two version of R2R are hereHEADmaster

13 files changed, 4168 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/__init__.py
new file mode 100644
index 00000000..f67ccda9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/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/dataset_dataplane/aio/_azure_machine_learning_workspaces.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_azure_machine_learning_workspaces.py
new file mode 100644
index 00000000..597cca3d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_azure_machine_learning_workspaces.py
@@ -0,0 +1,111 @@
+# 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, Optional, TYPE_CHECKING
+
+from azure.core.rest import AsyncHttpResponse, HttpRequest
+from azure.mgmt.core import AsyncARMPipelineClient
+from msrest import Deserializer, Serializer
+
+from .. import models
+from ._configuration import AzureMachineLearningWorkspacesConfiguration
+from .operations import DataCallOperations, DataContainerOperations, DataVersionOperations, DatasetControllerV2Operations, DatasetV2Operations, DatasetsV1Operations, DeleteOperations, GetOperationStatusOperations
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from azure.core.credentials_async import AsyncTokenCredential
+
+class AzureMachineLearningWorkspaces:
+    """AzureMachineLearningWorkspaces.
+
+    :ivar data_call: DataCallOperations operations
+    :vartype data_call: azure.mgmt.machinelearningservices.aio.operations.DataCallOperations
+    :ivar data_container: DataContainerOperations operations
+    :vartype data_container:
+     azure.mgmt.machinelearningservices.aio.operations.DataContainerOperations
+    :ivar delete: DeleteOperations operations
+    :vartype delete: azure.mgmt.machinelearningservices.aio.operations.DeleteOperations
+    :ivar datasets_v1: DatasetsV1Operations operations
+    :vartype datasets_v1: azure.mgmt.machinelearningservices.aio.operations.DatasetsV1Operations
+    :ivar dataset_controller_v2: DatasetControllerV2Operations operations
+    :vartype dataset_controller_v2:
+     azure.mgmt.machinelearningservices.aio.operations.DatasetControllerV2Operations
+    :ivar dataset_v2: DatasetV2Operations operations
+    :vartype dataset_v2: azure.mgmt.machinelearningservices.aio.operations.DatasetV2Operations
+    :ivar data_version: DataVersionOperations operations
+    :vartype data_version: azure.mgmt.machinelearningservices.aio.operations.DataVersionOperations
+    :ivar get_operation_status: GetOperationStatusOperations operations
+    :vartype get_operation_status:
+     azure.mgmt.machinelearningservices.aio.operations.GetOperationStatusOperations
+    :param credential: Credential needed for the client to connect to Azure.
+    :type credential: ~azure.core.credentials_async.AsyncTokenCredential
+    :param base_url: Service URL. Default value is ''.
+    :type base_url: 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",
+        base_url: str = "",
+        **kwargs: Any
+    ) -> None:
+        self._config = AzureMachineLearningWorkspacesConfiguration(credential=credential, **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.data_call = DataCallOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.data_container = DataContainerOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.delete = DeleteOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.datasets_v1 = DatasetsV1Operations(self._client, self._config, self._serialize, self._deserialize)
+        self.dataset_controller_v2 = DatasetControllerV2Operations(self._client, self._config, self._serialize, self._deserialize)
+        self.dataset_v2 = DatasetV2Operations(self._client, self._config, self._serialize, self._deserialize)
+        self.data_version = DataVersionOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.get_operation_status = GetOperationStatusOperations(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/dataset_dataplane/aio/_configuration.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_configuration.py
new file mode 100644
index 00000000..26def54e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_configuration.py
@@ -0,0 +1,60 @@
+# 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):
+    """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
+    """
+
+    def __init__(
+        self,
+        credential: "AsyncTokenCredential",
+        **kwargs: Any
+    ) -> None:
+        super(AzureMachineLearningWorkspacesConfiguration, self).__init__(**kwargs)
+        if credential is None:
+            raise ValueError("Parameter 'credential' must not be None.")
