diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/_meta.json b/sdk/machinelearning/azure-mgmt-machinelearningservices/_meta.json
index 216b79b26570..79f60642c4c5 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/_meta.json
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/_meta.json
@@ -1,11 +1,11 @@
 {
-  "commit": "b32e1896f30e6ea155449cb49719a6286e32b961",
+  "commit": "599a0ee791f6aebbd27879f04a07011f02380c81",
   "repository_url": "https://github.com/Azure/azure-rest-api-specs",
   "autorest": "3.9.2",
   "use": [
-    "@autorest/python@6.2.7",
+    "@autorest/python@6.4.3",
     "@autorest/modelerfour@4.24.3"
   ],
-  "autorest_command": "autorest specification/machinelearningservices/resource-manager/readme.md --generate-sample=True --include-x-ms-examples-original-file=True --python --python-sdks-folder=/home/vsts/work/1/azure-sdk-for-python/sdk --use=@autorest/python@6.2.7 --use=@autorest/modelerfour@4.24.3 --version=3.9.2 --version-tolerant=False",
+  "autorest_command": "autorest specification/machinelearningservices/resource-manager/readme.md --generate-sample=True --include-x-ms-examples-original-file=True --python --python-sdks-folder=/mnt/vss/_work/1/s/azure-sdk-for-python/sdk --use=@autorest/python@6.4.3 --use=@autorest/modelerfour@4.24.3 --version=3.9.2 --version-tolerant=False",
   "readme": "specification/machinelearningservices/resource-manager/readme.md"
 }
\ No newline at end of file
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_configuration.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_configuration.py
index 12c43638a412..714bb8882944 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_configuration.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_configuration.py
@@ -35,14 +35,14 @@ class MachineLearningServicesMgmtClientConfiguration(Configuration):  # pylint:
     :type credential: ~azure.core.credentials.TokenCredential
     :param subscription_id: The ID of the target subscription. Required.
     :type subscription_id: str
-    :keyword api_version: Api Version. Default value is "2022-10-01". Note that overriding this
+    :keyword api_version: Api Version. Default value is "2023-04-01". Note that overriding this
      default value may result in unsupported behavior.
     :paramtype api_version: str
     """
 
     def __init__(self, credential: "TokenCredential", subscription_id: str, **kwargs: Any) -> None:
         super(MachineLearningServicesMgmtClientConfiguration, self).__init__(**kwargs)
-        api_version: Literal["2022-10-01"] = kwargs.pop("api_version", "2022-10-01")
+        api_version: Literal["2023-04-01"] = kwargs.pop("api_version", "2023-04-01")
 
         if credential is None:
             raise ValueError("Parameter 'credential' must not be None.")
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_machine_learning_services_mgmt_client.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_machine_learning_services_mgmt_client.py
index feffb8cfc3ed..89db7c4a90ff 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_machine_learning_services_mgmt_client.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_machine_learning_services_mgmt_client.py
@@ -37,6 +37,7 @@
     PrivateEndpointConnectionsOperations,
     PrivateLinkResourcesOperations,
     QuotasOperations,
+    RegistriesOperations,
     SchedulesOperations,
     UsagesOperations,
     VirtualMachineSizesOperations,
@@ -120,6 +121,8 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
      azure.mgmt.machinelearningservices.operations.OnlineDeploymentsOperations
     :ivar schedules: SchedulesOperations operations
     :vartype schedules: azure.mgmt.machinelearningservices.operations.SchedulesOperations
+    :ivar registries: RegistriesOperations operations
+    :vartype registries: azure.mgmt.machinelearningservices.operations.RegistriesOperations
     :ivar workspace_features: WorkspaceFeaturesOperations operations
     :vartype workspace_features:
      azure.mgmt.machinelearningservices.operations.WorkspaceFeaturesOperations
@@ -129,7 +132,7 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
     :type subscription_id: str
     :param base_url: Service URL. Default value is "https://management.azure.com".
     :type base_url: str
-    :keyword api_version: Api Version. Default value is "2022-10-01". Note that overriding this
+    :keyword api_version: Api Version. Default value is "2023-04-01". Note that overriding this
      default value may result in unsupported behavior.
     :paramtype api_version: str
     :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
@@ -146,7 +149,7 @@ def __init__(
         self._config = MachineLearningServicesMgmtClientConfiguration(
             credential=credential, subscription_id=subscription_id, **kwargs
         )
-        self._client = ARMPipelineClient(base_url=base_url, config=self._config, **kwargs)
+        self._client: ARMPipelineClient = ARMPipelineClient(base_url=base_url, config=self._config, **kwargs)
 
         client_models = {k: v for k, v in _models.__dict__.items() if isinstance(v, type)}
         self._serialize = Serializer(client_models)
@@ -202,6 +205,7 @@ def __init__(
             self._client, self._config, self._serialize, self._deserialize
         )
         self.schedules = SchedulesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.registries = RegistriesOperations(self._client, self._config, self._serialize, self._deserialize)
         self.workspace_features = WorkspaceFeaturesOperations(
             self._client, self._config, self._serialize, self._deserialize
         )
@@ -235,5 +239,5 @@ def __enter__(self) -> "MachineLearningServicesMgmtClient":
         self._client.__enter__()
         return self
 
-    def __exit__(self, *exc_details) -> None:
+    def __exit__(self, *exc_details: Any) -> None:
         self._client.__exit__(*exc_details)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_serialization.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_serialization.py
index 2c170e28dbca..f17c068e833e 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_serialization.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_serialization.py
@@ -38,7 +38,22 @@
 import re
 import sys
 import codecs
-from typing import Optional, Union, AnyStr, IO, Mapping
+from typing import (
+    Dict,
+    Any,
+    cast,
+    Optional,
+    Union,
+    AnyStr,
+    IO,
+    Mapping,
+    Callable,
+    TypeVar,
+    MutableMapping,
+    Type,
+    List,
+    Mapping,
+)
 
 try:
     from urllib import quote  # type: ignore
@@ -48,12 +63,14 @@
 
 import isodate  # type: ignore
 
-from typing import Dict, Any, cast
-
 from azure.core.exceptions import DeserializationError, SerializationError, raise_with_traceback
+from azure.core.serialization import NULL as AzureCoreNull
 
 _BOM = codecs.BOM_UTF8.decode(encoding="utf-8")
 
+ModelType = TypeVar("ModelType", bound="Model")
+JSON = MutableMapping[str, Any]
+
 
 class RawDeserializer:
 
@@ -277,8 +294,8 @@ class Model(object):
     _attribute_map: Dict[str, Dict[str, Any]] = {}
     _validation: Dict[str, Dict[str, Any]] = {}
 
-    def __init__(self, **kwargs):
-        self.additional_properties = {}
+    def __init__(self, **kwargs: Any) -> None:
+        self.additional_properties: Dict[str, Any] = {}
         for k in kwargs:
             if k not in self._attribute_map:
                 _LOGGER.warning("%s is not a known attribute of class %s and will be ignored", k, self.__class__)
@@ -287,25 +304,25 @@ def __init__(self, **kwargs):
             else:
                 setattr(self, k, kwargs[k])
 
-    def __eq__(self, other):
+    def __eq__(self, other: Any) -> bool:
         """Compare objects by comparing all attributes."""
         if isinstance(other, self.__class__):
             return self.__dict__ == other.__dict__
         return False
 
-    def __ne__(self, other):
+    def __ne__(self, other: Any) -> bool:
         """Compare objects by comparing all attributes."""
         return not self.__eq__(other)
 
-    def __str__(self):
+    def __str__(self) -> str:
         return str(self.__dict__)
 
     @classmethod
-    def enable_additional_properties_sending(cls):
+    def enable_additional_properties_sending(cls) -> None:
         cls._attribute_map["additional_properties"] = {"key": "", "type": "{object}"}
 
     @classmethod
-    def is_xml_model(cls):
+    def is_xml_model(cls) -> bool:
         try:
             cls._xml_map  # type: ignore
         except AttributeError:
@@ -322,7 +339,7 @@ def _create_xml_node(cls):
 
         return _create_xml_node(xml_map.get("name", cls.__name__), xml_map.get("prefix", None), xml_map.get("ns", None))
 
-    def serialize(self, keep_readonly=False, **kwargs):
+    def serialize(self, keep_readonly: bool = False, **kwargs: Any) -> JSON:
         """Return the JSON that would be sent to azure from this model.
 
