from Public Network are allowed. Possible
+         values include: "Enabled", "Disabled".
+        :paramtype public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.PublicNetworkAccess
+        :keyword shared_private_link_resources: The list of shared private link resources in this
+         workspace.
+        :paramtype shared_private_link_resources:
+         list[~azure.mgmt.machinelearningservices.models.SharedPrivateLinkResource]
+        :keyword service_managed_resources_settings: The service managed resource settings.
+        :paramtype service_managed_resources_settings:
+         ~azure.mgmt.machinelearningservices.models.ServiceManagedResourcesSettings
+        :keyword primary_user_assigned_identity: The user assigned identity resource id that represents
+         the workspace identity.
+        :paramtype primary_user_assigned_identity: str
+        :keyword v1_legacy_mode: Enabling v1_legacy_mode may prevent you from using features provided
+         by the v2 API.
+        :paramtype v1_legacy_mode: bool
+        """
+        super(Workspace, self).__init__(**kwargs)
+        self.identity = identity
+        self.location = location
+        self.tags = tags
+        self.sku = sku
+        self.workspace_id = None
+        self.description = description
+        self.friendly_name = friendly_name
+        self.key_vault = key_vault
+        self.application_insights = application_insights
+        self.container_registry = container_registry
+        self.storage_account = storage_account
+        self.discovery_url = discovery_url
+        self.provisioning_state = None
+        self.encryption = encryption
+        self.hbi_workspace = hbi_workspace
+        self.service_provisioned_resource_group = None
+        self.private_link_count = None
+        self.image_build_compute = image_build_compute
+        self.allow_public_access_when_behind_vnet = allow_public_access_when_behind_vnet
+        self.public_network_access = public_network_access
+        self.private_endpoint_connections = None
+        self.shared_private_link_resources = shared_private_link_resources
+        self.notebook_info = None
+        self.service_managed_resources_settings = service_managed_resources_settings
+        self.primary_user_assigned_identity = primary_user_assigned_identity
+        self.tenant_id = None
+        self.storage_hns_enabled = None
+        self.ml_flow_tracking_uri = None
+        self.v1_legacy_mode = v1_legacy_mode
+
+
+class WorkspaceConnectionManagedIdentity(msrest.serialization.Model):
+    """WorkspaceConnectionManagedIdentity.
+
+    :ivar resource_id:
+    :vartype resource_id: str
+    :ivar client_id:
+    :vartype client_id: str
+    """
+
+    _attribute_map = {
+        'resource_id': {'key': 'resourceId', 'type': 'str'},
+        'client_id': {'key': 'clientId', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        resource_id: Optional[str] = None,
+        client_id: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword resource_id:
+        :paramtype resource_id: str
+        :keyword client_id:
+        :paramtype client_id: str
+        """
+        super(WorkspaceConnectionManagedIdentity, self).__init__(**kwargs)
+        self.resource_id = resource_id
+        self.client_id = client_id
+
+
+class WorkspaceConnectionPersonalAccessToken(msrest.serialization.Model):
+    """WorkspaceConnectionPersonalAccessToken.
+
+    :ivar pat:
+    :vartype pat: str
+    """
+
+    _attribute_map = {
+        'pat': {'key': 'pat', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        pat: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword pat:
+        :paramtype pat: str
+        """
+        super(WorkspaceConnectionPersonalAccessToken, self).__init__(**kwargs)
+        self.pat = pat
+
+
+class WorkspaceConnectionPropertiesV2BasicResource(Resource):
+    """WorkspaceConnectionPropertiesV2BasicResource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: Required.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2
+    """
+
+    _validation = {
+        'id': {'readonly': True},
+        'name': {'readonly': True},
+        'type': {'readonly': True},
+        'system_data': {'readonly': True},
+        'properties': {'required': True},
+    }
+
+    _attribute_map = {
+        'id': {'key': 'id', 'type': 'str'},
+        'name': {'key': 'name', 'type': 'str'},
+        'type': {'key': 'type', 'type': 'str'},
+        'system_data': {'key': 'systemData', 'type': 'SystemData'},
+        'properties': {'key': 'properties', 'type': 'WorkspaceConnectionPropertiesV2'},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: "WorkspaceConnectionPropertiesV2",
+        **kwargs
+    ):
+        """
+        :keyword properties: Required.
+        :paramtype properties:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2
+        """
+        super(WorkspaceConnectionPropertiesV2BasicResource, self).__init__(**kwargs)
+        self.properties = properties
+
+
+class WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult(msrest.serialization.Model):
+    """WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value:
+    :vartype value:
+     list[~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource]
+    :ivar next_link:
+    :vartype next_link: str
+    """
+
+    _validation = {
+        'next_link': {'readonly': True},
+    }
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[WorkspaceConnectionPropertiesV2BasicResource]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional[List["WorkspaceConnectionPropertiesV2BasicResource"]] = None,
+        **kwargs
+    ):
+        """
+        :keyword value:
+        :paramtype value:
+         list[~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource]
+        """
+        super(WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult, self).__init__(**kwargs)
+        self.value = value
+        self.next_link = None
+
+
+class WorkspaceConnectionSharedAccessSignature(msrest.serialization.Model):
+    """WorkspaceConnectionSharedAccessSignature.
+
+    :ivar sas:
+    :vartype sas: str
+    """
+
+    _attribute_map = {
+        'sas': {'key': 'sas', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        sas: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword sas:
+        :paramtype sas: str
+        """
+        super(WorkspaceConnectionSharedAccessSignature, self).__init__(**kwargs)
+        self.sas = sas
+
+
+class WorkspaceConnectionUsernamePassword(msrest.serialization.Model):
+    """WorkspaceConnectionUsernamePassword.
+
+    :ivar username:
+    :vartype username: str
+    :ivar password:
+    :vartype password: str
+    """
+
+    _attribute_map = {
+        'username': {'key': 'username', 'type': 'str'},
+        'password': {'key': 'password', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        username: Optional[str] = None,
+        password: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword username:
+        :paramtype username: str
+        :keyword password:
+        :paramtype password: str
+        """
+        super(WorkspaceConnectionUsernamePassword, self).__init__(**kwargs)
+        self.username = username
+        self.password = password
+
+
+class WorkspaceListResult(msrest.serialization.Model):
+    """The result of a request to list machine learning workspaces.
+
+    :ivar value: The list of machine learning workspaces. Since this list may be incomplete, the
+     nextLink field should be used to request the next list of machine learning workspaces.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Workspace]
+    :ivar next_link: The URI that can be used to request the next list of machine learning
+     workspaces.
+    :vartype next_link: str
+    """
+
+    _attribute_map = {
+        'value': {'key': 'value', 'type': '[Workspace]'},
+        'next_link': {'key': 'nextLink', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        value: Optional[List["Workspace"]] = None,
+        next_link: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword value: The list of machine learning workspaces. Since this list may be incomplete, the
+         nextLink field should be used to request the next list of machine learning workspaces.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.Workspace]
+        :keyword next_link: The URI that can be used to request the next list of machine learning
+         workspaces.
+        :paramtype next_link: str
+        """
+        super(WorkspaceListResult, self).__init__(**kwargs)
+        self.value = value
+        self.next_link = next_link
+
+
+class WorkspaceUpdateParameters(msrest.serialization.Model):
+    """The parameters for updating a machine learning workspace.
+
+    :ivar tags: A set of tags. The resource tags for the machine learning workspace.
+    :vartype tags: dict[str, str]
+    :ivar sku: The sku of the workspace.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    :ivar identity: The identity of the resource.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar description: The description of this workspace.
+    :vartype description: str
+    :ivar friendly_name: The friendly name for this workspace.
+    :vartype friendly_name: str
+    :ivar image_build_compute: The compute name for image build.
+    :vartype image_build_compute: str
+    :ivar service_managed_resources_settings: The service managed resource settings.
+    :vartype service_managed_resources_settings:
+     ~azure.mgmt.machinelearningservices.models.ServiceManagedResourcesSettings
+    :ivar primary_user_assigned_identity: The user assigned identity resource id that represents
+     the workspace identity.
+    :vartype primary_user_assigned_identity: str
+    :ivar public_network_access: Whether requests from Public Network are allowed. Possible values
+     include: "Enabled", "Disabled".
+    :vartype public_network_access: str or
+     ~azure.mgmt.machinelearningservices.models.PublicNetworkAccess
+    :ivar application_insights: ARM id of the application insights associated with this workspace.
+    :vartype application_insights: str
+    :ivar container_registry: ARM id of the container registry associated with this workspace.
+    :vartype container_registry: str
+    """
+
+    _attribute_map = {
+        'tags': {'key': 'tags', 'type': '{str}'},
+        'sku': {'key': 'sku', 'type': 'Sku'},
+        'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
+        'description': {'key': 'properties.description', 'type': 'str'},
+        'friendly_name': {'key': 'properties.friendlyName', 'type': 'str'},
+        'image_build_compute': {'key': 'properties.imageBuildCompute', 'type': 'str'},
+        'service_managed_resources_settings': {'key': 'properties.serviceManagedResourcesSettings', 'type': 'ServiceManagedResourcesSettings'},
+        'primary_user_assigned_identity': {'key': 'properties.primaryUserAssignedIdentity', 'type': 'str'},
+        'public_network_access': {'key': 'properties.publicNetworkAccess', 'type': 'str'},
+        'application_insights': {'key': 'properties.applicationInsights', 'type': 'str'},
+        'container_registry': {'key': 'properties.containerRegistry', 'type': 'str'},
+    }
+
+    def __init__(
+        self,
+        *,
+        tags: Optional[Dict[str, str]] = None,
+        sku: Optional["Sku"] = None,
+        identity: Optional["ManagedServiceIdentity"] = None,
+        description: Optional[str] = None,
+        friendly_name: Optional[str] = None,
+        image_build_compute: Optional[str] = None,
+        service_managed_resources_settings: Optional["ServiceManagedResourcesSettings"] = None,
+        primary_user_assigned_identity: Optional[str] = None,
+        public_network_access: Optional[Union[str, "PublicNetworkAccess"]] = None,
+        application_insights: Optional[str] = None,
+        container_registry: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        :keyword tags: A set of tags. The resource tags for the machine learning workspace.
