two version of R2R are here HEAD master

9 files changed, 5920 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/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/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/operations/_data_call_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_call_operations.py
new file mode 100644
index 00000000..4e7865d1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_call_operations.py
@@ -0,0 +1,356 @@
+# 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 TYPE_CHECKING
+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 HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from msrest import Serializer
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_get_schema_for_ml_table_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/schema')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_preview_for_ml_table_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/preview')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_quick_profile_for_ml_table_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacall/quickprofile')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class DataCallOperations(object):
+    """DataCallOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def get_schema_for_ml_table(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        body=None,  # type: Optional["_models.DataCallRequest"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> 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 = 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
+    def get_preview_for_ml_table(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        body=None,  # type: Optional["_models.DataCallRequest"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def get_quick_profile_for_ml_table(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        body=None,  # type: Optional["_models.DataCallRequest"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> 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 = 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/operations/_data_container_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_container_operations.py
new file mode 100644
index 00000000..ac3af23d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_container_operations.py
@@ -0,0 +1,464 @@
+# 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 TYPE_CHECKING
+import warnings
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from msrest import Serializer
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_create_data_container_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_list_data_container_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_data_container_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_modify_data_container_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PATCH",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class DataContainerOperations(object):
+    """DataContainerOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def create_data_container(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        body=None,  # type: Optional["_models.DataContainer"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_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.paging.ItemPaged[~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
+
+        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, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = 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 ItemPaged(
+            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
+    def get_data_container(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def modify_data_container(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        body=None,  # type: Optional["_models.DataContainerMutable"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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/operations/_data_version_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_version_operations.py
new file mode 100644
index 00000000..ae87ad4b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_data_version_operations.py
@@ -0,0 +1,1211 @@
+# 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 TYPE_CHECKING
+import warnings
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from msrest import Serializer
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_create_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    top = kwargs.pop('top', None)  # type: Optional[int]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if order_by is not None:
+        query_parameters['orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if top is not None:
+        query_parameters['top'] = _SERIALIZER.query("top", top, 'int')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_modify_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PATCH",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_exists_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/{name}/versions/{version}/exists')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_by_asset_id_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/getByAssetId')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_create_unregistered_input_data_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/createUnregisteredInput')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_create_unregistered_output_data_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/createUnregisteredOutput')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_registered_existing_data_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/registerExisting')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_batch_get_resolved_uris_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/dataversion/batchGetResolvedUris')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class DataVersionOperations(object):
+    """DataVersionOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def create(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        body=None,  # type: Optional["_models.DataVersion"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        order_by=None,  # type: Optional[str]
+        top=None,  # type: Optional[int]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_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.paging.ItemPaged[~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