         This is an alias to `as_dict(full_restapi_key_transformer, keep_readonly=False)`.
@@ -336,8 +353,13 @@ def serialize(self, keep_readonly=False, **kwargs):
         serializer = Serializer(self._infer_class_models())
         return serializer._serialize(self, keep_readonly=keep_readonly, **kwargs)
 
-    def as_dict(self, keep_readonly=True, key_transformer=attribute_transformer, **kwargs):
-        """Return a dict that can be JSONify using json.dump.
+    def as_dict(
+        self,
+        keep_readonly: bool = True,
+        key_transformer: Callable[[str, Dict[str, Any], Any], Any] = attribute_transformer,
+        **kwargs: Any
+    ) -> JSON:
+        """Return a dict that can be serialized using json.dump.
 
         Advanced usage might optionally use a callback as parameter:
 
@@ -384,7 +406,7 @@ def _infer_class_models(cls):
         return client_models
 
     @classmethod
-    def deserialize(cls, data, content_type=None):
+    def deserialize(cls: Type[ModelType], data: Any, content_type: Optional[str] = None) -> ModelType:
         """Parse a str using the RestAPI syntax and return a model.
 
         :param str data: A str using RestAPI structure. JSON by default.
@@ -396,7 +418,12 @@ def deserialize(cls, data, content_type=None):
         return deserializer(cls.__name__, data, content_type=content_type)
 
     @classmethod
-    def from_dict(cls, data, key_extractors=None, content_type=None):
+    def from_dict(
+        cls: Type[ModelType],
+        data: Any,
+        key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None,
+        content_type: Optional[str] = None,
+    ) -> ModelType:
         """Parse a dict using given key extractor return a model.
 
         By default consider key
@@ -409,8 +436,8 @@ def from_dict(cls, data, key_extractors=None, content_type=None):
         :raises: DeserializationError if something went wrong
         """
         deserializer = Deserializer(cls._infer_class_models())
-        deserializer.key_extractors = (
-            [
+        deserializer.key_extractors = (  # type: ignore
+            [  # type: ignore
                 attribute_key_case_insensitive_extractor,
                 rest_key_case_insensitive_extractor,
                 last_rest_key_case_insensitive_extractor,
@@ -518,7 +545,7 @@ class Serializer(object):
         "multiple": lambda x, y: x % y != 0,
     }
 
-    def __init__(self, classes=None):
+    def __init__(self, classes: Optional[Mapping[str, Type[ModelType]]] = None):
         self.serialize_type = {
             "iso-8601": Serializer.serialize_iso,
             "rfc-1123": Serializer.serialize_rfc,
@@ -534,7 +561,7 @@ def __init__(self, classes=None):
             "[]": self.serialize_iter,
             "{}": self.serialize_dict,
         }
-        self.dependencies = dict(classes) if classes else {}
+        self.dependencies: Dict[str, Type[ModelType]] = dict(classes) if classes else {}
         self.key_transformer = full_restapi_key_transformer
         self.client_side_validation = True
 
@@ -626,8 +653,7 @@ def _serialize(self, target_obj, data_type=None, **kwargs):
                             serialized.append(local_node)  # type: ignore
                     else:  # JSON
                         for k in reversed(keys):  # type: ignore
-                            unflattened = {k: new_attr}
-                            new_attr = unflattened
+                            new_attr = {k: new_attr}
 
                         _new_attr = new_attr
                         _serialized = serialized
@@ -656,8 +682,8 @@ def body(self, data, data_type, **kwargs):
         """
 
         # Just in case this is a dict
-        internal_data_type = data_type.strip("[]{}")
-        internal_data_type = self.dependencies.get(internal_data_type, None)
+        internal_data_type_str = data_type.strip("[]{}")
+        internal_data_type = self.dependencies.get(internal_data_type_str, None)
         try:
             is_xml_model_serialization = kwargs["is_xml"]
         except KeyError:
@@ -777,6 +803,8 @@ def serialize_data(self, data, data_type, **kwargs):
             raise ValueError("No value for given attribute")
 
         try:
+            if data is AzureCoreNull:
+                return None
             if data_type in self.basic_types.values():
                 return self.serialize_basic(data, data_type, **kwargs)
 
@@ -1161,7 +1189,8 @@ def rest_key_extractor(attr, attr_desc, data):
     working_data = data
 
     while "." in key:
-        dict_keys = _FLATTEN.split(key)
+        # Need the cast, as for some reasons "split" is typed as list[str | Any]
+        dict_keys = cast(List[str], _FLATTEN.split(key))
         if len(dict_keys) == 1:
             key = _decode_attribute_map_key(dict_keys[0])
             break
@@ -1332,7 +1361,7 @@ class Deserializer(object):
 
     valid_date = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}" r"\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?")
 
-    def __init__(self, classes=None):
+    def __init__(self, classes: Optional[Mapping[str, Type[ModelType]]] = None):
         self.deserialize_type = {
             "iso-8601": Deserializer.deserialize_iso,
             "rfc-1123": Deserializer.deserialize_rfc,
@@ -1352,7 +1381,7 @@ def __init__(self, classes=None):
             "duration": (isodate.Duration, datetime.timedelta),
             "iso-8601": (datetime.datetime),
         }
-        self.dependencies = dict(classes) if classes else {}
+        self.dependencies: Dict[str, Type[ModelType]] = dict(classes) if classes else {}
         self.key_extractors = [rest_key_extractor, xml_key_extractor]
         # Additional properties only works if the "rest_key_extractor" is used to
         # extract the keys. Making it to work whatever the key extractor is too much
@@ -1471,7 +1500,7 @@ def _classify_target(self, target, data):
         Once classification has been determined, initialize object.
 
         :param str target: The target object type to deserialize to.
-        :param str/dict data: The response data to deseralize.
+        :param str/dict data: The response data to deserialize.
         """
         if target is None:
             return None, None
@@ -1486,7 +1515,7 @@ def _classify_target(self, target, data):
             target = target._classify(data, self.dependencies)
         except AttributeError:
             pass  # Target is not a Model, no classify
-        return target, target.__class__.__name__
+        return target, target.__class__.__name__  # type: ignore
 
     def failsafe_deserialize(self, target_obj, data, content_type=None):
         """Ignores any errors encountered in deserialization,
@@ -1496,7 +1525,7 @@ def failsafe_deserialize(self, target_obj, data, content_type=None):
         a deserialization error.
 
         :param str target_obj: The target object type to deserialize to.
-        :param str/dict data: The response data to deseralize.
+        :param str/dict data: The response data to deserialize.
         :param str content_type: Swagger "produces" if available.
         """
         try:
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_vendor.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_vendor.py
index 9aad73fc743e..bd0df84f5319 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_vendor.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_vendor.py
@@ -5,6 +5,8 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 
+from typing import List, cast
+
 from azure.core.pipeline.transport import HttpRequest
 
 
@@ -22,6 +24,7 @@ def _format_url_section(template, **kwargs):
         try:
             return template.format(**kwargs)
         except KeyError as key:
-            formatted_components = template.split("/")
+            # Need the cast, as for some reasons "split" is typed as list[str | Any]
+            formatted_components = cast(List[str], template.split("/"))
             components = [c for c in formatted_components if "{}".format(key.args[0]) not in c]
             template = "/".join(components)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_version.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_version.py
index e32dc6ec4218..e5754a47ce68 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_version.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_version.py
@@ -6,4 +6,4 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 
-VERSION = "2.0.0b1"
+VERSION = "1.0.0b1"
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_configuration.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_configuration.py
index 88eada53ff68..51ad2016f27c 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_configuration.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_configuration.py
@@ -35,14 +35,14 @@ class MachineLearningServicesMgmtClientConfiguration(Configuration):  # pylint:
     :type credential: ~azure.core.credentials_async.AsyncTokenCredential
     :param subscription_id: The ID of the target subscription. Required.
     :type subscription_id: str
-    :keyword api_version: Api Version. Default value is "2022-10-01". Note that overriding this
+    :keyword api_version: Api Version. Default value is "2023-04-01". Note that overriding this
      default value may result in unsupported behavior.
     :paramtype api_version: str
     """
 
     def __init__(self, credential: "AsyncTokenCredential", subscription_id: str, **kwargs: Any) -> None:
         super(MachineLearningServicesMgmtClientConfiguration, self).__init__(**kwargs)
-        api_version: Literal["2022-10-01"] = kwargs.pop("api_version", "2022-10-01")
+        api_version: Literal["2023-04-01"] = kwargs.pop("api_version", "2023-04-01")
 