+        :paramtype tags: dict[str, str]
+        :keyword sku: The sku of the workspace.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        :keyword identity: The identity of the resource.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword description: The description of this workspace.
+        :paramtype description: str
+        :keyword friendly_name: The friendly name for this workspace.
+        :paramtype friendly_name: str
+        :keyword image_build_compute: The compute name for image build.
+        :paramtype image_build_compute: str
+        :keyword service_managed_resources_settings: The service managed resource settings.
+        :paramtype service_managed_resources_settings:
+         ~azure.mgmt.machinelearningservices.models.ServiceManagedResourcesSettings
+        :keyword primary_user_assigned_identity: The user assigned identity resource id that represents
+         the workspace identity.
+        :paramtype primary_user_assigned_identity: str
+        :keyword public_network_access: Whether requests from Public Network are allowed. Possible
+         values include: "Enabled", "Disabled".
+        :paramtype public_network_access: str or
+         ~azure.mgmt.machinelearningservices.models.PublicNetworkAccess
+        :keyword application_insights: ARM id of the application insights associated with this
+         workspace.
+        :paramtype application_insights: str
+        :keyword container_registry: ARM id of the container registry associated with this workspace.
+        :paramtype container_registry: str
+        """
+        super(WorkspaceUpdateParameters, self).__init__(**kwargs)
+        self.tags = tags
+        self.sku = sku
+        self.identity = identity
+        self.description = description
+        self.friendly_name = friendly_name
+        self.image_build_compute = image_build_compute
+        self.service_managed_resources_settings = service_managed_resources_settings
+        self.primary_user_assigned_identity = primary_user_assigned_identity
+        self.public_network_access = public_network_access
+        self.application_insights = application_insights
+        self.container_registry = container_registry
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/__init__.py
new file mode 100644
index 00000000..96b72908
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/__init__.py
@@ -0,0 +1,65 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from ._batch_endpoints_operations import BatchEndpointsOperations
+from ._batch_deployments_operations import BatchDeploymentsOperations
+from ._code_containers_operations import CodeContainersOperations
+from ._code_versions_operations import CodeVersionsOperations
+from ._component_containers_operations import ComponentContainersOperations
+from ._component_versions_operations import ComponentVersionsOperations
+from ._data_containers_operations import DataContainersOperations
+from ._data_versions_operations import DataVersionsOperations
+from ._datastores_operations import DatastoresOperations
+from ._environment_containers_operations import EnvironmentContainersOperations
+from ._environment_versions_operations import EnvironmentVersionsOperations
+from ._jobs_operations import JobsOperations
+from ._model_containers_operations import ModelContainersOperations
+from ._model_versions_operations import ModelVersionsOperations
+from ._online_endpoints_operations import OnlineEndpointsOperations
+from ._online_deployments_operations import OnlineDeploymentsOperations
+from ._schedules_operations import SchedulesOperations
+from ._operations import Operations
+from ._workspaces_operations import WorkspacesOperations
+from ._usages_operations import UsagesOperations
+from ._virtual_machine_sizes_operations import VirtualMachineSizesOperations
+from ._quotas_operations import QuotasOperations
+from ._compute_operations import ComputeOperations
+from ._private_endpoint_connections_operations import PrivateEndpointConnectionsOperations
+from ._private_link_resources_operations import PrivateLinkResourcesOperations
+from ._workspace_connections_operations import WorkspaceConnectionsOperations
+from ._workspace_features_operations import WorkspaceFeaturesOperations
+
+__all__ = [
+    'BatchEndpointsOperations',
+    'BatchDeploymentsOperations',
+    'CodeContainersOperations',
+    'CodeVersionsOperations',
+    'ComponentContainersOperations',
+    'ComponentVersionsOperations',
+    'DataContainersOperations',
+    'DataVersionsOperations',
+    'DatastoresOperations',
+    'EnvironmentContainersOperations',
+    'EnvironmentVersionsOperations',
+    'JobsOperations',
+    'ModelContainersOperations',
+    'ModelVersionsOperations',
+    'OnlineEndpointsOperations',
+    'OnlineDeploymentsOperations',
+    'SchedulesOperations',
+    'Operations',
+    'WorkspacesOperations',
+    'UsagesOperations',
+    'VirtualMachineSizesOperations',
+    'QuotasOperations',
+    'ComputeOperations',
+    'PrivateEndpointConnectionsOperations',
+    'PrivateLinkResourcesOperations',
+    'WorkspaceConnectionsOperations',
+    'WorkspaceFeaturesOperations',
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_batch_deployments_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_batch_deployments_operations.py
new file mode 100644
index 00000000..85781f67
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_batch_deployments_operations.py
@@ -0,0 +1,876 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    top = kwargs.pop('top', None)  # type: Optional[int]
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if order_by is not None:
+        _query_parameters['$orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if top is not None:
+        _query_parameters['$top'] = _SERIALIZER.query("top", top, 'int')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    deployment_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+        "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    deployment_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+        "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    deployment_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+        "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PATCH",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    deployment_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+        "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class BatchDeploymentsOperations(object):
+    """BatchDeploymentsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        order_by=None,  # type: Optional[str]
+        top=None,  # type: Optional[int]
+        skip=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.BatchDeploymentTrackedResourceArmPaginatedResult"]
+        """Lists Batch inference deployments in the workspace.
+
+        Lists Batch inference deployments in the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Endpoint name.
+        :type endpoint_name: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Top of list.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either BatchDeploymentTrackedResourceArmPaginatedResult
+         or the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.BatchDeploymentTrackedResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDeploymentTrackedResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("BatchDeploymentTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments"}  # type: ignore
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Delete Batch Inference deployment (asynchronous).
+
+        Delete Batch Inference deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference deployment identifier.
+        :type deployment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.BatchDeployment"
+        """Gets a batch inference deployment by id.
+
+        Gets a batch inference deployment by id.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: The identifier for the Batch deployments.
+        :type deployment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: BatchDeployment, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.BatchDeployment
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDeployment"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('BatchDeployment', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    def _update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        body,  # type: "_models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Optional["_models.BatchDeployment"]
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.BatchDeployment"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('BatchDeployment', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        body,  # type: "_models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.BatchDeployment"]
+        """Update a batch inference deployment (asynchronous).
+
+        Update a batch inference deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: The identifier for the Batch inference deployment.
+        :type deployment_name: str
+        :param body: Batch inference deployment definition object.
+        :type body:
+         ~azure.mgmt.machinelearningservices.models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either BatchDeployment or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.BatchDeployment]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDeployment"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('BatchDeployment', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        body,  # type: "_models.BatchDeployment"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.BatchDeployment"
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDeployment"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'BatchDeployment')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('BatchDeployment', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('BatchDeployment', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        body,  # type: "_models.BatchDeployment"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.BatchDeployment"]
+        """Creates/updates a batch inference deployment (asynchronous).
+
+        Creates/updates a batch inference deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: The identifier for the Batch inference deployment.
+        :type deployment_name: str
+        :param body: Batch inference deployment definition object.
+        :type body: ~azure.mgmt.machinelearningservices.models.BatchDeployment
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either BatchDeployment or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.BatchDeployment]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDeployment"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('BatchDeployment', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_batch_endpoints_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_batch_endpoints_operations.py
new file mode 100644
index 00000000..a908e8ae
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_batch_endpoints_operations.py
@@ -0,0 +1,934 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    count = kwargs.pop('count', None)  # type: Optional[int]
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if count is not None:
+        _query_parameters['count'] = _SERIALIZER.query("count", count, 'int')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PATCH",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_keys_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/listkeys")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class BatchEndpointsOperations(object):
+    """BatchEndpointsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        count=None,  # type: Optional[int]
+        skip=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.BatchEndpointTrackedResourceArmPaginatedResult"]
+        """Lists Batch inference endpoint in the workspace.
+
+        Lists Batch inference endpoint in the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param count: Number of endpoints to be retrieved in a page of results.
+        :type count: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either BatchEndpointTrackedResourceArmPaginatedResult or
+         the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.BatchEndpointTrackedResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEndpointTrackedResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    count=count,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    count=count,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("BatchEndpointTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints"}  # type: ignore
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Delete Batch Inference Endpoint (asynchronous).
+
+        Delete Batch Inference Endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.BatchEndpoint"
+        """Gets a batch inference endpoint by name.
+
+        Gets a batch inference endpoint by name.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Name for the Batch Endpoint.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: BatchEndpoint, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.BatchEndpoint
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEndpoint"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+
+    def _update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        body,  # type: "_models.PartialMinimalTrackedResourceWithIdentity"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Optional["_models.BatchEndpoint"]
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.BatchEndpoint"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'PartialMinimalTrackedResourceWithIdentity')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        body,  # type: "_models.PartialMinimalTrackedResourceWithIdentity"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.BatchEndpoint"]
+        """Update a batch inference endpoint (asynchronous).
+
+        Update a batch inference endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Name for the Batch inference endpoint.
+        :type endpoint_name: str
+        :param body: Mutable batch inference endpoint definition object.
+        :type body:
+         ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithIdentity
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either BatchEndpoint or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.BatchEndpoint]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEndpoint"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        body,  # type: "_models.BatchEndpoint"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.BatchEndpoint"
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEndpoint"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'BatchEndpoint')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        body,  # type: "_models.BatchEndpoint"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.BatchEndpoint"]
+        """Creates a batch inference endpoint (asynchronous).
+
+        Creates a batch inference endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Name for the Batch inference endpoint.
+        :type endpoint_name: str
+        :param body: Batch inference endpoint definition object.
+        :type body: ~azure.mgmt.machinelearningservices.models.BatchEndpoint
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either BatchEndpoint or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.BatchEndpoint]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEndpoint"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('BatchEndpoint', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}"}  # type: ignore
+
+    @distributed_trace
+    def list_keys(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.EndpointAuthKeys"
+        """Lists batch Inference Endpoint keys.