+
+        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, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = 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 ItemPaged(
+            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
+    def get(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def modify(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        body=None,  # type: Optional["_models.DataVersionMutable"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def delete(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def exists(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> 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 = 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
+    def get_by_asset_id(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        body=None,  # type: Optional["_models.AssetId"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def create_unregistered_input_data(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        body=None,  # type: Optional["_models.CreateUnregisteredInputData"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def create_unregistered_output_data(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        body=None,  # type: Optional["_models.CreateUnregisteredOutputData"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def registered_existing_data(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        body=None,  # type: Optional["_models.RegisterExistingData"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def batch_get_resolved_uris(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        body=None,  # type: Optional["_models.BatchGetResolvedURIs"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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/operations/_dataset_controller_v2_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_dataset_controller_v2_operations.py
new file mode 100644
index 00000000..05d64736
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_dataset_controller_v2_operations.py
@@ -0,0 +1,1300 @@
+# 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 TYPE_CHECKING
+import warnings
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from msrest import Serializer
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Generic, Iterable, List, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_get_dataset_definition_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions/{version}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_all_dataset_definitions_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    continuation_token_parameter = kwargs.pop('continuation_token_parameter', None)  # type: Optional[str]
+    page_size = kwargs.pop('page_size', None)  # type: Optional[int]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if continuation_token_parameter is not None:
+        query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str')
+    if page_size is not None:
+        query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_update_definition_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+    register_as_pending = kwargs.pop('register_as_pending', False)  # type: Optional[bool]
+    force_update = kwargs.pop('force_update', False)  # type: Optional[bool]
+    dataset_type = kwargs.pop('dataset_type', None)  # type: Optional[str]
+    user_version_id = kwargs.pop('user_version_id', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if register_as_pending is not None:
+        query_parameters['registerAsPending'] = _SERIALIZER.query("register_as_pending", register_as_pending, 'bool')
+    if force_update is not None:
+        query_parameters['forceUpdate'] = _SERIALIZER.query("force_update", force_update, 'bool')
+    if dataset_type is not None:
+        query_parameters['datasetType'] = _SERIALIZER.query("dataset_type", dataset_type, 'str')
+    if user_version_id is not None:
+        query_parameters['userVersionId'] = _SERIALIZER.query("user_version_id", user_version_id, 'str')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_all_dataset_versions_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    continuation_token_parameter = kwargs.pop('continuation_token_parameter', None)  # type: Optional[str]
+    page_size = kwargs.pop('page_size', None)  # type: Optional[int]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/versions')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if continuation_token_parameter is not None:
+        query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str')
+    if page_size is not None:
+        query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_dataset_by_name_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    version_id = kwargs.pop('version_id', None)  # type: Optional[str]
+    include_latest_definition = kwargs.pop('include_latest_definition', True)  # type: Optional[bool]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetName": _SERIALIZER.url("dataset_name", dataset_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if version_id is not None:
+        query_parameters['versionId'] = _SERIALIZER.query("version_id", version_id, 'str')
+    if include_latest_definition is not None:
+        query_parameters['includeLatestDefinition'] = _SERIALIZER.query("include_latest_definition", include_latest_definition, 'bool')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    dataset_names = kwargs.pop('dataset_names', None)  # type: Optional[List[str]]
+    search_text = kwargs.pop('search_text', None)  # type: Optional[str]
+    include_invisible = kwargs.pop('include_invisible', False)  # type: Optional[bool]
+    status = kwargs.pop('status', None)  # type: Optional[str]
+    continuation_token_parameter = kwargs.pop('continuation_token_parameter', None)  # type: Optional[str]
+    page_size = kwargs.pop('page_size', None)  # type: Optional[int]
+    include_latest_definition = kwargs.pop('include_latest_definition', False)  # type: Optional[bool]
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    order_by_asc = kwargs.pop('order_by_asc', False)  # type: Optional[bool]