         if credential is None:
             raise ValueError("Parameter 'credential' must not be None.")
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_machine_learning_services_mgmt_client.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_machine_learning_services_mgmt_client.py
index 4c1c840cfff5..75515d93edcd 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_machine_learning_services_mgmt_client.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_machine_learning_services_mgmt_client.py
@@ -37,6 +37,7 @@
     PrivateEndpointConnectionsOperations,
     PrivateLinkResourcesOperations,
     QuotasOperations,
+    RegistriesOperations,
     SchedulesOperations,
     UsagesOperations,
     VirtualMachineSizesOperations,
@@ -123,6 +124,8 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
      azure.mgmt.machinelearningservices.aio.operations.OnlineDeploymentsOperations
     :ivar schedules: SchedulesOperations operations
     :vartype schedules: azure.mgmt.machinelearningservices.aio.operations.SchedulesOperations
+    :ivar registries: RegistriesOperations operations
+    :vartype registries: azure.mgmt.machinelearningservices.aio.operations.RegistriesOperations
     :ivar workspace_features: WorkspaceFeaturesOperations operations
     :vartype workspace_features:
      azure.mgmt.machinelearningservices.aio.operations.WorkspaceFeaturesOperations
@@ -132,7 +135,7 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
     :type subscription_id: str
     :param base_url: Service URL. Default value is "https://management.azure.com".
     :type base_url: str
-    :keyword api_version: Api Version. Default value is "2022-10-01". Note that overriding this
+    :keyword api_version: Api Version. Default value is "2023-04-01". Note that overriding this
      default value may result in unsupported behavior.
     :paramtype api_version: str
     :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
@@ -149,7 +152,7 @@ def __init__(
         self._config = MachineLearningServicesMgmtClientConfiguration(
             credential=credential, subscription_id=subscription_id, **kwargs
         )
-        self._client = AsyncARMPipelineClient(base_url=base_url, config=self._config, **kwargs)
+        self._client: AsyncARMPipelineClient = AsyncARMPipelineClient(base_url=base_url, config=self._config, **kwargs)
 
         client_models = {k: v for k, v in _models.__dict__.items() if isinstance(v, type)}
         self._serialize = Serializer(client_models)
@@ -205,6 +208,7 @@ def __init__(
             self._client, self._config, self._serialize, self._deserialize
         )
         self.schedules = SchedulesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.registries = RegistriesOperations(self._client, self._config, self._serialize, self._deserialize)
         self.workspace_features = WorkspaceFeaturesOperations(
             self._client, self._config, self._serialize, self._deserialize
         )
@@ -238,5 +242,5 @@ async def __aenter__(self) -> "MachineLearningServicesMgmtClient":
         await self._client.__aenter__()
         return self
 
-    async def __aexit__(self, *exc_details) -> None:
+    async def __aexit__(self, *exc_details: Any) -> None:
         await self._client.__aexit__(*exc_details)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/__init__.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/__init__.py
index 0065485916d9..3a8e74c05cba 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/__init__.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/__init__.py
@@ -32,6 +32,7 @@
 from ._online_endpoints_operations import OnlineEndpointsOperations
 from ._online_deployments_operations import OnlineDeploymentsOperations
 from ._schedules_operations import SchedulesOperations
+from ._registries_operations import RegistriesOperations
 from ._workspace_features_operations import WorkspaceFeaturesOperations
 