+
+        Lists batch Inference Endpoint keys.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EndpointAuthKeys, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EndpointAuthKeys"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self.list_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('EndpointAuthKeys', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batchEndpoints/{endpointName}/listkeys"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_code_containers_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_code_containers_operations.py
new file mode 100644
index 00000000..8c2fcb16
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_code_containers_operations.py
@@ -0,0 +1,511 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class CodeContainersOperations(object):
+    """CodeContainersOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        skip=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.CodeContainerResourceArmPaginatedResult"]
+        """List containers.
+
+        List containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either CodeContainerResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.CodeContainerResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeContainerResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("CodeContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.CodeContainer"
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('CodeContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        body,  # type: "_models.CodeContainer"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.CodeContainer"
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param body: Container entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'CodeContainer')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('CodeContainer', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('CodeContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_code_versions_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_code_versions_operations.py
new file mode 100644
index 00000000..42bdffe9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_code_versions_operations.py
@@ -0,0 +1,552 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    top = kwargs.pop('top', None)  # type: Optional[int]
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if order_by is not None:
+        _query_parameters['$orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if top is not None:
+        _query_parameters['$top'] = _SERIALIZER.query("top", top, 'int')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class CodeVersionsOperations(object):
+    """CodeVersionsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        order_by=None,  # type: Optional[str]
+        top=None,  # type: Optional[int]
+        skip=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.CodeVersionResourceArmPaginatedResult"]
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Maximum number of records to return.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either CodeVersionResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.CodeVersionResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeVersionResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("CodeVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.CodeVersion"
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('CodeVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        body,  # type: "_models.CodeVersion"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.CodeVersion"
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :param body: Version entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeVersion
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.CodeVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'CodeVersion')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('CodeVersion', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('CodeVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/codes/{name}/versions/{version}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_component_containers_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_component_containers_operations.py
new file mode 100644
index 00000000..27b26586
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_component_containers_operations.py
@@ -0,0 +1,519 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    list_view_type = kwargs.pop('list_view_type', None)  # type: Optional[Union[str, "_models.ListViewType"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if list_view_type is not None:
+        _query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class ComponentContainersOperations(object):
+    """ComponentContainersOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        skip=None,  # type: Optional[str]
+        list_view_type=None,  # type: Optional[Union[str, "_models.ListViewType"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.ComponentContainerResourceArmPaginatedResult"]
+        """List component containers.
+
+        List component containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ComponentContainerResourceArmPaginatedResult or
+         the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ComponentContainerResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentContainerResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ComponentContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ComponentContainer"
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ComponentContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        body,  # type: "_models.ComponentContainer"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ComponentContainer"
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param body: Container entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'ComponentContainer')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('ComponentContainer', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('ComponentContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_component_versions_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_component_versions_operations.py
new file mode 100644
index 00000000..4bbe1375
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_component_versions_operations.py
@@ -0,0 +1,560 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    top = kwargs.pop('top', None)  # type: Optional[int]
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    list_view_type = kwargs.pop('list_view_type', None)  # type: Optional[Union[str, "_models.ListViewType"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if order_by is not None:
+        _query_parameters['$orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if top is not None:
+        _query_parameters['$top'] = _SERIALIZER.query("top", top, 'int')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if list_view_type is not None:
+        _query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class ComponentVersionsOperations(object):
+    """ComponentVersionsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        order_by=None,  # type: Optional[str]
+        top=None,  # type: Optional[int]
+        skip=None,  # type: Optional[str]
+        list_view_type=None,  # type: Optional[Union[str, "_models.ListViewType"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.ComponentVersionResourceArmPaginatedResult"]
+        """List component versions.
+
+        List component versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Component name.
+        :type name: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Maximum number of records to return.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ComponentVersionResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ComponentVersionResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentVersionResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ComponentVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ComponentVersion"
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ComponentVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        body,  # type: "_models.ComponentVersion"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ComponentVersion"
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :param body: Version entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentVersion
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComponentVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'ComponentVersion')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('ComponentVersion', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('ComponentVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/components/{name}/versions/{version}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_compute_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_compute_operations.py
new file mode 100644
index 00000000..a74f6c09
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_compute_operations.py
@@ -0,0 +1,1500 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    compute_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    compute_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    compute_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PATCH",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    compute_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    underlying_resource_action = kwargs.pop('underlying_resource_action')  # type: Union[str, "_models.UnderlyingResourceAction"]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    _query_parameters['underlyingResourceAction'] = _SERIALIZER.query("underlying_resource_action", underlying_resource_action, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_nodes_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    compute_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listNodes")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_keys_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    compute_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listKeys")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_start_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    compute_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/start")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_stop_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    compute_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/stop")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_restart_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    compute_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/restart")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class ComputeOperations(object):
+    """ComputeOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        skip=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.PaginatedComputeResourcesList"]
+        """Gets computes in specified workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PaginatedComputeResourcesList or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedComputeResourcesList]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedComputeResourcesList"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("PaginatedComputeResourcesList", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes"}  # type: ignore
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ComputeResource"
+        """Gets compute definition by its name. Any secrets (storage keys, service credentials, etc) are
+        not returned - use 'keys' nested resource to get them.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComputeResource, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComputeResource
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ComputeResource', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        parameters,  # type: "_models.ComputeResource"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ComputeResource"
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'ComputeResource')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('ComputeResource', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('ComputeResource', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        parameters,  # type: "_models.ComputeResource"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.ComputeResource"]
+        """Creates or updates compute. This call will overwrite a compute if it exists. This is a
+        nonrecoverable operation. If your intent is to create a new compute, do a GET first to verify
+        that it does not exist yet.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :param parameters: Payload with Machine Learning compute definition.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.ComputeResource
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ComputeResource or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ComputeResource]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                parameters=parameters,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('ComputeResource', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+    def _update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        parameters,  # type: "_models.ClusterUpdateParameters"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ComputeResource"
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'ClusterUpdateParameters')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ComputeResource', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        parameters,  # type: "_models.ClusterUpdateParameters"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.ComputeResource"]
+        """Updates properties of a compute. This call will overwrite a compute if it exists. This is a
+        nonrecoverable operation.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :param parameters: Additional parameters for cluster update.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.ClusterUpdateParameters
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ComputeResource or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ComputeResource]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                parameters=parameters,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('ComputeResource', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        underlying_resource_action,  # type: Union[str, "_models.UnderlyingResourceAction"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            underlying_resource_action=underlying_resource_action,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        underlying_resource_action,  # type: Union[str, "_models.UnderlyingResourceAction"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Deletes specified Machine Learning compute.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :param underlying_resource_action: Delete the underlying compute if 'Delete', or detach the
+         underlying compute from workspace if 'Detach'.
+        :type underlying_resource_action: str or
+         ~azure.mgmt.machinelearningservices.models.UnderlyingResourceAction
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                underlying_resource_action=underlying_resource_action,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"}  # type: ignore
+
+    @distributed_trace
+    def list_nodes(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.AmlComputeNodesInformation"]
+        """Get the details (e.g IP address, port etc) of all the compute nodes in the compute.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either AmlComputeNodesInformation or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.AmlComputeNodesInformation]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.AmlComputeNodesInformation"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_nodes_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    compute_name=compute_name,
+                    api_version=api_version,
+                    template_url=self.list_nodes.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_nodes_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    compute_name=compute_name,
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("AmlComputeNodesInformation", pipeline_response)
+            list_of_elem = deserialized.nodes
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list_nodes.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listNodes"}  # type: ignore
+
+    @distributed_trace
+    def list_keys(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ComputeSecrets"
+        """Gets secrets related to Machine Learning compute (storage keys, service credentials, etc).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComputeSecrets, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComputeSecrets
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeSecrets"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            template_url=self.list_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ComputeSecrets', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listKeys"}  # type: ignore
+
+
+    def _start_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_start_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            template_url=self._start_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    _start_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/start"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_start(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Posts a start action to a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._start_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_start.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/start"}  # type: ignore
+
+    def _stop_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_stop_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            template_url=self._stop_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    _stop_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/stop"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_stop(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Posts a stop action to a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._stop_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_stop.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/stop"}  # type: ignore
+
+    def _restart_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_restart_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            api_version=api_version,
+            template_url=self._restart_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    _restart_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/restart"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_restart(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        compute_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Posts a restart action to a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute.
+        :type compute_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._restart_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                compute_name=compute_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_restart.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/restart"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_data_containers_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_data_containers_operations.py
new file mode 100644
index 00000000..21c81906
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_data_containers_operations.py
@@ -0,0 +1,519 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    list_view_type = kwargs.pop('list_view_type', None)  # type: Optional[Union[str, "_models.ListViewType"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if list_view_type is not None:
+        _query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class DataContainersOperations(object):
+    """DataContainersOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        skip=None,  # type: Optional[str]
+        list_view_type=None,  # type: Optional[Union[str, "_models.ListViewType"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.DataContainerResourceArmPaginatedResult"]
+        """List data containers.
+
+        List data containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either DataContainerResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.DataContainerResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainerResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("DataContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.DataContainer"
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('DataContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        body,  # type: "_models.DataContainer"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.DataContainer"
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param body: Container entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.DataContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'DataContainer')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('DataContainer', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('DataContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_data_versions_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_data_versions_operations.py
new file mode 100644
index 00000000..bf1b67b9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_data_versions_operations.py
@@ -0,0 +1,572 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    top = kwargs.pop('top', None)  # type: Optional[int]
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    tags = kwargs.pop('tags', None)  # type: Optional[str]
+    list_view_type = kwargs.pop('list_view_type', None)  # type: Optional[Union[str, "_models.ListViewType"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if order_by is not None:
+        _query_parameters['$orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if top is not None:
+        _query_parameters['$top'] = _SERIALIZER.query("top", top, 'int')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if tags is not None:
+        _query_parameters['$tags'] = _SERIALIZER.query("tags", tags, 'str')
+    if list_view_type is not None:
+        _query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class DataVersionsOperations(object):
+    """DataVersionsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        order_by=None,  # type: Optional[str]
+        top=None,  # type: Optional[int]
+        skip=None,  # type: Optional[str]
+        tags=None,  # type: Optional[str]
+        list_view_type=None,  # type: Optional[Union[str, "_models.ListViewType"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.DataVersionBaseResourceArmPaginatedResult"]
+        """List data versions in the data container.