+
+        self.credential = credential
+        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/dataset_dataplane/aio/_patch.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/_patch.py
new file mode 100644
index 00000000..74e48ecd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/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/dataset_dataplane/aio/operations/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/__init__.py
new file mode 100644
index 00000000..f0340813
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/__init__.py
@@ -0,0 +1,27 @@
+# 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 ._data_call_operations import DataCallOperations
+from ._data_container_operations import DataContainerOperations
+from ._delete_operations import DeleteOperations
+from ._datasets_v1_operations import DatasetsV1Operations
+from ._dataset_controller_v2_operations import DatasetControllerV2Operations
+from ._dataset_v2_operations import DatasetV2Operations
+from ._data_version_operations import DataVersionOperations
+from ._get_operation_status_operations import GetOperationStatusOperations
+
+__all__ = [
+    'DataCallOperations',
+    'DataContainerOperations',
+    'DeleteOperations',
+    'DatasetsV1Operations',
+    'DatasetControllerV2Operations',
+    'DatasetV2Operations',
+    'DataVersionOperations',
+    'GetOperationStatusOperations',
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_call_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_call_operations.py
new file mode 100644
index 00000000..cf00280c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_call_operations.py
@@ -0,0 +1,243 @@
+# 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 functools
+from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar
+import warnings
+
+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._data_call_operations import build_get_preview_for_ml_table_request, build_get_quick_profile_for_ml_table_request, build_get_schema_for_ml_table_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class DataCallOperations:
+    """DataCallOperations 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_schema_for_ml_table(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        body: Optional["_models.DataCallRequest"] = None,
+        **kwargs: Any
+    ) -> List["_models.ColumnDefinition"]:
+        """Get schema for a specific MLTable.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DataCallRequest
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: list of ColumnDefinition, or the result of cls(response)
+        :rtype: list[~azure.mgmt.machinelearningservices.models.ColumnDefinition]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[List["_models.ColumnDefinition"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DataCallRequest')
+        else:
+            _json = None
+
+        request = build_get_schema_for_ml_table_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            template_url=self.get_schema_for_ml_table.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('[ColumnDefinition]', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_schema_for_ml_table.metadata = {'url': '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/schema'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get_preview_for_ml_table(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        body: Optional["_models.DataCallRequest"] = None,
+        **kwargs: Any
+    ) -> "_models.DataViewSetResult":
+        """Get preview for a specific MLTable.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DataCallRequest
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataViewSetResult, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataViewSetResult
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataViewSetResult"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DataCallRequest')
+        else:
+            _json = None
+
+        request = build_get_preview_for_ml_table_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            template_url=self.get_preview_for_ml_table.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DataViewSetResult', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_preview_for_ml_table.metadata = {'url': '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/preview'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get_quick_profile_for_ml_table(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        body: Optional["_models.DataCallRequest"] = None,
+        **kwargs: Any
+    ) -> List["_models.ProfileResult"]:
+        """Get quick profile for a specific MLTable.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DataCallRequest
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: list of ProfileResult, or the result of cls(response)
+        :rtype: list[~azure.mgmt.machinelearningservices.models.ProfileResult]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[List["_models.ProfileResult"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DataCallRequest')
+        else:
+            _json = None
+
+        request = build_get_quick_profile_for_ml_table_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            template_url=self.get_quick_profile_for_ml_table.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('[ProfileResult]', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_quick_profile_for_ml_table.metadata = {'url': '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/quickprofile'}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_container_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_container_operations.py
new file mode 100644
index 00000000..c4c78abb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_container_operations.py
@@ -0,0 +1,321 @@
+# 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 functools
+from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar
+import warnings
+
+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_container_operations import build_create_data_container_request, build_get_data_container_request, build_list_data_container_request, build_modify_data_container_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class DataContainerOperations:
+    """DataContainerOperations 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_data_container(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        body: Optional["_models.DataContainer"] = None,
+        **kwargs: Any
+    ) -> "_models.DataContainerEntity":
+        """create_data_container.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DataContainer
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataContainerEntity, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainerEntity"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DataContainer')
+        else:
+            _json = None
+
+        request = build_create_data_container_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_data_container.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DataContainerEntity', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer'}  # type: ignore
+
+
+    @distributed_trace
+    def list_data_container(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PaginatedDataContainerEntityList"]:
+        """list_data_container.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the 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 PaginatedDataContainerEntityList or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDataContainerEntityList]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedDataContainerEntityList"]
+        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_data_container_request(
+                    subscription_id=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    template_url=self.list_data_container.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_list_data_container_request(
+                    subscription_id=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    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("PaginatedDataContainerEntityList", 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(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_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer'}  # type: ignore
+
+    @distributed_trace_async
+    async def get_data_container(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> "_models.DataContainerEntity":
+        """get_data_container.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataContainerEntity, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainerEntity"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_get_data_container_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            template_url=self.get_data_container.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DataContainerEntity', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def modify_data_container(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        body: Optional["_models.DataContainerMutable"] = None,
+        **kwargs: Any
+    ) -> "_models.DataContainerEntity":
+        """modify_data_container.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DataContainerMutable
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataContainerEntity, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainerEntity"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DataContainerMutable')
+        else:
+            _json = None
+
+        request = build_modify_data_container_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            content_type=content_type,
+            json=_json,