 from ._patch import __all__ as _patch_all
@@ -65,6 +66,7 @@
     "OnlineEndpointsOperations",
     "OnlineDeploymentsOperations",
     "SchedulesOperations",
+    "RegistriesOperations",
     "WorkspaceFeaturesOperations",
 ]
 __all__.extend([p for p in _patch_all if p not in __all__])
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_deployments_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_deployments_operations.py
index 37950b084904..b2c5c6241114 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_deployments_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_deployments_operations.py
@@ -103,7 +103,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchDeploymentTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -163,8 +163,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -195,7 +196,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -214,8 +215,9 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -272,7 +274,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -349,7 +351,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchDeployment] = kwargs.pop("cls", None)
@@ -368,8 +370,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -410,7 +413,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -441,8 +444,9 @@ async def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -587,8 +591,8 @@ async def begin_update(
         :type endpoint_name: str
         :param deployment_name: The identifier for the Batch inference deployment. Required.
         :type deployment_name: str
-        :param body: Batch inference deployment definition object. Is either a model type or a IO type.
-         Required.
+        :param body: Batch inference deployment definition object. Is either a
+         PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties type or a IO type. Required.
         :type body:
          ~azure.mgmt.machinelearningservices.models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties
          or IO
@@ -612,7 +616,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -681,7 +685,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -712,8 +716,9 @@ async def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -859,8 +864,8 @@ async def begin_create_or_update(
         :type endpoint_name: str
         :param deployment_name: The identifier for the Batch inference deployment. Required.
         :type deployment_name: str
-        :param body: Batch inference deployment definition object. Is either a model type or a IO type.
-         Required.
+        :param body: Batch inference deployment definition object. Is either a BatchDeployment type or
+         a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.BatchDeployment or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -882,7 +887,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_endpoints_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_endpoints_operations.py
index 848fe6dc7e27..218202358cd5 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_endpoints_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_endpoints_operations.py
@@ -98,7 +98,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchEndpointTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -156,8 +156,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -188,7 +189,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -206,8 +207,9 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -262,7 +264,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -336,7 +338,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None)
@@ -354,8 +356,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -395,7 +398,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -425,8 +428,9 @@ async def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -562,8 +566,8 @@ async def begin_update(
         :type workspace_name: str
         :param endpoint_name: Name for the Batch inference endpoint. Required.
         :type endpoint_name: str
-        :param body: Mutable batch inference endpoint definition object. Is either a model type or a IO
-         type. Required.
+        :param body: Mutable batch inference endpoint definition object. Is either a
+         PartialMinimalTrackedResourceWithIdentity type or a IO type. Required.
         :type body:
          ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithIdentity or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -586,7 +590,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -653,7 +657,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -683,8 +687,9 @@ async def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -821,8 +826,8 @@ async def begin_create_or_update(
         :type workspace_name: str
         :param endpoint_name: Name for the Batch inference endpoint. Required.
         :type endpoint_name: str
-        :param body: Batch inference endpoint definition object. Is either a model type or a IO type.
-         Required.
+        :param body: Batch inference endpoint definition object. Is either a BatchEndpoint type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.BatchEndpoint or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -844,7 +849,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -923,7 +928,7 @@ async def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None)
@@ -941,8 +946,9 @@ async def list_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_containers_operations.py
index c69213fa842c..5bdc6d6ba49f 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_containers_operations.py
@@ -87,7 +87,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -144,8 +144,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -193,7 +194,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -211,8 +212,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -260,7 +262,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeContainer] = kwargs.pop("cls", None)
@@ -278,8 +280,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -386,7 +389,7 @@ async def create_or_update(
         :type workspace_name: str
         :param name: Container name. This is case-sensitive. Required.
         :type name: str
-        :param body: Container entity to create or update. Is either a model type or a IO type.
+        :param body: Container entity to create or update. Is either a CodeContainer type or a IO type.
          Required.
         :type body: ~azure.mgmt.machinelearningservices.models.CodeContainer or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -408,7 +411,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -438,8 +441,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_versions_operations.py
index 473dec5348b9..f11ffc01acf5 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_versions_operations.py
@@ -100,7 +100,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -160,8 +160,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -211,7 +212,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -230,8 +231,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -281,7 +283,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeVersion] = kwargs.pop("cls", None)
@@ -300,8 +302,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -417,7 +420,8 @@ async def create_or_update(
         :type name: str
         :param version: Version identifier. This is case-sensitive. Required.
         :type version: str
-        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :param body: Version entity to create or update. Is either a CodeVersion type or a IO type.
+         Required.
         :type body: ~azure.mgmt.machinelearningservices.models.CodeVersion or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -438,7 +442,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -469,8 +473,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_containers_operations.py
index 59f121afb4b1..ea694337a70e 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_containers_operations.py
@@ -95,7 +95,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -153,8 +153,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -202,7 +203,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -220,8 +221,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -269,7 +271,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentContainer] = kwargs.pop("cls", None)
@@ -287,8 +289,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -395,8 +398,8 @@ async def create_or_update(
         :type workspace_name: str
         :param name: Container name. Required.
         :type name: str
-        :param body: Container entity to create or update. Is either a model type or a IO type.
-         Required.
+        :param body: Container entity to create or update. Is either a ComponentContainer type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.ComponentContainer or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -417,7 +420,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -447,8 +450,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_versions_operations.py
index f217f6650def..8c9c328af4d9 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_versions_operations.py
@@ -104,7 +104,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -165,8 +165,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -216,7 +217,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -235,8 +236,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -286,7 +288,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentVersion] = kwargs.pop("cls", None)
@@ -305,8 +307,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -422,7 +425,8 @@ async def create_or_update(
         :type name: str
         :param version: Version identifier. Required.
         :type version: str
-        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :param body: Version entity to create or update. Is either a ComponentVersion type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.ComponentVersion or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -443,7 +447,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -474,8 +478,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_compute_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_compute_operations.py
index 382828847481..54f37028d9e7 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_compute_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_compute_operations.py
@@ -93,7 +93,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PaginatedComputeResourcesList] = kwargs.pop("cls", None)
@@ -150,8 +150,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -198,7 +199,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComputeResource] = kwargs.pop("cls", None)
@@ -216,8 +217,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -257,7 +259,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -287,8 +289,9 @@ async def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -422,8 +425,8 @@ async def begin_create_or_update(
         :type workspace_name: str
         :param compute_name: Name of the Azure Machine Learning compute. Required.
         :type compute_name: str
-        :param parameters: Payload with Machine Learning compute definition. Is either a model type or
-         a IO type. Required.
+        :param parameters: Payload with Machine Learning compute definition. Is either a
+         ComputeResource type or a IO type. Required.
         :type parameters: ~azure.mgmt.machinelearningservices.models.ComputeResource or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -445,7 +448,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -512,7 +515,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -542,8 +545,9 @@ async def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -665,8 +669,8 @@ async def begin_update(
         :type workspace_name: str
         :param compute_name: Name of the Azure Machine Learning compute. Required.
         :type compute_name: str
-        :param parameters: Additional parameters for cluster update. Is either a model type or a IO
-         type. Required.
+        :param parameters: Additional parameters for cluster update. Is either a
+         ClusterUpdateParameters type or a IO type. Required.
         :type parameters: ~azure.mgmt.machinelearningservices.models.ClusterUpdateParameters or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -688,7 +692,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -755,7 +759,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -774,8 +778,9 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -837,7 +842,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -904,7 +909,7 @@ def list_nodes(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.AmlComputeNodesInformation] = kwargs.pop("cls", None)
@@ -961,8 +966,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -1008,7 +1014,7 @@ async def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComputeSecrets] = kwargs.pop("cls", None)
@@ -1026,8 +1032,9 @@ async def list_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1062,7 +1069,7 @@ async def _start_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1080,8 +1087,9 @@ async def _start_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1126,7 +1134,7 @@ async def begin_start(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1183,7 +1191,7 @@ async def _stop_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1201,8 +1209,9 @@ async def _stop_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1247,7 +1256,7 @@ async def begin_stop(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1304,7 +1313,7 @@ async def _restart_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1322,8 +1331,9 @@ async def _restart_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1368,7 +1378,7 @@ async def begin_restart(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_containers_operations.py
index 4c5aca387b8f..4d34c50f4a16 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_containers_operations.py
@@ -95,7 +95,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -153,8 +153,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -202,7 +203,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -220,8 +221,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -269,7 +271,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataContainer] = kwargs.pop("cls", None)
@@ -287,8 +289,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -395,7 +398,7 @@ async def create_or_update(
         :type workspace_name: str
         :param name: Container name. Required.
         :type name: str
-        :param body: Container entity to create or update. Is either a model type or a IO type.
+        :param body: Container entity to create or update. Is either a DataContainer type or a IO type.
          Required.
         :type body: ~azure.mgmt.machinelearningservices.models.DataContainer or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -417,7 +420,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -447,8 +450,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_versions_operations.py
index a9f90dd943ed..7c7b2da5aa56 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_versions_operations.py
@@ -112,7 +112,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataVersionBaseResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -174,8 +174,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -225,7 +226,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -244,8 +245,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -295,7 +297,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataVersionBase] = kwargs.pop("cls", None)
@@ -314,8 +316,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -431,7 +434,8 @@ async def create_or_update(
         :type name: str
         :param version: Version identifier. Required.
         :type version: str
-        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :param body: Version entity to create or update. Is either a DataVersionBase type or a IO type.
+         Required.
         :type body: ~azure.mgmt.machinelearningservices.models.DataVersionBase or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -452,7 +456,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -483,8 +487,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_datastores_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_datastores_operations.py
index 293c073184e6..4d430b8f1a25 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_datastores_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_datastores_operations.py
@@ -110,7 +110,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DatastoreResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -173,8 +173,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -222,7 +223,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -240,8 +241,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -287,7 +289,7 @@ async def get(self, resource_group_name: str, workspace_name: str, name: str, **
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Datastore] = kwargs.pop("cls", None)
@@ -305,8 +307,9 @@ async def get(self, resource_group_name: str, workspace_name: str, name: str, **
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -420,7 +423,7 @@ async def create_or_update(