+
+        List data versions in the data container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Data container's name.
+        :type name: str
+        :param order_by: Please choose OrderBy value from ['createdtime', 'modifiedtime'].
+        :type order_by: str
+        :param top: Top count of results, top count cannot be greater than the page size.
+                                       If topCount > page size, results with be default page size count
+         will be returned.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param tags: Comma-separated list of tag names (and optionally values). Example:
+         tag1,tag2=value2.
+        :type tags: str
+        :param list_view_type: [ListViewType.ActiveOnly, ListViewType.ArchivedOnly,
+         ListViewType.All]View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either DataVersionBaseResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.DataVersionBaseResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataVersionBaseResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    tags=tags,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    tags=tags,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("DataVersionBaseResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.DataVersionBase"
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataVersionBase, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionBase
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataVersionBase"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('DataVersionBase', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        body,  # type: "_models.DataVersionBase"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.DataVersionBase"
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name.
+        :type name: str
+        :param version: Version identifier.
+        :type version: str
+        :param body: Version entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.DataVersionBase
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataVersionBase, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionBase
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataVersionBase"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'DataVersionBase')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('DataVersionBase', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('DataVersionBase', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/data/{name}/versions/{version}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_datastores_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_datastores_operations.py
new file mode 100644
index 00000000..a2d67b55
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_datastores_operations.py
@@ -0,0 +1,671 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, List, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    count = kwargs.pop('count', 30)  # type: Optional[int]
+    is_default = kwargs.pop('is_default', None)  # type: Optional[bool]
+    names = kwargs.pop('names', None)  # type: Optional[List[str]]
+    search_text = kwargs.pop('search_text', None)  # type: Optional[str]
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    order_by_asc = kwargs.pop('order_by_asc', False)  # type: Optional[bool]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if count is not None:
+        _query_parameters['count'] = _SERIALIZER.query("count", count, 'int')
+    if is_default is not None:
+        _query_parameters['isDefault'] = _SERIALIZER.query("is_default", is_default, 'bool')
+    if names is not None:
+        _query_parameters['names'] = _SERIALIZER.query("names", names, '[str]', div=',')
+    if search_text is not None:
+        _query_parameters['searchText'] = _SERIALIZER.query("search_text", search_text, 'str')
+    if order_by is not None:
+        _query_parameters['orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if order_by_asc is not None:
+        _query_parameters['orderByAsc'] = _SERIALIZER.query("order_by_asc", order_by_asc, 'bool')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+    skip_validation = kwargs.pop('skip_validation', False)  # type: Optional[bool]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip_validation is not None:
+        _query_parameters['skipValidation'] = _SERIALIZER.query("skip_validation", skip_validation, 'bool')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_secrets_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}/listSecrets")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class DatastoresOperations(object):
+    """DatastoresOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        skip=None,  # type: Optional[str]
+        count=30,  # type: Optional[int]
+        is_default=None,  # type: Optional[bool]
+        names=None,  # type: Optional[List[str]]
+        search_text=None,  # type: Optional[str]
+        order_by=None,  # type: Optional[str]
+        order_by_asc=False,  # type: Optional[bool]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.DatastoreResourceArmPaginatedResult"]
+        """List datastores.
+
+        List datastores.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param count: Maximum number of results to return.
+        :type count: int
+        :param is_default: Filter down to the workspace default datastore.
+        :type is_default: bool
+        :param names: Names of datastores to return.
+        :type names: list[str]
+        :param search_text: Text to search for in the datastore names.
+        :type search_text: str
+        :param order_by: Order by property (createdtime | modifiedtime | name).
+        :type order_by: str
+        :param order_by_asc: Order by property in ascending order.
+        :type order_by_asc: bool
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either DatastoreResourceArmPaginatedResult or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.DatastoreResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatastoreResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    count=count,
+                    is_default=is_default,
+                    names=names,
+                    search_text=search_text,
+                    order_by=order_by,
+                    order_by_asc=order_by_asc,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    count=count,
+                    is_default=is_default,
+                    names=names,
+                    search_text=search_text,
+                    order_by=order_by,
+                    order_by_asc=order_by_asc,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("DatastoreResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete datastore.
+
+        Delete datastore.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Datastore name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.Datastore"
+        """Get datastore.
+
+        Get datastore.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Datastore name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Datastore, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Datastore
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Datastore"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('Datastore', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        body,  # type: "_models.Datastore"
+        skip_validation=False,  # type: Optional[bool]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.Datastore"
+        """Create or update datastore.
+
+        Create or update datastore.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Datastore name.
+        :type name: str
+        :param body: Datastore entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.Datastore
+        :param skip_validation: Flag to skip validation.
+        :type skip_validation: bool
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Datastore, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Datastore
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Datastore"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'Datastore')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            skip_validation=skip_validation,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('Datastore', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('Datastore', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def list_secrets(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.DatastoreSecrets"
+        """Get datastore secrets.
+
+        Get datastore secrets.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Datastore name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DatastoreSecrets, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DatastoreSecrets
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatastoreSecrets"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_secrets_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.list_secrets.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('DatastoreSecrets', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_secrets.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}/listSecrets"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_environment_containers_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_environment_containers_operations.py
new file mode 100644
index 00000000..ee1f061b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_environment_containers_operations.py
@@ -0,0 +1,519 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    list_view_type = kwargs.pop('list_view_type', None)  # type: Optional[Union[str, "_models.ListViewType"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if list_view_type is not None:
+        _query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class EnvironmentContainersOperations(object):
+    """EnvironmentContainersOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        skip=None,  # type: Optional[str]
+        list_view_type=None,  # type: Optional[Union[str, "_models.ListViewType"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.EnvironmentContainerResourceArmPaginatedResult"]
+        """List environment containers.
+
+        List environment containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either EnvironmentContainerResourceArmPaginatedResult or
+         the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.EnvironmentContainerResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentContainerResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("EnvironmentContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.EnvironmentContainer"
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('EnvironmentContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        body,  # type: "_models.EnvironmentContainer"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.EnvironmentContainer"
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param body: Container entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'EnvironmentContainer')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('EnvironmentContainer', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('EnvironmentContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_environment_versions_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_environment_versions_operations.py
new file mode 100644
index 00000000..c262a3ac
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_environment_versions_operations.py
@@ -0,0 +1,560 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    top = kwargs.pop('top', None)  # type: Optional[int]
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    list_view_type = kwargs.pop('list_view_type', None)  # type: Optional[Union[str, "_models.ListViewType"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if order_by is not None:
+        _query_parameters['$orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if top is not None:
+        _query_parameters['$top'] = _SERIALIZER.query("top", top, 'int')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if list_view_type is not None:
+        _query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class EnvironmentVersionsOperations(object):
+    """EnvironmentVersionsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        order_by=None,  # type: Optional[str]
+        top=None,  # type: Optional[int]
+        skip=None,  # type: Optional[str]
+        list_view_type=None,  # type: Optional[Union[str, "_models.ListViewType"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.EnvironmentVersionResourceArmPaginatedResult"]
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Maximum number of records to return.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either EnvironmentVersionResourceArmPaginatedResult or
+         the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.EnvironmentVersionResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentVersionResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("EnvironmentVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.EnvironmentVersion"
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('EnvironmentVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        body,  # type: "_models.EnvironmentVersion"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.EnvironmentVersion"
+        """Creates or updates an EnvironmentVersion.
+
+        Creates or updates an EnvironmentVersion.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Name of EnvironmentVersion. This is case-sensitive.
+        :type name: str
+        :param version: Version of EnvironmentVersion.
+        :type version: str
+        :param body: Definition of EnvironmentVersion.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EnvironmentVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'EnvironmentVersion')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('EnvironmentVersion', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('EnvironmentVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/environments/{name}/versions/{version}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_jobs_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_jobs_operations.py
new file mode 100644
index 00000000..e27f83e4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_jobs_operations.py
@@ -0,0 +1,755 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    job_type = kwargs.pop('job_type', None)  # type: Optional[str]
+    tag = kwargs.pop('tag', None)  # type: Optional[str]
+    list_view_type = kwargs.pop('list_view_type', None)  # type: Optional[Union[str, "_models.ListViewType"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if job_type is not None:
+        _query_parameters['jobType'] = _SERIALIZER.query("job_type", job_type, 'str')
+    if tag is not None:
+        _query_parameters['tag'] = _SERIALIZER.query("tag", tag, 'str')
+    if list_view_type is not None:
+        _query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "id": _SERIALIZER.url("id", id, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "id": _SERIALIZER.url("id", id, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "id": _SERIALIZER.url("id", id, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_cancel_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}/cancel")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "id": _SERIALIZER.url("id", id, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class JobsOperations(object):
+    """JobsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        skip=None,  # type: Optional[str]
+        job_type=None,  # type: Optional[str]
+        tag=None,  # type: Optional[str]
+        list_view_type=None,  # type: Optional[Union[str, "_models.ListViewType"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.JobBaseResourceArmPaginatedResult"]
+        """Lists Jobs in the workspace.
+
+        Lists Jobs in the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param job_type: Type of job to be returned.
+        :type job_type: str
+        :param tag: Jobs returned will have this tag key.
+        :type tag: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either JobBaseResourceArmPaginatedResult or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.JobBaseResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.JobBaseResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    job_type=job_type,
+                    tag=tag,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    job_type=job_type,
+                    tag=tag,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("JobBaseResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs"}  # type: ignore
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        id,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        id,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Deletes a Job (asynchronous).