+            template_url=self.modify_data_container.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DataContainerEntity', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    modify_data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}'}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_version_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_version_operations.py
new file mode 100644
index 00000000..9f375aeb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_data_version_operations.py
@@ -0,0 +1,804 @@
+# 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 functools
+from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar
+import warnings
+
+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_version_operations import build_batch_get_resolved_uris_request, build_create_request, build_create_unregistered_input_data_request, build_create_unregistered_output_data_request, build_delete_request, build_exists_request, build_get_by_asset_id_request, build_get_request, build_list_request, build_modify_request, build_registered_existing_data_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class DataVersionOperations:
+    """DataVersionOperations 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,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        body: Optional["_models.DataVersion"] = None,
+        **kwargs: Any
+    ) -> "_models.DataVersionEntity":
+        """create.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DataVersion
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataVersionEntity, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataVersionEntity"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DataVersion')
+        else:
+            _json = None
+
+        request = build_create_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            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(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('DataVersionEntity', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions'}  # type: ignore
+
+
+    @distributed_trace
+    def list(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PaginatedDataVersionEntityList"]:
+        """list.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :param order_by:
+        :type order_by: str
+        :param top:
+        :type top: int
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PaginatedDataVersionEntityList or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDataVersionEntityList]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedDataVersionEntityList"]
+        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=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    order_by=order_by,
+                    top=top,
+                    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=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    name=name,
+                    order_by=order_by,
+                    top=top,
+                    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("PaginatedDataVersionEntityList", 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(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': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions'}  # type: ignore
+
+    @distributed_trace_async
+    async def get(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> "_models.DataVersionEntity":
+        """get.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :param version:
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataVersionEntity, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataVersionEntity"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_get_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=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(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('DataVersionEntity', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def modify(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        body: Optional["_models.DataVersionMutable"] = None,
+        **kwargs: Any
+    ) -> "_models.DataVersionEntity":
+        """modify.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :param version:
+        :type version: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DataVersionMutable
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataVersionEntity, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataVersionEntity"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DataVersionMutable')
+        else:
+            _json = None
+
+        request = build_modify_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            content_type=content_type,
+            json=_json,
+            template_url=self.modify.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DataVersionEntity', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    modify.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def delete(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> "_models.HttpResponseMessage":
+        """delete.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :param version:
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: HttpResponseMessage, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.HttpResponseMessage
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.HttpResponseMessage"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_delete_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=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(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('HttpResponseMessage', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    delete.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def exists(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version: str,
+        **kwargs: Any
+    ) -> bool:
+        """exists.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :param version:
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: bool, or the result of cls(response)
+        :rtype: bool
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType[bool]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_exists_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version=version,
+            template_url=self.exists.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('bool', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    exists.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}/exists'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get_by_asset_id(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        body: Optional["_models.AssetId"] = None,
+        **kwargs: Any
+    ) -> "_models.DataVersionEntity":
+        """get_by_asset_id.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.AssetId
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataVersionEntity, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataVersionEntity
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataVersionEntity"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'AssetId')
+        else:
+            _json = None
+
+        request = build_get_by_asset_id_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            template_url=self.get_by_asset_id.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DataVersionEntity', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_by_asset_id.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/getByAssetId'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_unregistered_input_data(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        body: Optional["_models.CreateUnregisteredInputData"] = None,
+        **kwargs: Any
+    ) -> "_models.DataContainerEntity":
+        """create_unregistered_input_data.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.CreateUnregisteredInputData
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataContainerEntity, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainerEntity"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'CreateUnregisteredInputData')
+        else:
+            _json = None
+
+        request = build_create_unregistered_input_data_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_unregistered_input_data.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DataContainerEntity', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_unregistered_input_data.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/createUnregisteredInput'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def create_unregistered_output_data(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        body: Optional["_models.CreateUnregisteredOutputData"] = None,
+        **kwargs: Any
+    ) -> "_models.DataContainerEntity":
+        """create_unregistered_output_data.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.CreateUnregisteredOutputData
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataContainerEntity, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainerEntity"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'CreateUnregisteredOutputData')
+        else:
+            _json = None
+
+        request = build_create_unregistered_output_data_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            template_url=self.create_unregistered_output_data.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DataContainerEntity', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create_unregistered_output_data.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/createUnregisteredOutput'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def registered_existing_data(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        body: Optional["_models.RegisterExistingData"] = None,
+        **kwargs: Any
+    ) -> "_models.DataContainerEntity":