         :type workspace_name: str
         :param name: Datastore name. Required.
         :type name: str
-        :param body: Datastore entity to create or update. Is either a model type or a IO type.
+        :param body: Datastore entity to create or update. Is either a Datastore type or a IO type.
          Required.
         :type body: ~azure.mgmt.machinelearningservices.models.Datastore or IO
         :param skip_validation: Flag to skip validation. Default value is False.
@@ -444,7 +447,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -475,8 +478,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -532,7 +536,7 @@ async def list_secrets(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DatastoreSecrets] = kwargs.pop("cls", None)
@@ -550,8 +554,9 @@ async def list_secrets(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_containers_operations.py
index 0115c7250a43..6da1c392aaa3 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_containers_operations.py
@@ -96,7 +96,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -154,8 +154,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -203,7 +204,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -221,8 +222,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -270,7 +272,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentContainer] = kwargs.pop("cls", None)
@@ -288,8 +290,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -396,8 +399,8 @@ async def create_or_update(
         :type workspace_name: str
         :param name: Container name. This is case-sensitive. Required.
         :type name: str
-        :param body: Container entity to create or update. Is either a model type or a IO type.
-         Required.
+        :param body: Container entity to create or update. Is either a EnvironmentContainer type or a
+         IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -418,7 +421,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -448,8 +451,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_versions_operations.py
index 8b4432552f55..4968c67b1ff5 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_versions_operations.py
@@ -104,7 +104,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -165,8 +165,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -216,7 +217,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -235,8 +236,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -286,7 +288,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentVersion] = kwargs.pop("cls", None)
@@ -305,8 +307,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -422,7 +425,8 @@ async def create_or_update(
         :type name: str
         :param version: Version of EnvironmentVersion. Required.
         :type version: str
-        :param body: Definition of EnvironmentVersion. Is either a model type or a IO type. Required.
+        :param body: Definition of EnvironmentVersion. Is either a EnvironmentVersion type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -443,7 +447,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -474,8 +478,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_jobs_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_jobs_operations.py
index e8e3110d7004..a37ed1f4c444 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_jobs_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_jobs_operations.py
@@ -104,7 +104,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.JobBaseResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -164,8 +164,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -196,7 +197,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -214,8 +215,9 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -270,7 +272,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -342,7 +344,7 @@ async def get(self, resource_group_name: str, workspace_name: str, id: str, **kw
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.JobBase] = kwargs.pop("cls", None)
@@ -360,8 +362,9 @@ async def get(self, resource_group_name: str, workspace_name: str, id: str, **kw
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -463,7 +466,7 @@ async def create_or_update(
         :type workspace_name: str
         :param id: The name and identifier for the Job. This is case-sensitive. Required.
         :type id: str
-        :param body: Job definition object. Is either a model type or a IO type. Required.
+        :param body: Job definition object. Is either a JobBase type or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.JobBase or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -484,7 +487,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -514,8 +517,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -554,7 +558,7 @@ async def _cancel_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -572,8 +576,9 @@ async def _cancel_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -625,7 +630,7 @@ async def begin_cancel(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_containers_operations.py
index 0f18875a197a..2f685504f69c 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_containers_operations.py
@@ -98,7 +98,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -157,8 +157,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -206,7 +207,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -224,8 +225,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -273,7 +275,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None)
@@ -291,8 +293,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -399,8 +402,8 @@ async def create_or_update(
         :type workspace_name: str
         :param name: Container name. This is case-sensitive. Required.
         :type name: str
-        :param body: Container entity to create or update. Is either a model type or a IO type.
-         Required.
+        :param body: Container entity to create or update. Is either a ModelContainer type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.ModelContainer or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -421,7 +424,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -451,8 +454,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_versions_operations.py
index 68a4fd3be570..2d8a8d63088b 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_versions_operations.py
@@ -124,7 +124,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -191,8 +191,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -242,7 +243,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -261,8 +262,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -312,7 +314,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelVersion] = kwargs.pop("cls", None)
@@ -331,8 +333,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -448,7 +451,8 @@ async def create_or_update(
         :type name: str
         :param version: Version identifier. This is case-sensitive. Required.
         :type version: str
-        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :param body: Version entity to create or update. Is either a ModelVersion type or a IO type.
+         Required.
         :type body: ~azure.mgmt.machinelearningservices.models.ModelVersion or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -469,7 +473,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -500,8 +504,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_deployments_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_deployments_operations.py
index f78323104bda..2e94e4b5f1e3 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_deployments_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_deployments_operations.py
@@ -105,7 +105,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineDeploymentTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -165,8 +165,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -197,7 +198,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -216,8 +217,9 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -274,7 +276,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -351,7 +353,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineDeployment] = kwargs.pop("cls", None)
@@ -370,8 +372,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -412,7 +415,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -443,8 +446,9 @@ async def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -588,8 +592,8 @@ async def begin_update(
         :type endpoint_name: str
         :param deployment_name: Inference Endpoint Deployment name. Required.
         :type deployment_name: str
-        :param body: Online Endpoint entity to apply during operation. Is either a model type or a IO
-         type. Required.
+        :param body: Online Endpoint entity to apply during operation. Is either a
+         PartialMinimalTrackedResourceWithSku type or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithSku or
          IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -612,7 +616,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -681,7 +685,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -712,8 +716,9 @@ async def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -859,8 +864,8 @@ async def begin_create_or_update(
         :type endpoint_name: str
         :param deployment_name: Inference Endpoint Deployment name. Required.
         :type deployment_name: str
-        :param body: Inference Endpoint entity to apply during operation. Is either a model type or a
-         IO type. Required.
+        :param body: Inference Endpoint entity to apply during operation. Is either a OnlineDeployment
+         type or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.OnlineDeployment or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -882,7 +887,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1026,8 +1031,8 @@ async def get_logs(
         :type endpoint_name: str
         :param deployment_name: The name and identifier for the endpoint. Required.
         :type deployment_name: str
-        :param body: The request containing parameters for retrieving logs. Is either a model type or a
-         IO type. Required.
+        :param body: The request containing parameters for retrieving logs. Is either a
+         DeploymentLogsRequest type or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.DeploymentLogsRequest or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1048,7 +1053,7 @@ async def get_logs(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1079,8 +1084,9 @@ async def get_logs(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1138,7 +1144,7 @@ def list_skus(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.SkuResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -1198,8 +1204,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_endpoints_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_endpoints_operations.py
index bb9f50e9e7b8..d54f7bf96e02 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_endpoints_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_endpoints_operations.py
@@ -121,7 +121,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineEndpointTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -184,8 +184,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -216,7 +217,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -234,8 +235,9 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -290,7 +292,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -364,7 +366,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineEndpoint] = kwargs.pop("cls", None)
@@ -382,8 +384,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -423,7 +426,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -453,8 +456,9 @@ async def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -590,8 +594,8 @@ async def begin_update(
         :type workspace_name: str
         :param endpoint_name: Online Endpoint name. Required.
         :type endpoint_name: str
-        :param body: Online Endpoint entity to apply during operation. Is either a model type or a IO
-         type. Required.
+        :param body: Online Endpoint entity to apply during operation. Is either a
+         PartialMinimalTrackedResourceWithIdentity type or a IO type. Required.
         :type body:
          ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithIdentity or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -614,7 +618,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -681,7 +685,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -711,8 +715,9 @@ async def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -849,8 +854,8 @@ async def begin_create_or_update(
         :type workspace_name: str
         :param endpoint_name: Online Endpoint name. Required.
         :type endpoint_name: str
-        :param body: Online Endpoint entity to apply during operation. Is either a model type or a IO
-         type. Required.
+        :param body: Online Endpoint entity to apply during operation. Is either a OnlineEndpoint type
+         or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.OnlineEndpoint or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -872,7 +877,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -951,7 +956,7 @@ async def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None)
@@ -969,8 +974,9 @@ async def list_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1010,7 +1016,7 @@ async def _regenerate_keys_initial(  # pylint: disable=inconsistent-return-state
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1040,8 +1046,9 @@ async def _regenerate_keys_initial(  # pylint: disable=inconsistent-return-state
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1163,7 +1170,8 @@ async def begin_regenerate_keys(
         :type workspace_name: str
         :param endpoint_name: Online Endpoint name. Required.
         :type endpoint_name: str
-        :param body: RegenerateKeys request . Is either a model type or a IO type. Required.
+        :param body: RegenerateKeys request . Is either a RegenerateEndpointKeysRequest type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.RegenerateEndpointKeysRequest or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1183,7 +1191,7 @@ async def begin_regenerate_keys(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1262,7 +1270,7 @@ async def get_token(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthToken] = kwargs.pop("cls", None)
@@ -1280,8 +1288,9 @@ async def get_token(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_operations.py
index ab8aec812cd0..7ca4d00fc24b 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_operations.py
@@ -70,7 +70,7 @@ def list(self, **kwargs: Any) -> AsyncIterable["_models.AmlOperation"]:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.AmlOperationListResult] = kwargs.pop("cls", None)
@@ -123,8 +123,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_endpoint_connections_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_endpoint_connections_operations.py
index ca1d84b13c3e..f2f00cfe5117 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_endpoint_connections_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_endpoint_connections_operations.py
@@ -84,7 +84,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateEndpointConnectionListResult] = kwargs.pop("cls", None)
@@ -140,8 +140,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -188,7 +189,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateEndpointConnection] = kwargs.pop("cls", None)
@@ -206,8 +207,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -311,8 +313,8 @@ async def create_or_update(
         :param private_endpoint_connection_name: The name of the private endpoint connection associated
          with the workspace. Required.
         :type private_endpoint_connection_name: str
-        :param properties: The private endpoint connection properties. Is either a model type or a IO
-         type. Required.
+        :param properties: The private endpoint connection properties. Is either a
+         PrivateEndpointConnection type or a IO type. Required.
         :type properties: ~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -333,7 +335,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -363,8 +365,9 @@ async def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -415,7 +418,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -433,8 +436,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_link_resources_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_link_resources_operations.py
index 8c30d300602a..fcdf8eeb0683 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_link_resources_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_link_resources_operations.py
@@ -82,7 +82,7 @@ async def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateLinkResourceListResult] = kwargs.pop("cls", None)
@@ -99,8 +99,9 @@ async def list(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_quotas_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_quotas_operations.py
index b101e920473f..5a9bea85b26b 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_quotas_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_quotas_operations.py
@@ -109,7 +109,8 @@ async def update(
 