+
+        Deletes a Job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param id: The name and identifier for the Job. This is case-sensitive.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                id=id,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}"}  # type: ignore
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        id,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.JobBase"
+        """Gets a Job by name/id.
+
+        Gets a Job by name/id.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param id: The name and identifier for the Job. This is case-sensitive.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: JobBase, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.JobBase
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.JobBase"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('JobBase', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        id,  # type: str
+        body,  # type: "_models.JobBase"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.JobBase"
+        """Creates and executes a Job.
+
+        Creates and executes a Job.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param id: The name and identifier for the Job. This is case-sensitive.
+        :type id: str
+        :param body: Job definition object.
+        :type body: ~azure.mgmt.machinelearningservices.models.JobBase
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: JobBase, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.JobBase
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.JobBase"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'JobBase')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('JobBase', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('JobBase', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}"}  # type: ignore
+
+
+    def _cancel_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        id,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_cancel_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            api_version=api_version,
+            template_url=self._cancel_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _cancel_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}/cancel"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_cancel(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        id,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Cancels a Job (asynchronous).
+
+        Cancels a Job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param id: The name and identifier for the Job. This is case-sensitive.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._cancel_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                id=id,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_cancel.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}/cancel"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_model_containers_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_model_containers_operations.py
new file mode 100644
index 00000000..fd47a2ec
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_model_containers_operations.py
@@ -0,0 +1,527 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    count = kwargs.pop('count', None)  # type: Optional[int]
+    list_view_type = kwargs.pop('list_view_type', None)  # type: Optional[Union[str, "_models.ListViewType"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if count is not None:
+        _query_parameters['count'] = _SERIALIZER.query("count", count, 'int')
+    if list_view_type is not None:
+        _query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class ModelContainersOperations(object):
+    """ModelContainersOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        skip=None,  # type: Optional[str]
+        count=None,  # type: Optional[int]
+        list_view_type=None,  # type: Optional[Union[str, "_models.ListViewType"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.ModelContainerResourceArmPaginatedResult"]
+        """List model containers.
+
+        List model containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param count: Maximum number of results to return.
+        :type count: int
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ModelContainerResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ModelContainerResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelContainerResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    count=count,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    count=count,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ModelContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ModelContainer"
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ModelContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        body,  # type: "_models.ModelContainer"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ModelContainer"
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param body: Container entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelContainer, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelContainer
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelContainer"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'ModelContainer')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('ModelContainer', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('ModelContainer', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_model_versions_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_model_versions_operations.py
new file mode 100644
index 00000000..b0195cd2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_model_versions_operations.py
@@ -0,0 +1,610 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    top = kwargs.pop('top', None)  # type: Optional[int]
+    version = kwargs.pop('version', None)  # type: Optional[str]
+    description = kwargs.pop('description', None)  # type: Optional[str]
+    offset = kwargs.pop('offset', None)  # type: Optional[int]
+    tags = kwargs.pop('tags', None)  # type: Optional[str]
+    properties = kwargs.pop('properties', None)  # type: Optional[str]
+    feed = kwargs.pop('feed', None)  # type: Optional[str]
+    list_view_type = kwargs.pop('list_view_type', None)  # type: Optional[Union[str, "_models.ListViewType"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if order_by is not None:
+        _query_parameters['$orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if top is not None:
+        _query_parameters['$top'] = _SERIALIZER.query("top", top, 'int')
+    if version is not None:
+        _query_parameters['version'] = _SERIALIZER.query("version", version, 'str')
+    if description is not None:
+        _query_parameters['description'] = _SERIALIZER.query("description", description, 'str')
+    if offset is not None:
+        _query_parameters['offset'] = _SERIALIZER.query("offset", offset, 'int')
+    if tags is not None:
+        _query_parameters['tags'] = _SERIALIZER.query("tags", tags, 'str')
+    if properties is not None:
+        _query_parameters['properties'] = _SERIALIZER.query("properties", properties, 'str')
+    if feed is not None:
+        _query_parameters['feed'] = _SERIALIZER.query("feed", feed, 'str')
+    if list_view_type is not None:
+        _query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions/{version}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class ModelVersionsOperations(object):
+    """ModelVersionsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        skip=None,  # type: Optional[str]
+        order_by=None,  # type: Optional[str]
+        top=None,  # type: Optional[int]
+        version=None,  # type: Optional[str]
+        description=None,  # type: Optional[str]
+        offset=None,  # type: Optional[int]
+        tags=None,  # type: Optional[str]
+        properties=None,  # type: Optional[str]
+        feed=None,  # type: Optional[str]
+        list_view_type=None,  # type: Optional[Union[str, "_models.ListViewType"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.ModelVersionResourceArmPaginatedResult"]
+        """List model versions.
+
+        List model versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Model name. This is case-sensitive.
+        :type name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Maximum number of records to return.
+        :type top: int
+        :param version: Model version.
+        :type version: str
+        :param description: Model description.
+        :type description: str
+        :param offset: Number of initial results to skip.
+        :type offset: int
+        :param tags: Comma-separated list of tag names (and optionally values). Example:
+         tag1,tag2=value2.
+        :type tags: str
+        :param properties: Comma-separated list of property names (and optionally values). Example:
+         prop1,prop2=value2.
+        :type properties: str
+        :param feed: Name of the feed.
+        :type feed: str
+        :param list_view_type: View type for including/excluding (for example) archived entities.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ModelVersionResourceArmPaginatedResult or the
+         result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ModelVersionResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelVersionResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    skip=skip,
+                    order_by=order_by,
+                    top=top,
+                    version=version,
+                    description=description,
+                    offset=offset,
+                    tags=tags,
+                    properties=properties,
+                    feed=feed,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    api_version=api_version,
+                    skip=skip,
+                    order_by=order_by,
+                    top=top,
+                    version=version,
+                    description=description,
+                    offset=offset,
+                    tags=tags,
+                    properties=properties,
+                    feed=feed,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ModelVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions"}  # type: ignore
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ModelVersion"
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ModelVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions/{version}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        body,  # type: "_models.ModelVersion"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ModelVersion"
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Container name. This is case-sensitive.
+        :type name: str
+        :param version: Version identifier. This is case-sensitive.
+        :type version: str
+        :param body: Version entity to create or update.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelVersion
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelVersion, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelVersion
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ModelVersion"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'ModelVersion')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if response.status_code == 200:
+            deserialized = self._deserialize('ModelVersion', pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize('ModelVersion', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/models/{name}/versions/{version}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_online_deployments_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_online_deployments_operations.py
new file mode 100644
index 00000000..5bb9ef9e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_online_deployments_operations.py
@@ -0,0 +1,1150 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    top = kwargs.pop('top', None)  # type: Optional[int]
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if order_by is not None:
+        _query_parameters['$orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if top is not None:
+        _query_parameters['$top'] = _SERIALIZER.query("top", top, 'int')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    deployment_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+        "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    deployment_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+        "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    deployment_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+        "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PATCH",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    deployment_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+        "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_logs_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    deployment_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}/getLogs")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+        "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_skus_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    deployment_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    count = kwargs.pop('count', None)  # type: Optional[int]
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}/skus")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+        "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if count is not None:
+        _query_parameters['count'] = _SERIALIZER.query("count", count, 'int')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class OnlineDeploymentsOperations(object):
+    """OnlineDeploymentsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        order_by=None,  # type: Optional[str]
+        top=None,  # type: Optional[int]
+        skip=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.OnlineDeploymentTrackedResourceArmPaginatedResult"]
+        """List Inference Endpoint Deployments.
+
+        List Inference Endpoint Deployments.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param order_by: Ordering of list.
+        :type order_by: str
+        :param top: Top of list.
+        :type top: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either OnlineDeploymentTrackedResourceArmPaginatedResult
+         or the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.OnlineDeploymentTrackedResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineDeploymentTrackedResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    api_version=api_version,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("OnlineDeploymentTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments"}  # type: ignore
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Delete Inference Endpoint Deployment (asynchronous).
+
+        Delete Inference Endpoint Deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference Endpoint Deployment name.
+        :type deployment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.OnlineDeployment"
+        """Get Inference Deployment Deployment.
+
+        Get Inference Deployment Deployment.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference Endpoint Deployment name.
+        :type deployment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: OnlineDeployment, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.OnlineDeployment
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineDeployment"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    def _update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        body,  # type: "_models.PartialMinimalTrackedResourceWithSku"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Optional["_models.OnlineDeployment"]
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.OnlineDeployment"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'PartialMinimalTrackedResourceWithSku')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        body,  # type: "_models.PartialMinimalTrackedResourceWithSku"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.OnlineDeployment"]
+        """Update Online Deployment (asynchronous).
+
+        Update Online Deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference Endpoint Deployment name.
+        :type deployment_name: str
+        :param body: Online Endpoint entity to apply during operation.
+        :type body: ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithSku
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either OnlineDeployment or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.OnlineDeployment]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineDeployment"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        body,  # type: "_models.OnlineDeployment"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.OnlineDeployment"
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineDeployment"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'OnlineDeployment')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        body,  # type: "_models.OnlineDeployment"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.OnlineDeployment"]
+        """Create or update Inference Endpoint Deployment (asynchronous).
+
+        Create or update Inference Endpoint Deployment (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference Endpoint Deployment name.
+        :type deployment_name: str
+        :param body: Inference Endpoint entity to apply during operation.
+        :type body: ~azure.mgmt.machinelearningservices.models.OnlineDeployment
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either OnlineDeployment or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.OnlineDeployment]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineDeployment"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                deployment_name=deployment_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('OnlineDeployment', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}"}  # type: ignore
+
+    @distributed_trace
+    def get_logs(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        body,  # type: "_models.DeploymentLogsRequest"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.DeploymentLogs"
+        """Polls an Endpoint operation.
+
+        Polls an Endpoint operation.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: The name and identifier for the endpoint.