+        """registered_existing_data.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.RegisterExistingData
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DataContainerEntity, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DataContainerEntity
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DataContainerEntity"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'RegisterExistingData')
+        else:
+            _json = None
+
+        request = build_registered_existing_data_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            template_url=self.registered_existing_data.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DataContainerEntity', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    registered_existing_data.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/registerExisting'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def batch_get_resolved_uris(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        body: Optional["_models.BatchGetResolvedURIs"] = None,
+        **kwargs: Any
+    ) -> "_models.BatchDataUriResponse":
+        """batch_get_resolved_uris.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.BatchGetResolvedURIs
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: BatchDataUriResponse, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.BatchDataUriResponse
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchDataUriResponse"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'BatchGetResolvedURIs')
+        else:
+            _json = None
+
+        request = build_batch_get_resolved_uris_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            template_url=self.batch_get_resolved_uris.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('BatchDataUriResponse', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    batch_get_resolved_uris.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/batchGetResolvedUris'}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_controller_v2_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_controller_v2_operations.py
new file mode 100644
index 00000000..a3574be3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_controller_v2_operations.py
@@ -0,0 +1,845 @@
+# 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 functools
+from typing import Any, AsyncIterable, Callable, Dict, Generic, List, Optional, TypeVar
+import warnings
+
+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._dataset_controller_v2_operations import build_delete_all_datasets_request, build_get_all_dataset_definitions_request, build_get_all_dataset_versions_request, build_get_dataset_by_name_request, build_get_dataset_definition_request, build_list_request, build_register_request, build_unregister_dataset_request, build_update_dataset_request, build_update_definition_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class DatasetControllerV2Operations:
+    """DatasetControllerV2Operations 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_dataset_definition(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        version: str,
+        **kwargs: Any
+    ) -> "_models.DatasetDefinition":
+        """Get a specific dataset definition.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param version:
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DatasetDefinition, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DatasetDefinition
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatasetDefinition"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_get_dataset_definition_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_id=dataset_id,
+            version=version,
+            template_url=self.get_dataset_definition.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DatasetDefinition', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_dataset_definition.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions/{version}'}  # type: ignore
+
+
+    @distributed_trace
+    def get_all_dataset_definitions(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        continuation_token_parameter: Optional[str] = None,
+        page_size: Optional[int] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PaginatedDatasetDefinitionList"]:
+        """Get all dataset definitions for a given dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param continuation_token_parameter:
+        :type continuation_token_parameter: str
+        :param page_size:
+        :type page_size: int
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PaginatedDatasetDefinitionList or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetDefinitionList]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedDatasetDefinitionList"]
+        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_get_all_dataset_definitions_request(
+                    subscription_id=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_id=dataset_id,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    template_url=self.get_all_dataset_definitions.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_get_all_dataset_definitions_request(
+                    subscription_id=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_id=dataset_id,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    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("PaginatedDatasetDefinitionList", 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(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
+        )
+    get_all_dataset_definitions.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'}  # type: ignore
+
+    @distributed_trace_async
+    async def update_definition(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        register_as_pending: Optional[bool] = False,
+        force_update: Optional[bool] = False,
+        dataset_type: Optional[str] = None,
+        user_version_id: Optional[str] = None,
+        body: Optional["_models.DatasetDefinition"] = None,
+        **kwargs: Any
+    ) -> "_models.Dataset":
+        """Update a dataset definition.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param register_as_pending:
+        :type register_as_pending: bool
+        :param force_update:
+        :type force_update: bool
+        :param dataset_type:
+        :type dataset_type: str
+        :param user_version_id:
+        :type user_version_id: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DatasetDefinition
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Dataset, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Dataset
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Dataset"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DatasetDefinition')
+        else:
+            _json = None
+
+        request = build_update_definition_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_id=dataset_id,
+            content_type=content_type,
+            json=_json,
+            register_as_pending=register_as_pending,
+            force_update=force_update,
+            dataset_type=dataset_type,
+            user_version_id=user_version_id,
+            template_url=self.update_definition.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('Dataset', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    update_definition.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'}  # type: ignore
+
+
+    @distributed_trace
+    def get_all_dataset_versions(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        continuation_token_parameter: Optional[str] = None,
+        page_size: Optional[int] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PaginatedStringList"]:
+        """Get all dataset versions for a given dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param continuation_token_parameter:
+        :type continuation_token_parameter: str
+        :param page_size:
+        :type page_size: int
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PaginatedStringList or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedStringList]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedStringList"]
+        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_get_all_dataset_versions_request(
+                    subscription_id=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_id=dataset_id,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    template_url=self.get_all_dataset_versions.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_get_all_dataset_versions_request(
+                    subscription_id=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_id=dataset_id,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    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("PaginatedStringList", 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(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
+        )
+    get_all_dataset_versions.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/versions'}  # type: ignore
+
+    @distributed_trace_async
+    async def get_dataset_by_name(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_name: str,
+        version_id: Optional[str] = None,
+        include_latest_definition: Optional[bool] = True,
+        **kwargs: Any
+    ) -> "_models.Dataset":
+        """Get a dataset for a given dataset name.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_name:
+        :type dataset_name: str
+        :param version_id:
+        :type version_id: str
+        :param include_latest_definition:
+        :type include_latest_definition: bool
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Dataset, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Dataset