+    dataset_types = kwargs.pop('dataset_types', None)  # type: Optional[List[str]]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if dataset_names is not None:
+        query_parameters['datasetNames'] = _SERIALIZER.query("dataset_names", dataset_names, '[str]')
+    if search_text is not None:
+        query_parameters['searchText'] = _SERIALIZER.query("search_text", search_text, 'str')
+    if include_invisible is not None:
+        query_parameters['includeInvisible'] = _SERIALIZER.query("include_invisible", include_invisible, 'bool')
+    if status is not None:
+        query_parameters['status'] = _SERIALIZER.query("status", status, 'str')
+    if continuation_token_parameter is not None:
+        query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str')
+    if page_size is not None:
+        query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int')
+    if include_latest_definition is not None:
+        query_parameters['includeLatestDefinition'] = _SERIALIZER.query("include_latest_definition", include_latest_definition, 'bool')
+    if order_by is not None:
+        query_parameters['orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if order_by_asc is not None:
+        query_parameters['orderByAsc'] = _SERIALIZER.query("order_by_asc", order_by_asc, 'bool')
+    if dataset_types is not None:
+        query_parameters['datasetTypes'] = _SERIALIZER.query("dataset_types", dataset_types, '[str]')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_register_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+    register_as_pending = kwargs.pop('register_as_pending', False)  # type: Optional[bool]
+    if_exists_ok = kwargs.pop('if_exists_ok', True)  # type: Optional[bool]
+    update_definition_if_exists = kwargs.pop('update_definition_if_exists', False)  # type: Optional[bool]
+    with_data_hash = kwargs.pop('with_data_hash', False)  # type: Optional[bool]
+    user_version_id = kwargs.pop('user_version_id', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if register_as_pending is not None:
+        query_parameters['registerAsPending'] = _SERIALIZER.query("register_as_pending", register_as_pending, 'bool')
+    if if_exists_ok is not None:
+        query_parameters['ifExistsOk'] = _SERIALIZER.query("if_exists_ok", if_exists_ok, 'bool')
+    if update_definition_if_exists is not None:
+        query_parameters['updateDefinitionIfExists'] = _SERIALIZER.query("update_definition_if_exists", update_definition_if_exists, 'bool')
+    if with_data_hash is not None:
+        query_parameters['withDataHash'] = _SERIALIZER.query("with_data_hash", with_data_hash, 'bool')
+    if user_version_id is not None:
+        query_parameters['userVersionId'] = _SERIALIZER.query("user_version_id", user_version_id, 'str')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_all_datasets_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_update_dataset_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+    force_update = kwargs.pop('force_update', False)  # type: Optional[bool]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if force_update is not None:
+        query_parameters['forceUpdate'] = _SERIALIZER.query("force_update", force_update, 'bool')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_unregister_dataset_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class DatasetControllerV2Operations(object):
+    """DatasetControllerV2Operations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def get_dataset_definition(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        continuation_token_parameter=None,  # type: Optional[str]
+        page_size=None,  # type: Optional[int]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_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.paging.ItemPaged[~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
+
+        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, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = 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 ItemPaged(
+            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
+    def update_definition(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        register_as_pending=False,  # type: Optional[bool]
+        force_update=False,  # type: Optional[bool]
+        dataset_type=None,  # type: Optional[str]
+        user_version_id=None,  # type: Optional[str]
+        body=None,  # type: Optional["_models.DatasetDefinition"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        continuation_token_parameter=None,  # type: Optional[str]
+        page_size=None,  # type: Optional[int]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_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.paging.ItemPaged[~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
+
+        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, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = 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 ItemPaged(
+            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
+    def get_dataset_by_name(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_name,  # type: str
+        version_id=None,  # type: Optional[str]
+        include_latest_definition=True,  # type: Optional[bool]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_names=None,  # type: Optional[List[str]]
+        search_text=None,  # type: Optional[str]
+        include_invisible=False,  # type: Optional[bool]
+        status=None,  # type: Optional[str]
+        continuation_token_parameter=None,  # type: Optional[str]
+        page_size=None,  # type: Optional[int]
+        include_latest_definition=False,  # type: Optional[bool]
+        order_by=None,  # type: Optional[str]
+        order_by_asc=False,  # type: Optional[bool]
+        dataset_types=None,  # type: Optional[List[str]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_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.paging.ItemPaged[~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
+
+        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, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = 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 ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': '/dataset/v1.2/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+    @distributed_trace