+        :type deployment_name: str
+        :param body: The request containing parameters for retrieving logs.
+        :type body: ~azure.mgmt.machinelearningservices.models.DeploymentLogsRequest
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DeploymentLogs, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DeploymentLogs
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DeploymentLogs"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'DeploymentLogsRequest')
+
+        request = build_get_logs_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            deployment_name=deployment_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.get_logs.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('DeploymentLogs', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_logs.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}/getLogs"}  # type: ignore
+
+
+    @distributed_trace
+    def list_skus(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        deployment_name,  # type: str
+        count=None,  # type: Optional[int]
+        skip=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.SkuResourceArmPaginatedResult"]
+        """List Inference Endpoint Deployment Skus.
+
+        List Inference Endpoint Deployment Skus.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Inference endpoint name.
+        :type endpoint_name: str
+        :param deployment_name: Inference Endpoint Deployment name.
+        :type deployment_name: str
+        :param count: Number of Skus to be retrieved in a page of results.
+        :type count: int
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either SkuResourceArmPaginatedResult or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.SkuResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.SkuResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_skus_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    deployment_name=deployment_name,
+                    api_version=api_version,
+                    count=count,
+                    skip=skip,
+                    template_url=self.list_skus.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_skus_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    endpoint_name=endpoint_name,
+                    deployment_name=deployment_name,
+                    api_version=api_version,
+                    count=count,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("SkuResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list_skus.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/deployments/{deploymentName}/skus"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_online_endpoints_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_online_endpoints_operations.py
new file mode 100644
index 00000000..20dceffa
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_online_endpoints_operations.py
@@ -0,0 +1,1257 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    name = kwargs.pop('name', None)  # type: Optional[str]
+    count = kwargs.pop('count', None)  # type: Optional[int]
+    compute_type = kwargs.pop('compute_type', None)  # type: Optional[Union[str, "_models.EndpointComputeType"]]
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    tags = kwargs.pop('tags', None)  # type: Optional[str]
+    properties = kwargs.pop('properties', None)  # type: Optional[str]
+    order_by = kwargs.pop('order_by', None)  # type: Optional[Union[str, "_models.OrderString"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if name is not None:
+        _query_parameters['name'] = _SERIALIZER.query("name", name, 'str')
+    if count is not None:
+        _query_parameters['count'] = _SERIALIZER.query("count", count, 'int')
+    if compute_type is not None:
+        _query_parameters['computeType'] = _SERIALIZER.query("compute_type", compute_type, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if tags is not None:
+        _query_parameters['tags'] = _SERIALIZER.query("tags", tags, 'str')
+    if properties is not None:
+        _query_parameters['properties'] = _SERIALIZER.query("properties", properties, 'str')
+    if order_by is not None:
+        _query_parameters['orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PATCH",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_keys_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/listKeys")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_regenerate_keys_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/regenerateKeys")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_token_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    endpoint_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/token")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class OnlineEndpointsOperations(object):
+    """OnlineEndpointsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name=None,  # type: Optional[str]
+        count=None,  # type: Optional[int]
+        compute_type=None,  # type: Optional[Union[str, "_models.EndpointComputeType"]]
+        skip=None,  # type: Optional[str]
+        tags=None,  # type: Optional[str]
+        properties=None,  # type: Optional[str]
+        order_by=None,  # type: Optional[Union[str, "_models.OrderString"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.OnlineEndpointTrackedResourceArmPaginatedResult"]
+        """List Online Endpoints.
+
+        List Online Endpoints.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Name of the endpoint.
+        :type name: str
+        :param count: Number of endpoints to be retrieved in a page of results.
+        :type count: int
+        :param compute_type: EndpointComputeType to be filtered by.
+        :type compute_type: str or ~azure.mgmt.machinelearningservices.models.EndpointComputeType
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param tags: A set of tags with which to filter the returned models. It is a comma separated
+         string of tags key or tags key=value. Example: tagKey1,tagKey2,tagKey3=value3 .
+        :type tags: str
+        :param properties: A set of properties with which to filter the returned models. It is a comma
+         separated string of properties key and/or properties key=value Example:
+         propKey1,propKey2,propKey3=value3 .
+        :type properties: str
+        :param order_by: The option to order the response.
+        :type order_by: str or ~azure.mgmt.machinelearningservices.models.OrderString
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either OnlineEndpointTrackedResourceArmPaginatedResult or
+         the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.OnlineEndpointTrackedResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineEndpointTrackedResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    name=name,
+                    count=count,
+                    compute_type=compute_type,
+                    skip=skip,
+                    tags=tags,
+                    properties=properties,
+                    order_by=order_by,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    name=name,
+                    count=count,
+                    compute_type=compute_type,
+                    skip=skip,
+                    tags=tags,
+                    properties=properties,
+                    order_by=order_by,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("OnlineEndpointTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints"}  # type: ignore
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Delete Online Endpoint (asynchronous).
+
+        Delete Online Endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.OnlineEndpoint"
+        """Get Online Endpoint.
+
+        Get Online Endpoint.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: OnlineEndpoint, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.OnlineEndpoint
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineEndpoint"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+
+    def _update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        body,  # type: "_models.PartialMinimalTrackedResourceWithIdentity"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Optional["_models.OnlineEndpoint"]
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.OnlineEndpoint"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'PartialMinimalTrackedResourceWithIdentity')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        body,  # type: "_models.PartialMinimalTrackedResourceWithIdentity"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.OnlineEndpoint"]
+        """Update Online Endpoint (asynchronous).
+
+        Update Online Endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :param body: Online Endpoint entity to apply during operation.
+        :type body:
+         ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithIdentity
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either OnlineEndpoint or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.OnlineEndpoint]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineEndpoint"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        body,  # type: "_models.OnlineEndpoint"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.OnlineEndpoint"
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineEndpoint"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'OnlineEndpoint')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        body,  # type: "_models.OnlineEndpoint"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.OnlineEndpoint"]
+        """Create or update Online Endpoint (asynchronous).
+
+        Create or update Online Endpoint (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :param body: Online Endpoint entity to apply during operation.
+        :type body: ~azure.mgmt.machinelearningservices.models.OnlineEndpoint
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either OnlineEndpoint or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.OnlineEndpoint]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.OnlineEndpoint"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('OnlineEndpoint', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}"}  # type: ignore
+
+    @distributed_trace
+    def list_keys(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.EndpointAuthKeys"
+        """List EndpointAuthKeys for an Endpoint using Key-based authentication.
+
+        List EndpointAuthKeys for an Endpoint using Key-based authentication.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EndpointAuthKeys, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EndpointAuthKeys
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EndpointAuthKeys"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self.list_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('EndpointAuthKeys', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/listKeys"}  # type: ignore
+
+
+    def _regenerate_keys_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        body,  # type: "_models.RegenerateEndpointKeysRequest"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'RegenerateEndpointKeysRequest')
+
+        request = build_regenerate_keys_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._regenerate_keys_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _regenerate_keys_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/regenerateKeys"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_regenerate_keys(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        body,  # type: "_models.RegenerateEndpointKeysRequest"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Regenerate EndpointAuthKeys for an Endpoint using Key-based authentication (asynchronous).
+
+        Regenerate EndpointAuthKeys for an Endpoint using Key-based authentication (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :param body: RegenerateKeys request .
+        :type body: ~azure.mgmt.machinelearningservices.models.RegenerateEndpointKeysRequest
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._regenerate_keys_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                endpoint_name=endpoint_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_regenerate_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/regenerateKeys"}  # type: ignore
+
+    @distributed_trace
+    def get_token(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        endpoint_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.EndpointAuthToken"
+        """Retrieve a valid AAD token for an Endpoint using AMLToken-based authentication.
+
+        Retrieve a valid AAD token for an Endpoint using AMLToken-based authentication.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param endpoint_name: Online Endpoint name.
+        :type endpoint_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EndpointAuthToken, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EndpointAuthToken
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.EndpointAuthToken"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_token_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            endpoint_name=endpoint_name,
+            api_version=api_version,
+            template_url=self.get_token.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('EndpointAuthToken', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_token.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/onlineEndpoints/{endpointName}/token"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_operations.py
new file mode 100644
index 00000000..6ba39620
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_operations.py
@@ -0,0 +1,154 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/providers/Microsoft.MachineLearningServices/operations")
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class Operations(object):
+    """Operations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.AmlOperationListResult"]
+        """Lists all of the available Azure Machine Learning Workspaces REST API operations.
+
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either AmlOperationListResult or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.AmlOperationListResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.AmlOperationListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    api_version=api_version,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("AmlOperationListResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/providers/Microsoft.MachineLearningServices/operations"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_private_endpoint_connections_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_private_endpoint_connections_operations.py
new file mode 100644
index 00000000..7b0dfce5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_private_endpoint_connections_operations.py
@@ -0,0 +1,494 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    subscription_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    private_endpoint_connection_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections/{privateEndpointConnectionName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "privateEndpointConnectionName": _SERIALIZER.url("private_endpoint_connection_name", private_endpoint_connection_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    private_endpoint_connection_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections/{privateEndpointConnectionName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "privateEndpointConnectionName": _SERIALIZER.url("private_endpoint_connection_name", private_endpoint_connection_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    private_endpoint_connection_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections/{privateEndpointConnectionName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "privateEndpointConnectionName": _SERIALIZER.url("private_endpoint_connection_name", private_endpoint_connection_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class PrivateEndpointConnectionsOperations(object):
+    """PrivateEndpointConnectionsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.PrivateEndpointConnectionListResult"]
+        """List all the private endpoint connections associated with the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PrivateEndpointConnectionListResult or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PrivateEndpointConnectionListResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PrivateEndpointConnectionListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    subscription_id=self._config.subscription_id,
+                    api_version=api_version,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    subscription_id=self._config.subscription_id,
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("PrivateEndpointConnectionListResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections"}  # type: ignore
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        private_endpoint_connection_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.PrivateEndpointConnection"
+        """Gets the specified private endpoint connection associated with the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param private_endpoint_connection_name: The name of the private endpoint connection associated
+         with the workspace.