         :param location: The location for update quota is queried. Required.
         :type location: str
-        :param parameters: Quota update parameters. Is either a model type or a IO type. Required.
+        :param parameters: Quota update parameters. Is either a QuotaUpdateParameters type or a IO
+         type. Required.
         :type parameters: ~azure.mgmt.machinelearningservices.models.QuotaUpdateParameters or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -130,7 +131,7 @@ async def update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -158,8 +159,9 @@ async def update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -195,7 +197,7 @@ def list(self, location: str, **kwargs: Any) -> AsyncIterable["_models.ResourceQ
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListWorkspaceQuotas] = kwargs.pop("cls", None)
@@ -250,8 +252,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registries_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registries_operations.py
new file mode 100644
index 000000000000..0a27aa7176ad
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registries_operations.py
@@ -0,0 +1,926 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._registries_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_get_request,
+    build_list_by_subscription_request,
+    build_list_request,
+    build_update_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class RegistriesOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`registries` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list_by_subscription(self, skip: Optional[str] = None, **kwargs: Any) -> AsyncIterable["_models.Registry"]:
+        """List registries by subscription.
+
+        List registries by subscription.
+
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either Registry or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.RegistryTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_by_subscription_request(
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list_by_subscription.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("RegistryTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            _stream = False
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=_stream, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list_by_subscription.metadata = {
+        "url": "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/registries"
+    }
+
+    @distributed_trace
+    def list(
+        self, resource_group_name: str, skip: Optional[str] = None, **kwargs: Any
+    ) -> AsyncIterable["_models.Registry"]:
+        """List registries.
+
+        List registries.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either Registry or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.RegistryTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("RegistryTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            _stream = False
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=_stream, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries"
+    }
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        _stream = False
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=_stream, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @distributed_trace_async
+    async def begin_delete(self, resource_group_name: str, registry_name: str, **kwargs: Any) -> AsyncLROPoller[None]:
+        """Delete registry.
+
+        Delete registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @distributed_trace_async
+    async def get(self, resource_group_name: str, registry_name: str, **kwargs: Any) -> _models.Registry:
+        """Get registry.
+
+        Get registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Registry or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Registry
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        _stream = False
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=_stream, **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("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    async def _update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: Union[_models.PartialRegistryPartialTrackedResource, IO],
+        **kwargs: Any
+    ) -> _models.Registry:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "PartialRegistryPartialTrackedResource")
+
+        request = build_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        _stream = False
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=_stream, **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)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if response.status_code == 202:
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @overload
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: _models.PartialRegistryPartialTrackedResource,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.PartialRegistryPartialTrackedResource
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: Union[_models.PartialRegistryPartialTrackedResource, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Is either a
+         PartialRegistryPartialTrackedResource type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.PartialRegistryPartialTrackedResource or
+         IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("Registry", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    async def _create_or_update_initial(
+        self, resource_group_name: str, registry_name: str, body: Union[_models.Registry, IO], **kwargs: Any
+    ) -> Optional[_models.Registry]:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[Optional[_models.Registry]] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "Registry")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        _stream = False
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=_stream, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201, 202]:
+            map_error(status_code=response.status_code, 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 = None
+        if response.status_code == 200:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: _models.Registry,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.Registry
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self, resource_group_name: str, registry_name: str, body: Union[_models.Registry, IO], **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Is either a Registry type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.Registry or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("Registry", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod,
+                AsyncARMPolling(lro_delay, lro_options={"final-state-via": "azure-async-operation"}, **kwargs),
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_schedules_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_schedules_operations.py
index 7e2d8fa6ae15..c4cb4777159c 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_schedules_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_schedules_operations.py
@@ -97,7 +97,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ScheduleResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -155,8 +155,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -187,7 +188,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -205,8 +206,9 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -261,7 +263,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -333,7 +335,7 @@ async def get(self, resource_group_name: str, workspace_name: str, name: str, **
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Schedule] = kwargs.pop("cls", None)
@@ -351,8 +353,9 @@ async def get(self, resource_group_name: str, workspace_name: str, name: str, **
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -387,7 +390,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -417,8 +420,9 @@ async def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -548,7 +552,7 @@ async def begin_create_or_update(
         :type workspace_name: str
         :param name: Schedule name. Required.
         :type name: str
-        :param body: Schedule definition. Is either a model type or a IO type. Required.
+        :param body: Schedule definition. Is either a Schedule type or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.Schedule or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -569,7 +573,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_usages_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_usages_operations.py
index f7db5ff755cf..04528ced57a8 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_usages_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_usages_operations.py
@@ -73,7 +73,7 @@ def list(self, location: str, **kwargs: Any) -> AsyncIterable["_models.Usage"]:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListUsagesResult] = kwargs.pop("cls", None)
@@ -128,8 +128,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_virtual_machine_sizes_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_virtual_machine_sizes_operations.py
index e11949599426..2eb8d1ed3744 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_virtual_machine_sizes_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_virtual_machine_sizes_operations.py
@@ -77,7 +77,7 @@ async def list(self, location: str, **kwargs: Any) -> _models.VirtualMachineSize
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.VirtualMachineSizeListResult] = kwargs.pop("cls", None)
@@ -93,8 +93,9 @@ async def list(self, location: str, **kwargs: Any) -> _models.VirtualMachineSize
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_connections_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_connections_operations.py
index d849184954e4..d153e4a9399a 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_connections_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_connections_operations.py
@@ -145,7 +145,7 @@ async def create(
         :param connection_name: Friendly name of the workspace connection. Required.
         :type connection_name: str
         :param parameters: The object for creating or updating a new workspace connection. Is either a
-         model type or a IO type. Required.
+         WorkspaceConnectionPropertiesV2BasicResource type or a IO type. Required.
         :type parameters:
          ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -167,7 +167,7 @@ async def create(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -197,8 +197,9 @@ async def create(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -248,7 +249,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceConnectionPropertiesV2BasicResource] = kwargs.pop("cls", None)
@@ -266,8 +267,9 @@ async def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -317,7 +319,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -335,8 +337,9 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -383,7 +386,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -443,8 +446,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_features_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_features_operations.py
index a968302c5446..e7f1c51a1545 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_features_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_features_operations.py
@@ -77,7 +77,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListAmlUserFeatureResult] = kwargs.pop("cls", None)
@@ -133,8 +133,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspaces_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspaces_operations.py
index 5c6cf54fea4d..23b45b601607 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspaces_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspaces_operations.py
@@ -100,7 +100,7 @@ async def get(self, resource_group_name: str, workspace_name: str, **kwargs: Any
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Workspace] = kwargs.pop("cls", None)
@@ -117,8 +117,9 @@ async def get(self, resource_group_name: str, workspace_name: str, **kwargs: Any
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -153,7 +154,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -182,8 +183,9 @@ async def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -294,7 +296,7 @@ async def begin_create_or_update(
         :param workspace_name: Name of Azure Machine Learning workspace. Required.
         :type workspace_name: str
         :param parameters: The parameters for creating or updating a machine learning workspace. Is
-         either a model type or a IO type. Required.
+         either a Workspace type or a IO type. Required.
         :type parameters: ~azure.mgmt.machinelearningservices.models.Workspace or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -316,7 +318,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -377,7 +379,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -394,8 +396,9 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -436,7 +439,7 @@ async def begin_delete(self, resource_group_name: str, workspace_name: str, **kw
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -496,7 +499,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -525,8 +528,9 @@ async def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -638,8 +642,8 @@ async def begin_update(
         :type resource_group_name: str
         :param workspace_name: Name of Azure Machine Learning workspace. Required.
         :type workspace_name: str
-        :param parameters: The parameters for updating a machine learning workspace. Is either a model
-         type or a IO type. Required.
+        :param parameters: The parameters for updating a machine learning workspace. Is either a
+         WorkspaceUpdateParameters type or a IO type. Required.
         :type parameters: ~azure.mgmt.machinelearningservices.models.WorkspaceUpdateParameters or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -661,7 +665,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -728,7 +732,7 @@ def list_by_resource_group(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceListResult] = kwargs.pop("cls", None)
@@ -784,8 +788,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -820,7 +825,7 @@ async def _diagnose_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -852,8 +857,9 @@ async def _diagnose_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -976,8 +982,8 @@ async def begin_diagnose(
         :type resource_group_name: str
         :param workspace_name: Name of Azure Machine Learning workspace. Required.
         :type workspace_name: str
-        :param parameters: The parameter of diagnosing workspace health. Is either a model type or a IO
-         type. Default value is None.
+        :param parameters: The parameter of diagnosing workspace health. Is either a
+         DiagnoseWorkspaceParameters type or a IO type. Default value is None.
         :type parameters: ~azure.mgmt.machinelearningservices.models.DiagnoseWorkspaceParameters or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -999,7 +1005,7 @@ async def begin_diagnose(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1076,7 +1082,7 @@ async def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListWorkspaceKeysResult] = kwargs.pop("cls", None)
@@ -1093,8 +1099,9 @@ async def list_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1129,7 +1136,7 @@ async def _resync_keys_initial(  # pylint: disable=inconsistent-return-statement
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1146,8 +1153,9 @@ async def _resync_keys_initial(  # pylint: disable=inconsistent-return-statement
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1191,7 +1199,7 @@ async def begin_resync_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1248,7 +1256,7 @@ def list_by_subscription(self, skip: Optional[str] = None, **kwargs: Any) -> Asy
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceListResult] = kwargs.pop("cls", None)
@@ -1303,8 +1311,9 @@ async def extract_data(pipeline_response):
         async def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -1348,7 +1357,7 @@ async def list_notebook_access_token(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.NotebookAccessTokenResult] = kwargs.pop("cls", None)
@@ -1365,8 +1374,9 @@ async def list_notebook_access_token(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1401,7 +1411,7 @@ async def _prepare_notebook_initial(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[Optional[_models.NotebookResourceInfo]] = kwargs.pop("cls", None)
@@ -1418,8 +1428,9 @@ async def _prepare_notebook_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1470,7 +1481,7 @@ async def begin_prepare_notebook(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.NotebookResourceInfo] = kwargs.pop("cls", None)
@@ -1543,7 +1554,7 @@ async def list_storage_account_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListStorageAccountKeysResult] = kwargs.pop("cls", None)
@@ -1560,8 +1571,9 @@ async def list_storage_account_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1609,7 +1621,7 @@ async def list_notebook_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListNotebookKeysResult] = kwargs.pop("cls", None)
@@ -1626,8 +1638,9 @@ async def list_notebook_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1679,7 +1692,7 @@ async def list_outbound_network_dependencies_endpoints(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ExternalFQDNResponse] = kwargs.pop("cls", None)
@@ -1696,8 +1709,9 @@ async def list_outbound_network_dependencies_endpoints(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/__init__.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/__init__.py
index 72bfcc9bc3c2..db2b564ff1f6 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/__init__.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/__init__.py
@@ -11,6 +11,7 @@
 from ._models_py3 import AKSSchemaProperties
 from ._models_py3 import AccountKeyDatastoreCredentials
 from ._models_py3 import AccountKeyDatastoreSecrets
+from ._models_py3 import AcrDetails
 from ._models_py3 import AksComputeSecrets
 from ._models_py3 import AksComputeSecretsProperties
 from ._models_py3 import AksNetworkingConfiguration
@@ -24,6 +25,7 @@
 from ._models_py3 import AmlOperationListResult
 from ._models_py3 import AmlToken
 from ._models_py3 import AmlUserFeature
+from ._models_py3 import ArmResourceId
 from ._models_py3 import AssetBase
 from ._models_py3 import AssetContainer
 from ._models_py3 import AssetJobInput
@@ -97,6 +99,7 @@
 from ._models_py3 import ContainerResourceRequirements
 from ._models_py3 import ContainerResourceSettings
 from ._models_py3 import CosmosDbSettings
+from ._models_py3 import Cron
 from ._models_py3 import CronTrigger
 from ._models_py3 import CustomForecastHorizon
 from ._models_py3 import CustomModelJobInput
@@ -256,6 +259,7 @@
 from ._models_py3 import PartialMinimalTrackedResource
 from ._models_py3 import PartialMinimalTrackedResourceWithIdentity
 from ._models_py3 import PartialMinimalTrackedResourceWithSku
+from ._models_py3 import PartialRegistryPartialTrackedResource
 from ._models_py3 import PartialSku
 from ._models_py3 import Password
 from ._models_py3 import PersonalComputeInstanceSettings
@@ -271,10 +275,15 @@
 from ._models_py3 import QuotaBaseProperties
 from ._models_py3 import QuotaUpdateParameters
 from ._models_py3 import RandomSamplingAlgorithm
+from ._models_py3 import Recurrence
 from ._models_py3 import RecurrenceSchedule
 from ._models_py3 import RecurrenceTrigger
 from ._models_py3 import RegenerateEndpointKeysRequest
+from ._models_py3 import Registry
 from ._models_py3 import RegistryListCredentialsResult
+from ._models_py3 import RegistryProperties
+from ._models_py3 import RegistryRegionArmDetails
+from ._models_py3 import RegistryTrackedResourceArmPaginatedResult
 from ._models_py3 import Regression
 from ._models_py3 import RegressionTrainingSettings
 from ._models_py3 import Resource
@@ -310,10 +319,13 @@
 from ._models_py3 import SkuSetting
 from ._models_py3 import SslConfiguration
 from ._models_py3 import StackEnsembleSettings
+from ._models_py3 import StorageAccountDetails
 from ._models_py3 import SweepJob
 from ._models_py3 import SweepJobLimits
 from ._models_py3 import SynapseSpark
 from ._models_py3 import SynapseSparkProperties
+from ._models_py3 import SystemCreatedAcrAccount
+from ._models_py3 import SystemCreatedStorageAccount
 from ._models_py3 import SystemData
 from ._models_py3 import SystemService
 from ._models_py3 import TableVertical
@@ -345,6 +357,8 @@
 from ._models_py3 import UsageName
 from ._models_py3 import UserAccountCredentials
 from ._models_py3 import UserAssignedIdentity
+from ._models_py3 import UserCreatedAcrAccount
+from ._models_py3 import UserCreatedStorageAccount
 from ._models_py3 import UserIdentity
 from ._models_py3 import UsernamePasswordAuthTypeWorkspaceConnectionProperties
 from ._models_py3 import VirtualMachine
@@ -493,6 +507,7 @@
     "AKSSchemaProperties",
     "AccountKeyDatastoreCredentials",
     "AccountKeyDatastoreSecrets",
+    "AcrDetails",
     "AksComputeSecrets",
     "AksComputeSecretsProperties",
     "AksNetworkingConfiguration",
@@ -506,6 +521,7 @@
     "AmlOperationListResult",
     "AmlToken",
     "AmlUserFeature",
+    "ArmResourceId",
     "AssetBase",
     "AssetContainer",
     "AssetJobInput",
@@ -579,6 +595,7 @@
     "ContainerResourceRequirements",
     "ContainerResourceSettings",
     "CosmosDbSettings",
+    "Cron",
     "CronTrigger",
     "CustomForecastHorizon",
     "CustomModelJobInput",
@@ -738,6 +755,7 @@
     "PartialMinimalTrackedResource",
     "PartialMinimalTrackedResourceWithIdentity",
     "PartialMinimalTrackedResourceWithSku",
+    "PartialRegistryPartialTrackedResource",
     "PartialSku",
     "Password",
     "PersonalComputeInstanceSettings",
@@ -753,10 +771,15 @@
     "QuotaBaseProperties",
     "QuotaUpdateParameters",
     "RandomSamplingAlgorithm",
+    "Recurrence",
     "RecurrenceSchedule",
     "RecurrenceTrigger",
     "RegenerateEndpointKeysRequest",
+    "Registry",
     "RegistryListCredentialsResult",
+    "RegistryProperties",
+    "RegistryRegionArmDetails",
+    "RegistryTrackedResourceArmPaginatedResult",
     "Regression",
     "RegressionTrainingSettings",
     "Resource",
@@ -792,10 +815,13 @@
     "SkuSetting",
     "SslConfiguration",
     "StackEnsembleSettings",
+    "StorageAccountDetails",
     "SweepJob",
     "SweepJobLimits",
     "SynapseSpark",
     "SynapseSparkProperties",
+    "SystemCreatedAcrAccount",
+    "SystemCreatedStorageAccount",
     "SystemData",
     "SystemService",
     "TableVertical",
@@ -827,6 +853,8 @@
     "UsageName",
     "UserAccountCredentials",
     "UserAssignedIdentity",
+    "UserCreatedAcrAccount",
+    "UserCreatedStorageAccount",
     "UserIdentity",
     "UsernamePasswordAuthTypeWorkspaceConnectionProperties",
     "VirtualMachine",
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_machine_learning_services_mgmt_client_enums.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_machine_learning_services_mgmt_client_enums.py
index 640b9b12a650..fc5401504593 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_machine_learning_services_mgmt_client_enums.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_machine_learning_services_mgmt_client_enums.py
@@ -74,30 +74,30 @@ class BillingCurrency(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class BlockedTransformers(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum for all classification models supported by AutoML."""
 