+    def register(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        register_as_pending=False,  # type: Optional[bool]
+        if_exists_ok=True,  # type: Optional[bool]
+        update_definition_if_exists=False,  # type: Optional[bool]
+        with_data_hash=False,  # type: Optional[bool]
+        user_version_id=None,  # type: Optional[str]
+        body=None,  # type: Optional["_models.Dataset"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def delete_all_datasets(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> 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 = 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
+    def update_dataset(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        force_update=False,  # type: Optional[bool]
+        body=None,  # type: Optional["_models.Dataset"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def unregister_dataset(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> 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 = 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/operations/_dataset_v2_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_dataset_v2_operations.py
new file mode 100644
index 00000000..7f686ab6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_dataset_v2_operations.py
@@ -0,0 +1,905 @@
+# 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 TYPE_CHECKING
+import warnings
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from msrest import Serializer
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Generic, Iterable, List, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_create_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+    if_exists_update = kwargs.pop('if_exists_update', False)  # type: Optional[bool]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if if_exists_update is not None:
+        query_parameters['ifExistsUpdate'] = _SERIALIZER.query("if_exists_update", if_exists_update, 'bool')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_all_datasets_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    names = kwargs.pop('names', None)  # type: Optional[List[str]]
+    search_text = kwargs.pop('search_text', None)  # type: Optional[str]
+    continuation_token_parameter = kwargs.pop('continuation_token_parameter', None)  # type: Optional[str]
+    page_size = kwargs.pop('page_size', None)  # type: Optional[int]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if names is not None:
+        query_parameters['names'] = _SERIALIZER.query("names", names, '[str]')
+    if search_text is not None:
+        query_parameters['searchText'] = _SERIALIZER.query("search_text", search_text, 'str')
+    if continuation_token_parameter is not None:
+        query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str')
+    if page_size is not None:
+        query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_dataset_by_name_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}/versions/{versionId}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "versionId": _SERIALIZER.url("version_id", version_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_update_dataset_by_name_and_version_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    version_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}/versions/{versionId}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+        "versionId": _SERIALIZER.url("version_id", version_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_dataset_by_id_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_update_dataset_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_dataset_by_name_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    version_id = kwargs.pop('version_id', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetName": _SERIALIZER.url("dataset_name", dataset_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if version_id is not None:
+        query_parameters['versionId'] = _SERIALIZER.query("version_id", version_id, 'str')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class DatasetV2Operations(object):
+    """DatasetV2Operations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def create(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        if_exists_update=False,  # type: Optional[bool]
+        body=None,  # type: Optional["_models.DatasetV2"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def delete_all_datasets(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> 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 = 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,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        names=None,  # type: Optional[List[str]]
+        search_text=None,  # type: Optional[str]
+        continuation_token_parameter=None,  # type: Optional[str]
+        page_size=None,  # type: Optional[int]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_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.paging.ItemPaged[~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
+
+        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, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = 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 ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': '/dataset/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+    @distributed_trace
+    def delete_dataset_by_name(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version_id,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> 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 = 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
+    def update_dataset_by_name_and_version(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        version_id,  # type: str
+        body=None,  # type: Optional["_models.DatasetV2"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def get_dataset_by_id(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def update_dataset(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        body=None,  # type: Optional["_models.DatasetV2"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def get_dataset_by_name(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_name,  # type: str