+        :type private_endpoint_connection_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: PrivateEndpointConnection, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PrivateEndpointConnection"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            private_endpoint_connection_name=private_endpoint_connection_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('PrivateEndpointConnection', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections/{privateEndpointConnectionName}"}  # type: ignore
+
+
+    @distributed_trace
+    def create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        private_endpoint_connection_name,  # type: str
+        properties,  # type: "_models.PrivateEndpointConnection"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.PrivateEndpointConnection"
+        """Update the state of specified private endpoint connection associated with the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param private_endpoint_connection_name: The name of the private endpoint connection associated
+         with the workspace.
+        :type private_endpoint_connection_name: str
+        :param properties: The private endpoint connection properties.
+        :type properties: ~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: PrivateEndpointConnection, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PrivateEndpointConnection"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(properties, 'PrivateEndpointConnection')
+
+        request = build_create_or_update_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            private_endpoint_connection_name=private_endpoint_connection_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_or_update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('PrivateEndpointConnection', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections/{privateEndpointConnectionName}"}  # type: ignore
+
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        private_endpoint_connection_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """Deletes the specified private endpoint connection associated with the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param private_endpoint_connection_name: The name of the private endpoint connection associated
+         with the workspace.
+        :type private_endpoint_connection_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            private_endpoint_connection_name=private_endpoint_connection_name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateEndpointConnections/{privateEndpointConnectionName}"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_private_link_resources_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_private_link_resources_operations.py
new file mode 100644
index 00000000..bda5baaa
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_private_link_resources_operations.py
@@ -0,0 +1,150 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateLinkResources")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class PrivateLinkResourcesOperations(object):
+    """PrivateLinkResourcesOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.PrivateLinkResourceListResult"
+        """Gets the private link resources that need to be created for a workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: PrivateLinkResourceListResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.PrivateLinkResourceListResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PrivateLinkResourceListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('PrivateLinkResourceListResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/privateLinkResources"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_quotas_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_quotas_operations.py
new file mode 100644
index 00000000..165211ca
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_quotas_operations.py
@@ -0,0 +1,269 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_update_request(
+    location,  # type: str
+    subscription_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/updateQuotas")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "location": _SERIALIZER.url("location", location, 'str', pattern=r'^[-\w\._]+$'),
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_request(
+    subscription_id,  # type: str
+    location,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/quotas")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "location": _SERIALIZER.url("location", location, 'str', pattern=r'^[-\w\._]+$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class QuotasOperations(object):
+    """QuotasOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def update(
+        self,
+        location,  # type: str
+        parameters,  # type: "_models.QuotaUpdateParameters"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.UpdateWorkspaceQuotasResult"
+        """Update quota for each VM family in workspace.
+
+        :param location: The location for update quota is queried.
+        :type location: str
+        :param parameters: Quota update parameters.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.QuotaUpdateParameters
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: UpdateWorkspaceQuotasResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.UpdateWorkspaceQuotasResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.UpdateWorkspaceQuotasResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'QuotaUpdateParameters')
+
+        request = build_update_request(
+            location=location,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.update.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('UpdateWorkspaceQuotasResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    update.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/updateQuotas"}  # type: ignore
+
+
+    @distributed_trace
+    def list(
+        self,
+        location,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.ListWorkspaceQuotas"]
+        """Gets the currently assigned Workspace Quotas based on VMFamily.
+
+        :param location: The location for which resource usage is queried.
+        :type location: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ListWorkspaceQuotas or the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ListWorkspaceQuotas]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListWorkspaceQuotas"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    location=location,
+                    api_version=api_version,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    location=location,
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ListWorkspaceQuotas", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/quotas"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_schedules_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_schedules_operations.py
new file mode 100644
index 00000000..f6063526
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_schedules_operations.py
@@ -0,0 +1,643 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+    list_view_type = kwargs.pop('list_view_type', None)  # type: Optional[Union[str, "_models.ScheduleListViewType"]]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+    if list_view_type is not None:
+        _query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str', pattern=r'^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class SchedulesOperations(object):
+    """SchedulesOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        skip=None,  # type: Optional[str]
+        list_view_type=None,  # type: Optional[Union[str, "_models.ScheduleListViewType"]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.ScheduleResourceArmPaginatedResult"]
+        """List schedules in specified workspace.
+
+        List schedules in specified workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :param list_view_type: Status filter for schedule.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ScheduleListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ScheduleResourceArmPaginatedResult or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ScheduleResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ScheduleResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ScheduleResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules"}  # type: ignore
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Delete schedule.
+
+        Delete schedule.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Schedule name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                name=name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}"}  # type: ignore
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.Schedule"
+        """Get schedule.
+
+        Get schedule.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Schedule name.
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Schedule, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Schedule
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Schedule"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('Schedule', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}"}  # type: ignore
+
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        body,  # type: "_models.Schedule"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.Schedule"
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Schedule"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(body, 'Schedule')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('Schedule', pipeline_response)
+
+        if response.status_code == 201:
+            response_headers['x-ms-async-operation-timeout']=self._deserialize('duration', response.headers.get('x-ms-async-operation-timeout'))
+            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
+            
+            deserialized = self._deserialize('Schedule', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        body,  # type: "_models.Schedule"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.Schedule"]
+        """Create or update schedule.
+
+        Create or update schedule.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param name: Schedule name.
+        :type name: str
+        :param body: Schedule definition.
+        :type body: ~azure.mgmt.machinelearningservices.models.Schedule
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either Schedule or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Schedule]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Schedule"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                name=name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('Schedule', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_usages_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_usages_operations.py
new file mode 100644
index 00000000..d35d0061
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_usages_operations.py
@@ -0,0 +1,169 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    location,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/usages")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "location": _SERIALIZER.url("location", location, 'str', pattern=r'^[-\w\._]+$'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class UsagesOperations(object):
+    """UsagesOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        location,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.ListUsagesResult"]
+        """Gets the current usage information as well as limits for AML resources for given subscription
+        and location.
+
+        :param location: The location for which resource usage is queried.
+        :type location: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ListUsagesResult or the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ListUsagesResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListUsagesResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    location=location,
+                    api_version=api_version,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    location=location,
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ListUsagesResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/usages"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_virtual_machine_sizes_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_virtual_machine_sizes_operations.py
new file mode 100644
index 00000000..57dac5e0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_virtual_machine_sizes_operations.py
@@ -0,0 +1,144 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    location,  # type: str
+    subscription_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/vmSizes")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "location": _SERIALIZER.url("location", location, 'str', pattern=r'^[-\w\._]+$'),
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class VirtualMachineSizesOperations(object):
+    """VirtualMachineSizesOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        location,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.VirtualMachineSizeListResult"
+        """Returns supported VM Sizes in a location.
+
+        :param location: The location upon which virtual-machine-sizes is queried.
+        :type location: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: VirtualMachineSizeListResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.VirtualMachineSizeListResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.VirtualMachineSizeListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_request(
+            location=location,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.list.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('VirtualMachineSizeListResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/vmSizes"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_workspace_connections_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_workspace_connections_operations.py
new file mode 100644
index 00000000..e4da5821
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_workspace_connections_operations.py
@@ -0,0 +1,508 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_create_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    connection_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections/{connectionName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    connection_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections/{connectionName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    connection_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections/{connectionName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    target = kwargs.pop('target', None)  # type: Optional[str]
+    category = kwargs.pop('category', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if target is not None:
+        _query_parameters['target'] = _SERIALIZER.query("target", target, 'str')
+    if category is not None:
+        _query_parameters['category'] = _SERIALIZER.query("category", category, 'str')
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class WorkspaceConnectionsOperations(object):
+    """WorkspaceConnectionsOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def create(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        connection_name,  # type: str
+        parameters,  # type: "_models.WorkspaceConnectionPropertiesV2BasicResource"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.WorkspaceConnectionPropertiesV2BasicResource"
+        """create.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param connection_name: Friendly name of the workspace connection.
+        :type connection_name: str
+        :param parameters: The object for creating or updating a new workspace connection.
+        :type parameters:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: WorkspaceConnectionPropertiesV2BasicResource, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.WorkspaceConnectionPropertiesV2BasicResource"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'WorkspaceConnectionPropertiesV2BasicResource')
+
+        request = build_create_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            connection_name=connection_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('WorkspaceConnectionPropertiesV2BasicResource', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections/{connectionName}"}  # type: ignore
+
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        connection_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.WorkspaceConnectionPropertiesV2BasicResource"
+        """get.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param connection_name: Friendly name of the workspace connection.
+        :type connection_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: WorkspaceConnectionPropertiesV2BasicResource, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.WorkspaceConnectionPropertiesV2BasicResource"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            connection_name=connection_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('WorkspaceConnectionPropertiesV2BasicResource', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections/{connectionName}"}  # type: ignore
+
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        connection_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        """delete.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param connection_name: Friendly name of the workspace connection.
+        :type connection_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None, or the result of cls(response)
+        :rtype: None
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            connection_name=connection_name,
+            api_version=api_version,
+            template_url=self.delete.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections/{connectionName}"}  # type: ignore
+
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        target=None,  # type: Optional[str]
+        category=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult"]
+        """list.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param target: Target of the workspace connection.
+        :type target: str
+        :param category: Category of the workspace connection.