-    #: Target encoding for text data.
     TEXT_TARGET_ENCODER = "TextTargetEncoder"
-    #: Ohe hot encoding creates a binary feature transformation.
+    """Target encoding for text data."""
     ONE_HOT_ENCODER = "OneHotEncoder"
-    #: Target encoding for categorical data.
+    """Ohe hot encoding creates a binary feature transformation."""
     CAT_TARGET_ENCODER = "CatTargetEncoder"
-    #: Tf-Idf stands for, term-frequency times inverse document-frequency. This is a common term
-    #: weighting scheme for identifying information from documents.
+    """Target encoding for categorical data."""
     TF_IDF = "TfIdf"
-    #: Weight of Evidence encoding is a technique used to encode categorical variables. It uses the
-    #: natural log of the P(1)/P(0) to create weights.
+    """Tf-Idf stands for, term-frequency times inverse document-frequency. This is a common term
+    #: weighting scheme for identifying information from documents."""
     WO_E_TARGET_ENCODER = "WoETargetEncoder"
-    #: Label encoder converts labels/categorical variables in a numerical form.
+    """Weight of Evidence encoding is a technique used to encode categorical variables. It uses the
+    #: natural log of the P(1)/P(0) to create weights."""
     LABEL_ENCODER = "LabelEncoder"
-    #: Word embedding helps represents words or phrases as a vector, or a series of numbers.
+    """Label encoder converts labels/categorical variables in a numerical form."""
     WORD_EMBEDDING = "WordEmbedding"
-    #: Naive Bayes is a classified that is used for classification of discrete features that are
-    #: categorically distributed.
+    """Word embedding helps represents words or phrases as a vector, or a series of numbers."""
     NAIVE_BAYES = "NaiveBayes"
-    #: Count Vectorizer converts a collection of text documents to a matrix of token counts.
+    """Naive Bayes is a classified that is used for classification of discrete features that are
+    #: categorically distributed."""
     COUNT_VECTORIZER = "CountVectorizer"
-    #: Hashing One Hot Encoder can turn categorical variables into a limited number of new features.
-    #: This is often used for high-cardinality categorical features.
+    """Count Vectorizer converts a collection of text documents to a matrix of token counts."""
     HASH_ONE_HOT_ENCODER = "HashOneHotEncoder"
+    """Hashing One Hot Encoder can turn categorical variables into a limited number of new features.
+    #: This is often used for high-cardinality categorical features."""
 