+        version_id=None,  # type: Optional[str]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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/operations/_datasets_v1_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_datasets_v1_operations.py
new file mode 100644
index 00000000..df1b1710
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_datasets_v1_operations.py
@@ -0,0 +1,1300 @@
+# 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 TYPE_CHECKING
+import warnings
+
+from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from msrest import Serializer
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Generic, Iterable, List, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_get_dataset_definition_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    version,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions/{version}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+        "version": _SERIALIZER.url("version", version, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_all_dataset_definitions_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    continuation_token_parameter = kwargs.pop('continuation_token_parameter', None)  # type: Optional[str]
+    page_size = kwargs.pop('page_size', None)  # type: Optional[int]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if continuation_token_parameter is not None:
+        query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str')
+    if page_size is not None:
+        query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_update_definition_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+    register_as_pending = kwargs.pop('register_as_pending', False)  # type: Optional[bool]
+    force_update = kwargs.pop('force_update', False)  # type: Optional[bool]
+    dataset_type = kwargs.pop('dataset_type', None)  # type: Optional[str]
+    user_version_id = kwargs.pop('user_version_id', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/definitions')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if register_as_pending is not None:
+        query_parameters['registerAsPending'] = _SERIALIZER.query("register_as_pending", register_as_pending, 'bool')
+    if force_update is not None:
+        query_parameters['forceUpdate'] = _SERIALIZER.query("force_update", force_update, 'bool')
+    if dataset_type is not None:
+        query_parameters['datasetType'] = _SERIALIZER.query("dataset_type", dataset_type, 'str')
+    if user_version_id is not None:
+        query_parameters['userVersionId'] = _SERIALIZER.query("user_version_id", user_version_id, 'str')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_all_dataset_versions_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    continuation_token_parameter = kwargs.pop('continuation_token_parameter', None)  # type: Optional[str]
+    page_size = kwargs.pop('page_size', None)  # type: Optional[int]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}/versions')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if continuation_token_parameter is not None:
+        query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str')
+    if page_size is not None:
+        query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_get_dataset_by_name_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    version_id = kwargs.pop('version_id', None)  # type: Optional[str]
+    include_latest_definition = kwargs.pop('include_latest_definition', True)  # type: Optional[bool]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/query/name={datasetName}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetName": _SERIALIZER.url("dataset_name", dataset_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if version_id is not None:
+        query_parameters['versionId'] = _SERIALIZER.query("version_id", version_id, 'str')
+    if include_latest_definition is not None:
+        query_parameters['includeLatestDefinition'] = _SERIALIZER.query("include_latest_definition", include_latest_definition, 'bool')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_list_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    dataset_names = kwargs.pop('dataset_names', None)  # type: Optional[List[str]]
+    search_text = kwargs.pop('search_text', None)  # type: Optional[str]
+    include_invisible = kwargs.pop('include_invisible', False)  # type: Optional[bool]
+    status = kwargs.pop('status', None)  # type: Optional[str]
+    continuation_token_parameter = kwargs.pop('continuation_token_parameter', None)  # type: Optional[str]
+    page_size = kwargs.pop('page_size', None)  # type: Optional[int]
+    include_latest_definition = kwargs.pop('include_latest_definition', False)  # type: Optional[bool]
+    order_by = kwargs.pop('order_by', None)  # type: Optional[str]
+    order_by_asc = kwargs.pop('order_by_asc', False)  # type: Optional[bool]
+    dataset_types = kwargs.pop('dataset_types', None)  # type: Optional[List[str]]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if dataset_names is not None:
+        query_parameters['datasetNames'] = _SERIALIZER.query("dataset_names", dataset_names, '[str]')
+    if search_text is not None:
+        query_parameters['searchText'] = _SERIALIZER.query("search_text", search_text, 'str')
+    if include_invisible is not None:
+        query_parameters['includeInvisible'] = _SERIALIZER.query("include_invisible", include_invisible, 'bool')
+    if status is not None:
+        query_parameters['status'] = _SERIALIZER.query("status", status, 'str')
+    if continuation_token_parameter is not None:
+        query_parameters['continuationToken'] = _SERIALIZER.query("continuation_token_parameter", continuation_token_parameter, 'str')
+    if page_size is not None:
+        query_parameters['pageSize'] = _SERIALIZER.query("page_size", page_size, 'int')
+    if include_latest_definition is not None:
+        query_parameters['includeLatestDefinition'] = _SERIALIZER.query("include_latest_definition", include_latest_definition, 'bool')
+    if order_by is not None:
+        query_parameters['orderBy'] = _SERIALIZER.query("order_by", order_by, 'str')
+    if order_by_asc is not None:
+        query_parameters['orderByAsc'] = _SERIALIZER.query("order_by_asc", order_by_asc, 'bool')
+    if dataset_types is not None:
+        query_parameters['datasetTypes'] = _SERIALIZER.query("dataset_types", dataset_types, '[str]')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_register_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+    register_as_pending = kwargs.pop('register_as_pending', False)  # type: Optional[bool]
+    if_exists_ok = kwargs.pop('if_exists_ok', True)  # type: Optional[bool]
+    update_definition_if_exists = kwargs.pop('update_definition_if_exists', False)  # type: Optional[bool]
+    with_data_hash = kwargs.pop('with_data_hash', False)  # type: Optional[bool]
+    user_version_id = kwargs.pop('user_version_id', None)  # type: Optional[str]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if register_as_pending is not None:
+        query_parameters['registerAsPending'] = _SERIALIZER.query("register_as_pending", register_as_pending, 'bool')
+    if if_exists_ok is not None:
+        query_parameters['ifExistsOk'] = _SERIALIZER.query("if_exists_ok", if_exists_ok, 'bool')
+    if update_definition_if_exists is not None:
+        query_parameters['updateDefinitionIfExists'] = _SERIALIZER.query("update_definition_if_exists", update_definition_if_exists, 'bool')
+    if with_data_hash is not None:
+        query_parameters['withDataHash'] = _SERIALIZER.query("with_data_hash", with_data_hash, 'bool')
+    if user_version_id is not None:
+        query_parameters['userVersionId'] = _SERIALIZER.query("user_version_id", user_version_id, 'str')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="POST",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_delete_all_datasets_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_update_dataset_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    dataset_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    content_type = kwargs.pop('content_type', None)  # type: Optional[str]
+    force_update = kwargs.pop('force_update', False)  # type: Optional[bool]
+
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetId}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "datasetId": _SERIALIZER.url("dataset_id", dataset_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct parameters
+    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
+    if force_update is not None:
+        query_parameters['forceUpdate'] = _SERIALIZER.query("force_update", force_update, 'bool')
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    if content_type is not None:
+        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="PUT",
+        url=url,
+        params=query_parameters,
+        headers=header_parameters,
+        **kwargs
+    )
+
+
+def build_unregister_dataset_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{name}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class DatasetsV1Operations(object):
+    """DatasetsV1Operations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def get_dataset_definition(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        version,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        continuation_token_parameter=None,  # type: Optional[str]
+        page_size=None,  # type: Optional[int]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_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.paging.ItemPaged[~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
+
+        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, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = 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 ItemPaged(
+            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
+    def update_definition(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        register_as_pending=False,  # type: Optional[bool]
+        force_update=False,  # type: Optional[bool]
+        dataset_type=None,  # type: Optional[str]
+        user_version_id=None,  # type: Optional[str]
+        body=None,  # type: Optional["_models.DatasetDefinition"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        continuation_token_parameter=None,  # type: Optional[str]
+        page_size=None,  # type: Optional[int]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_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.paging.ItemPaged[~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
+
+        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, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = 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 ItemPaged(
+            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
+    def get_dataset_by_name(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_name,  # type: str
+        version_id=None,  # type: Optional[str]
+        include_latest_definition=True,  # type: Optional[bool]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_names=None,  # type: Optional[List[str]]
+        search_text=None,  # type: Optional[str]
+        include_invisible=False,  # type: Optional[bool]
+        status=None,  # type: Optional[str]
+        continuation_token_parameter=None,  # type: Optional[str]
+        page_size=None,  # type: Optional[int]
+        include_latest_definition=False,  # type: Optional[bool]
+        order_by=None,  # type: Optional[str]
+        order_by_asc=False,  # type: Optional[bool]
+        dataset_types=None,  # type: Optional[List[str]]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> Iterable["_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.paging.ItemPaged[~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
+
+        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, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response = 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 ItemPaged(
+            get_next, extract_data
+        )
+    list.metadata = {'url': '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets'}  # type: ignore
+
+    @distributed_trace
+    def register(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        register_as_pending=False,  # type: Optional[bool]
+        if_exists_ok=True,  # type: Optional[bool]
+        update_definition_if_exists=False,  # type: Optional[bool]
+        with_data_hash=False,  # type: Optional[bool]
+        user_version_id=None,  # type: Optional[str]