+        :type category: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either
+         WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult or the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    target=target,
+                    category=category,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    target=target,
+                    category=category,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/connections"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_workspace_features_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_workspace_features_operations.py
new file mode 100644
index 00000000..d70e9526
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_workspace_features_operations.py
@@ -0,0 +1,176 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/features")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class WorkspaceFeaturesOperations(object):
+    """WorkspaceFeaturesOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.ListAmlUserFeatureResult"]
+        """Lists all enabled features for a workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ListAmlUserFeatureResult or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ListAmlUserFeatureResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListAmlUserFeatureResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    template_url=self.list.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    api_version=api_version,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ListAmlUserFeatureResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/features"}  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_workspaces_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_workspaces_operations.py
new file mode 100644
index 00000000..a95e8f4c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/operations/_workspaces_operations.py
@@ -0,0 +1,1832 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+from typing import TYPE_CHECKING
+
+from msrest import Serializer
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Iterable, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_create_or_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_update_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PATCH",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_by_resource_group_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_diagnose_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/diagnose")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        _header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_keys_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listKeys")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_resync_keys_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/resyncKeys")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_by_subscription_request(
+    subscription_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+    skip = kwargs.pop('skip', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/workspaces")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+    if skip is not None:
+        _query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_notebook_access_token_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listNotebookAccessToken")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_prepare_notebook_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/prepareNotebook")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_storage_account_keys_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listStorageAccountKeys")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_notebook_keys_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listNotebookKeys")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+
+def build_list_outbound_network_dependencies_endpoints_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+    accept = "application/json"
+    # Construct URL
+    _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/outboundNetworkDependenciesEndpoints")  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    _url = _format_url_section(_url, **path_format_arguments)
+
+    # Construct parameters
+    _query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
+
+    # Construct headers
+    _header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=_url,
+        params=_query_parameters,
+        headers=_header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class WorkspacesOperations(object):
+    """WorkspacesOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.Workspace"
+        """Gets the properties of the specified machine learning workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Workspace, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Workspace
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Workspace"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_get_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.get.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('Workspace', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        parameters,  # type: "_models.Workspace"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Optional["_models.Workspace"]
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.Workspace"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'Workspace')
+
+        request = build_create_or_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._create_or_update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        if response.status_code == 200:
+            deserialized = self._deserialize('Workspace', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        parameters,  # type: "_models.Workspace"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.Workspace"]
+        """Creates or updates a workspace with the specified parameters.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param parameters: The parameters for creating or updating a machine learning workspace.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.Workspace
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either Workspace or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Workspace]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Workspace"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                parameters=parameters,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('Workspace', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_create_or_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_delete_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    _delete_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_delete(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Deletes a machine learning workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._delete_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_delete.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+    def _update_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        parameters,  # type: "_models.WorkspaceUpdateParameters"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Optional["_models.Workspace"]
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.Workspace"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        _json = self._serialize.body(parameters, 'WorkspaceUpdateParameters')
+
+        request = build_update_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._update_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        if response.status_code == 200:
+            deserialized = self._deserialize('Workspace', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _update_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_update(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        parameters,  # type: "_models.WorkspaceUpdateParameters"
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.Workspace"]
+        """Updates a machine learning workspace with the specified parameters.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param parameters: The parameters for updating a machine learning workspace.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.WorkspaceUpdateParameters
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either Workspace or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Workspace]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Workspace"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                parameters=parameters,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('Workspace', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_update.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}"}  # type: ignore
+
+    @distributed_trace
+    def list_by_resource_group(
+        self,
+        resource_group_name,  # type: str
+        skip=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.WorkspaceListResult"]
+        """Lists all the available machine learning workspaces under the specified resource group.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either WorkspaceListResult or the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.WorkspaceListResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.WorkspaceListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_by_resource_group_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=self.list_by_resource_group.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_by_resource_group_request(
+                    subscription_id=self._config.subscription_id,
+                    resource_group_name=resource_group_name,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("WorkspaceListResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list_by_resource_group.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces"}  # type: ignore
+
+    def _diagnose_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        parameters=None,  # type: Optional["_models.DiagnoseWorkspaceParameters"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Optional["_models.DiagnoseResponseResult"]
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.DiagnoseResponseResult"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if parameters is not None:
+            _json = self._serialize.body(parameters, 'DiagnoseWorkspaceParameters')
+        else:
+            _json = None
+
+        request = build_diagnose_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            template_url=self._diagnose_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize('DiagnoseResponseResult', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            response_headers['Retry-After']=self._deserialize('int', response.headers.get('Retry-After'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _diagnose_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/diagnose"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_diagnose(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        parameters=None,  # type: Optional["_models.DiagnoseWorkspaceParameters"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.DiagnoseResponseResult"]
+        """Diagnose workspace setup issue.
+
+        Diagnose workspace setup issue.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :param parameters: The parameter of diagnosing workspace health.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.DiagnoseWorkspaceParameters
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either DiagnoseResponseResult or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.DiagnoseResponseResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DiagnoseResponseResult"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._diagnose_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                parameters=parameters,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('DiagnoseResponseResult', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_diagnose.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/diagnose"}  # type: ignore
+
+    @distributed_trace
+    def list_keys(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ListWorkspaceKeysResult"
+        """Lists all the keys associated with this workspace. This includes keys for the storage account,
+        app insights and password for container registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ListWorkspaceKeysResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ListWorkspaceKeysResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListWorkspaceKeysResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ListWorkspaceKeysResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listKeys"}  # type: ignore
+
+
+    def _resync_keys_initial(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> None
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_resync_keys_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self._resync_keys_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    _resync_keys_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/resyncKeys"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_resync_keys(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller[None]
+        """Resync all the keys associated with this workspace. This includes keys for the storage account,
+        app insights and password for container registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType[None]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._resync_keys_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            if cls:
+                return cls(pipeline_response, None, {})
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_resync_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/resyncKeys"}  # type: ignore
+
+    @distributed_trace
+    def list_by_subscription(
+        self,
+        skip=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_models.WorkspaceListResult"]
+        """Lists all the available machine learning workspaces under the specified subscription.
+
+        :param skip: Continuation token for pagination.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either WorkspaceListResult or the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.WorkspaceListResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.WorkspaceListResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+        def prepare_request(next_link=None):
+            if not next_link:
+                
+                request = build_list_by_subscription_request(
+                    subscription_id=self._config.subscription_id,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=self.list_by_subscription.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_by_subscription_request(
+                    subscription_id=self._config.subscription_id,
+                    api_version=api_version,
+                    skip=skip,
+                    template_url=next_link,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("WorkspaceListResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+                request,
+                stream=False,
+                **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+
+        return ItemPaged(
+            get_next, extract_data
+        )
+    list_by_subscription.metadata = {'url': "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/workspaces"}  # type: ignore
+
+    @distributed_trace
+    def list_notebook_access_token(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.NotebookAccessTokenResult"
+        """return notebook access token and refresh token.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: NotebookAccessTokenResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.NotebookAccessTokenResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.NotebookAccessTokenResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_notebook_access_token_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list_notebook_access_token.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('NotebookAccessTokenResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_notebook_access_token.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listNotebookAccessToken"}  # type: ignore
+
+
+    def _prepare_notebook_initial(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Optional["_models.NotebookResourceInfo"]
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.NotebookResourceInfo"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_prepare_notebook_request_initial(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self._prepare_notebook_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            raise HttpResponseError(response=response, error_format=ARMErrorFormat)
+
+        deserialized = None
+        if response.status_code == 200:
+            deserialized = self._deserialize('NotebookResourceInfo', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _prepare_notebook_initial.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/prepareNotebook"}  # type: ignore
+
+
+    @distributed_trace
+    def begin_prepare_notebook(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_models.NotebookResourceInfo"]
+        """Prepare a notebook.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either NotebookResourceInfo or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.NotebookResourceInfo]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+        polling = kwargs.pop('polling', True)  # type: Union[bool, PollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.NotebookResourceInfo"]
+        lro_delay = kwargs.pop(
+            'polling_interval',
+            self._config.polling_interval
+        )
+        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
+        if cont_token is None:
+            raw_result = self._prepare_notebook_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                api_version=api_version,
+                cls=lambda x,y,z: x,
+                **kwargs
+            )
+        kwargs.pop('error_map', None)
+
+        def get_long_running_output(pipeline_response):
+            response = pipeline_response.http_response
+            deserialized = self._deserialize('NotebookResourceInfo', pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+
+        if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_prepare_notebook.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/prepareNotebook"}  # type: ignore
+
+    @distributed_trace
+    def list_storage_account_keys(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ListStorageAccountKeysResult"
+        """List storage account keys of a workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ListStorageAccountKeysResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ListStorageAccountKeysResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListStorageAccountKeysResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_storage_account_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list_storage_account_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ListStorageAccountKeysResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_storage_account_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listStorageAccountKeys"}  # type: ignore
+
+
+    @distributed_trace
+    def list_notebook_keys(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ListNotebookKeysResult"
+        """List keys of a notebook.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ListNotebookKeysResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ListNotebookKeysResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ListNotebookKeysResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_notebook_keys_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list_notebook_keys.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ListNotebookKeysResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_notebook_keys.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listNotebookKeys"}  # type: ignore
+
+
+    @distributed_trace
+    def list_outbound_network_dependencies_endpoints(
+        self,
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_models.ExternalFQDNResponse"
+        """Called by Client (Portal, CLI, etc) to get a list of all external outbound dependencies (FQDNs)
+        programmatically.
+
+        Called by Client (Portal, CLI, etc) to get a list of all external outbound dependencies (FQDNs)
+        programmatically.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace.
+        :type workspace_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ExternalFQDNResponse, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ExternalFQDNResponse
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ExternalFQDNResponse"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        api_version = kwargs.pop('api_version', "2022-10-01")  # type: str
+
+        
+        request = build_list_outbound_network_dependencies_endpoints_request(
+            subscription_id=self._config.subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            api_version=api_version,
+            template_url=self.list_outbound_network_dependencies_endpoints.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = self._client._pipeline.run(  # pylint: disable=protected-access
+            request,
+            stream=False,
+            **kwargs
+        )
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize('ExternalFQDNResponse', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    list_outbound_network_dependencies_endpoints.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/outboundNetworkDependenciesEndpoints"}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/py.typed b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/py.typed
new file mode 100644
index 00000000..e5aff4f8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/v2022_10_01/py.typed
@@ -0,0 +1 @@
+# Marker file for PEP 561.
\ No newline at end of file