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Dataset"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_get_dataset_by_name_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_name=dataset_name,
+            version_id=version_id,
+            include_latest_definition=include_latest_definition,
+            template_url=self.get_dataset_by_name.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('Dataset', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_dataset_by_name.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}'}  # type: ignore
+
+
+    @distributed_trace
+    def list(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_names: Optional[List[str]] = None,
+        search_text: Optional[str] = None,
+        include_invisible: Optional[bool] = False,
+        status: Optional[str] = None,
+        continuation_token_parameter: Optional[str] = None,
+        page_size: Optional[int] = None,
+        include_latest_definition: Optional[bool] = False,
+        order_by: Optional[str] = None,
+        order_by_asc: Optional[bool] = False,
+        dataset_types: Optional[List[str]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PaginatedDatasetList"]:
+        """Get a list of datasets.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_names:
+        :type dataset_names: list[str]
+        :param search_text:
+        :type search_text: str
+        :param include_invisible:
+        :type include_invisible: bool
+        :param status:
+        :type status: str
+        :param continuation_token_parameter:
+        :type continuation_token_parameter: str
+        :param page_size:
+        :type page_size: int
+        :param include_latest_definition:
+        :type include_latest_definition: bool
+        :param order_by:
+        :type order_by: str
+        :param order_by_asc:
+        :type order_by_asc: bool
+        :param dataset_types:
+        :type dataset_types: list[str]
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PaginatedDatasetList or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetList]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedDatasetList"]
+        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=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_names=dataset_names,
+                    search_text=search_text,
+                    include_invisible=include_invisible,
+                    status=status,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    include_latest_definition=include_latest_definition,
+                    order_by=order_by,
+                    order_by_asc=order_by_asc,
+                    dataset_types=dataset_types,
+                    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=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_names=dataset_names,
+                    search_text=search_text,
+                    include_invisible=include_invisible,
+                    status=status,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    include_latest_definition=include_latest_definition,
+                    order_by=order_by,
+                    order_by_asc=order_by_asc,
+                    dataset_types=dataset_types,
+                    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("PaginatedDatasetList", 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(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': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+    @distributed_trace_async
+    async def register(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        register_as_pending: Optional[bool] = False,
+        if_exists_ok: Optional[bool] = True,
+        update_definition_if_exists: Optional[bool] = False,
+        with_data_hash: Optional[bool] = False,
+        user_version_id: Optional[str] = None,
+        body: Optional["_models.Dataset"] = None,
+        **kwargs: Any
+    ) -> "_models.Dataset":
+        """Register new dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param register_as_pending:
+        :type register_as_pending: bool
+        :param if_exists_ok:
+        :type if_exists_ok: bool
+        :param update_definition_if_exists:
+        :type update_definition_if_exists: bool
+        :param with_data_hash:
+        :type with_data_hash: bool
+        :param user_version_id:
+        :type user_version_id: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.Dataset
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Dataset, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Dataset
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Dataset"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'Dataset')
+        else:
+            _json = None
+
+        request = build_register_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            register_as_pending=register_as_pending,
+            if_exists_ok=if_exists_ok,
+            update_definition_if_exists=update_definition_if_exists,
+            with_data_hash=with_data_hash,
+            user_version_id=user_version_id,
+            template_url=self.register.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('Dataset', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    register.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def delete_all_datasets(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> None:
+        """Unregister all datasets in the workspace.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_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', {}))
+
+        
+        request = build_delete_all_datasets_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            template_url=self.delete_all_datasets.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
+        response = pipeline_response.http_response
+
+        if response.status_code not in []:
+            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_all_datasets.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def update_dataset(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        force_update: Optional[bool] = False,
+        body: Optional["_models.Dataset"] = None,
+        **kwargs: Any
+    ) -> "_models.Dataset":
+        """Update a dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param force_update:
+        :type force_update: bool
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.Dataset
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Dataset, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Dataset
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Dataset"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'Dataset')
+        else:
+            _json = None
+
+        request = build_update_dataset_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_id=dataset_id,
+            content_type=content_type,
+            json=_json,
+            force_update=force_update,
+            template_url=self.update_dataset.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('Dataset', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    update_dataset.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def unregister_dataset(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> None:
+        """Unregister a dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param 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', {}))
+
+        
+        request = build_unregister_dataset_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            template_url=self.unregister_dataset.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
+        response = pipeline_response.http_response
+
+        if response.status_code not in []:
+            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, {})
+
+    unregister_dataset.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}'}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_v2_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_v2_operations.py
new file mode 100644
index 00000000..584122c1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_dataset_v2_operations.py
@@ -0,0 +1,592 @@
+# 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 functools
+from typing import Any, AsyncIterable, Callable, Dict, Generic, List, Optional, TypeVar
+import warnings
+
+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._dataset_v2_operations import build_create_request, build_delete_all_datasets_request, build_delete_dataset_by_name_request, build_get_dataset_by_id_request, build_get_dataset_by_name_request, build_list_request, build_update_dataset_by_name_and_version_request, build_update_dataset_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class DatasetV2Operations:
+    """DatasetV2Operations 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,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        if_exists_update: Optional[bool] = False,
+        body: Optional["_models.DatasetV2"] = None,
+        **kwargs: Any
+    ) -> "_models.DatasetV2":
+        """Create new dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param if_exists_update:
+        :type if_exists_update: bool
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DatasetV2
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DatasetV2, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatasetV2"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DatasetV2')
+        else:
+            _json = None
+
+        request = build_create_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            if_exists_update=if_exists_update,