+        body=None,  # type: Optional["_models.Dataset"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def delete_all_datasets(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> 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 = 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
+    def update_dataset(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        dataset_id,  # type: str
+        force_update=False,  # type: Optional[bool]
+        body=None,  # type: Optional["_models.Dataset"]
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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
+    def unregister_dataset(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> 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 = 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/operations/_delete_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_delete_operations.py
new file mode 100644
index 00000000..e1cd955c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_delete_operations.py
@@ -0,0 +1,145 @@
+# 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 TYPE_CHECKING
+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 HttpResponse
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from msrest import Serializer
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Generic, Optional, TypeVar
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_data_container_request(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    name,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/data/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datacontainer/{name}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "name": _SERIALIZER.url("name", name, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="DELETE",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class DeleteOperations(object):
+    """DeleteOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    @distributed_trace
+    def data_container(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        name,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> "_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 = 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/operations/_get_operation_status_operations.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_get_operation_status_operations.py
new file mode 100644
index 00000000..085f9749
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_restclient/dataset_dataplane/operations/_get_operation_status_operations.py
@@ -0,0 +1,212 @@
+# 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 TYPE_CHECKING
+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 HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+from msrest import Serializer
+
+from .. import models as _models
+from .._vendor import _convert_request, _format_url_section
+
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any, Callable, Dict, Generic, Optional, TypeVar, Union
+    T = TypeVar('T')
+    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+# fmt: off
+
+def build_get_dataset_operation_status_request_initial(
+    subscription_id,  # type: str
+    resource_group_name,  # type: str
+    workspace_name,  # type: str
+    operation_id,  # type: str
+    **kwargs  # type: Any
+):
+    # type: (...) -> HttpRequest
+    accept = "application/json"
+    # Construct URL
+    url = kwargs.pop("template_url", '/dataset/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/operations/{operationId}')
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
+        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
+        "operationId": _SERIALIZER.url("operation_id", operation_id, 'str'),
+    }
+
+    url = _format_url_section(url, **path_format_arguments)
+
+    # Construct headers
+    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
+    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
+
+    return HttpRequest(
+        method="GET",
+        url=url,
+        headers=header_parameters,
+        **kwargs
+    )
+
+# fmt: on
+class GetOperationStatusOperations(object):
+    """GetOperationStatusOperations operations.
+
+    You should not instantiate this class directly. Instead, you should create a Client instance that
+    instantiates it for you and attaches it as an attribute.
+
+    :ivar models: Alias to model classes used in this operation group.
+    :type models: ~azure.mgmt.machinelearningservices.models
+    :param client: Client for service requests.
+    :param config: Configuration of service client.
+    :param serializer: An object model serializer.
+    :param deserializer: An object model deserializer.
+    """
+
+    models = _models
+
+    def __init__(self, client, config, serializer, deserializer):
+        self._client = client
+        self._serialize = serializer
+        self._deserialize = deserializer
+        self._config = config
+
+    def _get_dataset_operation_status_initial(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        operation_id,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> 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 = 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
+    def begin_get_dataset_operation_status(
+        self,
+        subscription_id,  # type: str
+        resource_group_name,  # type: str
+        workspace_name,  # type: str
+        operation_id,  # type: str
+        **kwargs  # type: Any
+    ):
+        # type: (...) -> LROPoller["_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 ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either
+         LongRunningOperationResponse1LongRunningOperationResponseObject or the result of cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.LongRunningOperationResponse1LongRunningOperationResponseObject]
+        :raises: ~azure.core.exceptions.HttpResponseError
+        """
+        polling = kwargs.pop('polling', True)  # type: Union[bool, azure.core.polling.PollingMethod]
+        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 = 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 = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
+        elif polling is False: polling_method = NoPolling()
+        else: polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output
+            )
+        else:
+            return LROPoller(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