+            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(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('DatasetV2', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    create.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def delete_all_datasets(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete all datasets in the workspace.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_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', {}))
+
+        
+        request = build_delete_all_datasets_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            template_url=self.delete_all_datasets.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
+        response = pipeline_response.http_response
+
+        if response.status_code not in []:
+            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_all_datasets.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+
+    @distributed_trace
+    def list(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        names: Optional[List[str]] = None,
+        search_text: Optional[str] = None,
+        continuation_token_parameter: Optional[str] = None,
+        page_size: Optional[int] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PaginatedDatasetV2List"]:
+        """Get a list of datasets.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param names:
+        :type names: list[str]
+        :param search_text:
+        :type search_text: str
+        :param continuation_token_parameter:
+        :type continuation_token_parameter: str
+        :param page_size:
+        :type page_size: int
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PaginatedDatasetV2List or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetV2List]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedDatasetV2List"]
+        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=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    names=names,
+                    search_text=search_text,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    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=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    names=names,
+                    search_text=search_text,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    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("PaginatedDatasetV2List", 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(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': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+    @distributed_trace_async
+    async def delete_dataset_by_name(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version_id: str,
+        **kwargs: Any
+    ) -> None:
+        """Delete a dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :param version_id:
+        :type version_id: 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', {}))
+
+        
+        request = build_delete_dataset_by_name_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version_id=version_id,
+            template_url=self.delete_dataset_by_name.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
+        response = pipeline_response.http_response
+
+        if response.status_code not in []:
+            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_dataset_by_name.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}/versions/{versionId}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def update_dataset_by_name_and_version(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        version_id: str,
+        body: Optional["_models.DatasetV2"] = None,
+        **kwargs: Any
+    ) -> "_models.DatasetV2":
+        """Update a dataset by its name and version.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :param version_id:
+        :type version_id: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DatasetV2
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DatasetV2, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatasetV2"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DatasetV2')
+        else:
+            _json = None
+
+        request = build_update_dataset_by_name_and_version_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            version_id=version_id,
+            content_type=content_type,
+            json=_json,
+            template_url=self.update_dataset_by_name_and_version.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DatasetV2', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    update_dataset_by_name_and_version.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}/versions/{versionId}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get_dataset_by_id(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        **kwargs: Any
+    ) -> "_models.DatasetV2":
+        """Get a dataset for a given dataset id.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DatasetV2, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatasetV2"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_get_dataset_by_id_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_id=dataset_id,
+            template_url=self.get_dataset_by_id.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DatasetV2', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_dataset_by_id.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def update_dataset(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        body: Optional["_models.DatasetV2"] = None,
+        **kwargs: Any
+    ) -> "_models.DatasetV2":
+        """Update a dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DatasetV2
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DatasetV2, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatasetV2"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DatasetV2')
+        else:
+            _json = None
+
+        request = build_update_dataset_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_id=dataset_id,
+            content_type=content_type,
+            json=_json,
+            template_url=self.update_dataset.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DatasetV2', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    update_dataset.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def get_dataset_by_name(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_name: str,
+        version_id: Optional[str] = None,
+        **kwargs: Any
+    ) -> "_models.DatasetV2":
+        """Get a dataset for a given dataset name.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_name:
+        :type dataset_name: str
+        :param version_id:
+        :type version_id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DatasetV2, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DatasetV2
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatasetV2"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_get_dataset_by_name_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_name=dataset_name,
+            version_id=version_id,
+            template_url=self.get_dataset_by_name.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DatasetV2', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_dataset_by_name.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}'}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_datasets_v1_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_datasets_v1_operations.py
new file mode 100644
index 00000000..97e74d53
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_datasets_v1_operations.py
@@ -0,0 +1,845 @@
+# 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 functools
+from typing import Any, AsyncIterable, Callable, Dict, Generic, List, Optional, TypeVar
+import warnings
+
+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._datasets_v1_operations import build_delete_all_datasets_request, build_get_all_dataset_definitions_request, build_get_all_dataset_versions_request, build_get_dataset_by_name_request, build_get_dataset_definition_request, build_list_request, build_register_request, build_unregister_dataset_request, build_update_dataset_request, build_update_definition_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class DatasetsV1Operations:
+    """DatasetsV1Operations 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_dataset_definition(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        version: str,
+        **kwargs: Any
+    ) -> "_models.DatasetDefinition":
+        """Get a specific dataset definition.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param version:
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: DatasetDefinition, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.DatasetDefinition
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.DatasetDefinition"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_get_dataset_definition_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_id=dataset_id,
+            version=version,
+            template_url=self.get_dataset_definition.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('DatasetDefinition', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_dataset_definition.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions/{version}'}  # type: ignore
+
+
+    @distributed_trace
+    def get_all_dataset_definitions(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        continuation_token_parameter: Optional[str] = None,
+        page_size: Optional[int] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PaginatedDatasetDefinitionList"]:
+        """Get all dataset definitions for a given dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param continuation_token_parameter:
+        :type continuation_token_parameter: str
+        :param page_size:
+        :type page_size: int
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PaginatedDatasetDefinitionList or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetDefinitionList]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedDatasetDefinitionList"]
+        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_get_all_dataset_definitions_request(
+                    subscription_id=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_id=dataset_id,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    template_url=self.get_all_dataset_definitions.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_get_all_dataset_definitions_request(
+                    subscription_id=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_id=dataset_id,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    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("PaginatedDatasetDefinitionList", 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(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
+        )
+    get_all_dataset_definitions.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'}  # type: ignore
+
+    @distributed_trace_async
+    async def update_definition(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        register_as_pending: Optional[bool] = False,
+        force_update: Optional[bool] = False,
+        dataset_type: Optional[str] = None,
+        user_version_id: Optional[str] = None,
+        body: Optional["_models.DatasetDefinition"] = None,
+        **kwargs: Any
+    ) -> "_models.Dataset":
+        """Update a dataset definition.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param register_as_pending:
+        :type register_as_pending: bool
+        :param force_update:
+        :type force_update: bool
+        :param dataset_type:
+        :type dataset_type: str
+        :param user_version_id:
+        :type user_version_id: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.DatasetDefinition
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Dataset, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Dataset
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Dataset"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'DatasetDefinition')
+        else:
+            _json = None
+
+        request = build_update_definition_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_id=dataset_id,
+            content_type=content_type,
+            json=_json,
+            register_as_pending=register_as_pending,
+            force_update=force_update,
+            dataset_type=dataset_type,
+            user_version_id=user_version_id,
+            template_url=self.update_definition.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('Dataset', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    update_definition.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions'}  # type: ignore
+
+
+    @distributed_trace
+    def get_all_dataset_versions(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        continuation_token_parameter: Optional[str] = None,
+        page_size: Optional[int] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PaginatedStringList"]:
+        """Get all dataset versions for a given dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param continuation_token_parameter:
+        :type continuation_token_parameter: str
+        :param page_size:
+        :type page_size: int
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PaginatedStringList or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedStringList]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedStringList"]
+        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_get_all_dataset_versions_request(
+                    subscription_id=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_id=dataset_id,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    template_url=self.get_all_dataset_versions.metadata['url'],
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                
+                request = build_get_all_dataset_versions_request(
+                    subscription_id=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_id=dataset_id,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    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("PaginatedStringList", 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(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
+        )
+    get_all_dataset_versions.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/versions'}  # type: ignore
+
+    @distributed_trace_async
+    async def get_dataset_by_name(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_name: str,
+        version_id: Optional[str] = None,
+        include_latest_definition: Optional[bool] = True,
+        **kwargs: Any
+    ) -> "_models.Dataset":
+        """Get a dataset for a given dataset name.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_name:
+        :type dataset_name: str
+        :param version_id:
+        :type version_id: str
+        :param include_latest_definition:
+        :type include_latest_definition: bool
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Dataset, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Dataset
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Dataset"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_get_dataset_by_name_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_name=dataset_name,
+            version_id=version_id,
+            include_latest_definition=include_latest_definition,
+            template_url=self.get_dataset_by_name.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('Dataset', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get_dataset_by_name.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}'}  # type: ignore
+
+
+    @distributed_trace
+    def list(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_names: Optional[List[str]] = None,
+        search_text: Optional[str] = None,
+        include_invisible: Optional[bool] = False,
+        status: Optional[str] = None,
+        continuation_token_parameter: Optional[str] = None,
+        page_size: Optional[int] = None,
+        include_latest_definition: Optional[bool] = False,
+        order_by: Optional[str] = None,
+        order_by_asc: Optional[bool] = False,
+        dataset_types: Optional[List[str]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.PaginatedDatasetList"]:
+        """Get a list of datasets.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_names:
+        :type dataset_names: list[str]
+        :param search_text:
+        :type search_text: str
+        :param include_invisible:
+        :type include_invisible: bool
+        :param status:
+        :type status: str
+        :param continuation_token_parameter:
+        :type continuation_token_parameter: str
+        :param page_size:
+        :type page_size: int
+        :param include_latest_definition:
+        :type include_latest_definition: bool
+        :param order_by:
+        :type order_by: str
+        :param order_by_asc:
+        :type order_by_asc: bool
+        :param dataset_types:
+        :type dataset_types: list[str]
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either PaginatedDatasetList or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedDatasetList]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedDatasetList"]
+        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=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_names=dataset_names,
+                    search_text=search_text,
+                    include_invisible=include_invisible,
+                    status=status,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    include_latest_definition=include_latest_definition,
+                    order_by=order_by,
+                    order_by_asc=order_by_asc,
+                    dataset_types=dataset_types,
+                    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=subscription_id,
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    dataset_names=dataset_names,
+                    search_text=search_text,
+                    include_invisible=include_invisible,
+                    status=status,
+                    continuation_token_parameter=continuation_token_parameter,
+                    page_size=page_size,
+                    include_latest_definition=include_latest_definition,
+                    order_by=order_by,
+                    order_by_asc=order_by_asc,
+                    dataset_types=dataset_types,
+                    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("PaginatedDatasetList", 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(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': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+    @distributed_trace_async
+    async def register(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        register_as_pending: Optional[bool] = False,
+        if_exists_ok: Optional[bool] = True,
+        update_definition_if_exists: Optional[bool] = False,
+        with_data_hash: Optional[bool] = False,
+        user_version_id: Optional[str] = None,
+        body: Optional["_models.Dataset"] = None,
+        **kwargs: Any
+    ) -> "_models.Dataset":
+        """Register new dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param register_as_pending:
+        :type register_as_pending: bool
+        :param if_exists_ok:
+        :type if_exists_ok: bool
+        :param update_definition_if_exists:
+        :type update_definition_if_exists: bool
+        :param with_data_hash:
+        :type with_data_hash: bool
+        :param user_version_id:
+        :type user_version_id: str
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.Dataset
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Dataset, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Dataset
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Dataset"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'Dataset')
+        else:
+            _json = None
+
+        request = build_register_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            content_type=content_type,
+            json=_json,
+            register_as_pending=register_as_pending,
+            if_exists_ok=if_exists_ok,
+            update_definition_if_exists=update_definition_if_exists,
+            with_data_hash=with_data_hash,
+            user_version_id=user_version_id,
+            template_url=self.register.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('Dataset', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    register.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def delete_all_datasets(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        **kwargs: Any
+    ) -> None:
+        """Unregister all datasets in the workspace.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_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', {}))
+
+        
+        request = build_delete_all_datasets_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            template_url=self.delete_all_datasets.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
+        response = pipeline_response.http_response
+
+        if response.status_code not in []:
+            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_all_datasets.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def update_dataset(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        dataset_id: str,
+        force_update: Optional[bool] = False,
+        body: Optional["_models.Dataset"] = None,
+        **kwargs: Any
+    ) -> "_models.Dataset":
+        """Update a dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param dataset_id:
+        :type dataset_id: str
+        :param force_update:
+        :type force_update: bool
+        :param body:
+        :type body: ~azure.mgmt.machinelearningservices.models.Dataset
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Dataset, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Dataset
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.Dataset"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
+
+        if body is not None:
+            _json = self._serialize.body(body, 'Dataset')
+        else:
+            _json = None
+
+        request = build_update_dataset_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            dataset_id=dataset_id,
+            content_type=content_type,
+            json=_json,
+            force_update=force_update,
+            template_url=self.update_dataset.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('Dataset', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    update_dataset.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def unregister_dataset(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> None:
+        """Unregister a dataset.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param 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', {}))
+
+        
+        request = build_unregister_dataset_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            template_url=self.unregister_dataset.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
+        response = pipeline_response.http_response
+
+        if response.status_code not in []:
+            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, {})
+
+    unregister_dataset.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}'}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_delete_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_delete_operations.py
new file mode 100644
index 00000000..9bb293a5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_delete_operations.py
@@ -0,0 +1,104 @@
+# 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 functools
+from typing import Any, Callable, Dict, Generic, Optional, TypeVar
+import warnings
+
+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._delete_operations import build_data_container_request
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class DeleteOperations:
+    """DeleteOperations 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 data_container(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        name: str,
+        **kwargs: Any
+    ) -> "_models.HttpResponseMessage":
+        """data_container.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param name:
+        :type name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: HttpResponseMessage, or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.HttpResponseMessage
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.HttpResponseMessage"]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_data_container_request(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            name=name,
+            template_url=self.data_container.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('HttpResponseMessage', pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    data_container.metadata = {'url': '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}'}  # type: ignore
+
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_get_operation_status_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_get_operation_status_operations.py
new file mode 100644
index 00000000..273b307f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/aio/operations/_get_operation_status_operations.py
@@ -0,0 +1,170 @@
+# 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 functools
+from typing import Any, Callable, Dict, Generic, Optional, TypeVar, Union
+import warnings
+
+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_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._get_operation_status_operations import build_get_dataset_operation_status_request_initial
+T = TypeVar('T')
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+class GetOperationStatusOperations:
+    """GetOperationStatusOperations 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
+
+    async def _get_dataset_operation_status_initial(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        operation_id: str,
+        **kwargs: Any
+    ) -> Optional["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"]:
+        cls = kwargs.pop('cls', None)  # type: ClsType[Optional["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"]]
+        error_map = {
+            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
+        }
+        error_map.update(kwargs.pop('error_map', {}))
+
+        
+        request = build_get_dataset_operation_status_request_initial(
+            subscription_id=subscription_id,
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            operation_id=operation_id,
+            template_url=self._get_dataset_operation_status_initial.metadata['url'],
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response = await self._client._pipeline.run(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('LongRunningOperationResponse1LongRunningOperationResponseObject', pipeline_response)
+
+        if response.status_code == 202:
+            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
+            
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _get_dataset_operation_status_initial.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/operations/{operationId}'}  # type: ignore
+
+
+    @distributed_trace_async
+    async def begin_get_dataset_operation_status(
+        self,
+        subscription_id: str,
+        resource_group_name: str,
+        workspace_name: str,
+        operation_id: str,
+        **kwargs: Any
+    ) -> AsyncLROPoller["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"]:
+        """get_dataset_operation_status.
+
+        :param subscription_id: The Azure Subscription ID.
+        :type subscription_id: str
+        :param resource_group_name: The Name of the resource group in which the workspace is located.
+        :type resource_group_name: str
+        :param workspace_name: The name of the workspace.
+        :type workspace_name: str
+        :param operation_id:
+        :type operation_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
+         LongRunningOperationResponse1LongRunningOperationResponseObject or the result of cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.LongRunningOperationResponse1LongRunningOperationResponseObject]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        polling = kwargs.pop('polling', True)  # type: Union[bool, azure.core.polling.AsyncPollingMethod]
+        cls = kwargs.pop('cls', None)  # type: ClsType["_models.LongRunningOperationResponse1LongRunningOperationResponseObject"]
+        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._get_dataset_operation_status_initial(
+                subscription_id=subscription_id,
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                operation_id=operation_id,
+                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('LongRunningOperationResponse1LongRunningOperationResponseObject', 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
+            )
+        else:
+            return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
+
+    begin_get_dataset_operation_status.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/operations/{operationId}'}  # type: ignore