 
 class Caching(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -111,107 +111,107 @@ class Caching(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class ClassificationModels(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum for all classification models supported by AutoML."""
 
-    #: Logistic regression is a fundamental classification technique.
+    LOGISTIC_REGRESSION = "LogisticRegression"
+    """Logistic regression is a fundamental classification technique.
     #: It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear
     #: regression.
     #: Logistic regression is fast and relatively uncomplicated, and it's convenient for you to
     #: interpret the results.
     #: Although it's essentially a method for binary classification, it can also be applied to
-    #: multiclass problems.
-    LOGISTIC_REGRESSION = "LogisticRegression"
-    #: SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
+    #: multiclass problems."""
+    SGD = "SGD"
+    """SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
     #: applications
     #: to find the model parameters that correspond to the best fit between predicted and actual
-    #: outputs.
-    SGD = "SGD"
-    #: The multinomial Naive Bayes classifier is suitable for classification with discrete features
+    #: outputs."""
+    MULTINOMIAL_NAIVE_BAYES = "MultinomialNaiveBayes"
+    """The multinomial Naive Bayes classifier is suitable for classification with discrete features
     #: (e.g., word counts for text classification).
     #: The multinomial distribution normally requires integer feature counts. However, in practice,
-    #: fractional counts such as tf-idf may also work.
-    MULTINOMIAL_NAIVE_BAYES = "MultinomialNaiveBayes"
-    #: Naive Bayes classifier for multivariate Bernoulli models.
+    #: fractional counts such as tf-idf may also work."""
     BERNOULLI_NAIVE_BAYES = "BernoulliNaiveBayes"
-    #: A support vector machine (SVM) is a supervised machine learning model that uses classification
+    """Naive Bayes classifier for multivariate Bernoulli models."""
+    SVM = "SVM"
+    """A support vector machine (SVM) is a supervised machine learning model that uses classification
     #: algorithms for two-group classification problems.
     #: After giving an SVM model sets of labeled training data for each category, they're able to
-    #: categorize new text.
-    SVM = "SVM"
-    #: A support vector machine (SVM) is a supervised machine learning model that uses classification
+    #: categorize new text."""
+    LINEAR_SVM = "LinearSVM"
+    """A support vector machine (SVM) is a supervised machine learning model that uses classification
     #: algorithms for two-group classification problems.
     #: After giving an SVM model sets of labeled training data for each category, they're able to
     #: categorize new text.
     #: Linear SVM performs best when input data is linear, i.e., data can be easily classified by
-    #: drawing the straight line between classified values on a plotted graph.
-    LINEAR_SVM = "LinearSVM"
-    #: K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
+    #: drawing the straight line between classified values on a plotted graph."""
+    KNN = "KNN"
+    """K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
     #: datapoints
     #: which further means that the new data point will be assigned a value based on how closely it
-    #: matches the points in the training set.
-    KNN = "KNN"
-    #: Decision Trees are a non-parametric supervised learning method used for both classification and
+    #: matches the points in the training set."""
+    DECISION_TREE = "DecisionTree"
+    """Decision Trees are a non-parametric supervised learning method used for both classification and
     #: regression tasks.
     #: The goal is to create a model that predicts the value of a target variable by learning simple
-    #: decision rules inferred from the data features.
-    DECISION_TREE = "DecisionTree"
-    #: Random forest is a supervised learning algorithm.
-    #: The "forest"\  it builds, is an ensemble of decision trees, usually trained with the “bagging”\
+    #: decision rules inferred from the data features."""
+    RANDOM_FOREST = "RandomForest"
+    """Random forest is a supervised learning algorithm.
+    #: The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging”
     #: method.
     #: The general idea of the bagging method is that a combination of learning models increases the
-    #: overall result.
-    RANDOM_FOREST = "RandomForest"
-    #: Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
-    #: decision trees. It is related to the widely used random forest algorithm.
+    #: overall result."""
     EXTREME_RANDOM_TREES = "ExtremeRandomTrees"
-    #: LightGBM is a gradient boosting framework that uses tree based learning algorithms.
+    """Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
+    #: decision trees. It is related to the widely used random forest algorithm."""
     LIGHT_GBM = "LightGBM"
-    #: The technique of transiting week learners into a strong learner is called Boosting. The
-    #: gradient boosting algorithm process works on this theory of execution.
+    """LightGBM is a gradient boosting framework that uses tree based learning algorithms."""
     GRADIENT_BOOSTING = "GradientBoosting"
-    #: XGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where
-    #: target column values can be divided into distinct class values.
+    """The technique of transiting week learners into a strong learner is called Boosting. The
+    #: gradient boosting algorithm process works on this theory of execution."""
     XG_BOOST_CLASSIFIER = "XGBoostClassifier"
+    """XGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where
+    #: target column values can be divided into distinct class values."""
 
 
 class ClassificationMultilabelPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for classification multilabel tasks."""
 
-    #: AUC is the Area under the curve.
-    #: This metric represents arithmetic mean of the score for each class,
-    #: weighted by the number of true instances in each class.
     AUC_WEIGHTED = "AUCWeighted"
-    #: Accuracy is the ratio of predictions that exactly match the true class labels.
+    """AUC is the Area under the curve.
+    #: This metric represents arithmetic mean of the score for each class,
+    #: weighted by the number of true instances in each class."""
     ACCURACY = "Accuracy"
-    #: Normalized macro recall is recall macro-averaged and normalized, so that random
-    #: performance has a score of 0, and perfect performance has a score of 1.
+    """Accuracy is the ratio of predictions that exactly match the true class labels."""
     NORM_MACRO_RECALL = "NormMacroRecall"
-    #: The arithmetic mean of the average precision score for each class, weighted by
-    #: the number of true instances in each class.
+    """Normalized macro recall is recall macro-averaged and normalized, so that random
+    #: performance has a score of 0, and perfect performance has a score of 1."""
     AVERAGE_PRECISION_SCORE_WEIGHTED = "AveragePrecisionScoreWeighted"
-    #: The arithmetic mean of precision for each class, weighted by number of true instances in each
-    #: class.
+    """The arithmetic mean of the average precision score for each class, weighted by
+    #: the number of true instances in each class."""
     PRECISION_SCORE_WEIGHTED = "PrecisionScoreWeighted"
-    #: Intersection Over Union. Intersection of predictions divided by union of predictions.
+    """The arithmetic mean of precision for each class, weighted by number of true instances in each
+    #: class."""
     IOU = "IOU"
+    """Intersection Over Union. Intersection of predictions divided by union of predictions."""
 
 
 class ClassificationPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for classification tasks."""
 
-    #: AUC is the Area under the curve.
-    #: This metric represents arithmetic mean of the score for each class,
-    #: weighted by the number of true instances in each class.
     AUC_WEIGHTED = "AUCWeighted"
-    #: Accuracy is the ratio of predictions that exactly match the true class labels.
+    """AUC is the Area under the curve.
+    #: This metric represents arithmetic mean of the score for each class,
+    #: weighted by the number of true instances in each class."""
     ACCURACY = "Accuracy"
-    #: Normalized macro recall is recall macro-averaged and normalized, so that random
-    #: performance has a score of 0, and perfect performance has a score of 1.
+    """Accuracy is the ratio of predictions that exactly match the true class labels."""
     NORM_MACRO_RECALL = "NormMacroRecall"
-    #: The arithmetic mean of the average precision score for each class, weighted by
-    #: the number of true instances in each class.
+    """Normalized macro recall is recall macro-averaged and normalized, so that random
+    #: performance has a score of 0, and perfect performance has a score of 1."""
     AVERAGE_PRECISION_SCORE_WEIGHTED = "AveragePrecisionScoreWeighted"
-    #: The arithmetic mean of precision for each class, weighted by number of true instances in each
-    #: class.
+    """The arithmetic mean of the average precision score for each class, weighted by
+    #: the number of true instances in each class."""
     PRECISION_SCORE_WEIGHTED = "PrecisionScoreWeighted"
+    """The arithmetic mean of precision for each class, weighted by number of true instances in each
+    #: class."""
 
 
 class ClusterPurpose(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -420,125 +420,125 @@ class EnvironmentType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class FeatureLags(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Flag for generating lags for the numeric features."""
 
-    #: No feature lags generated.
     NONE = "None"
-    #: System auto-generates feature lags.
+    """No feature lags generated."""
     AUTO = "Auto"
+    """System auto-generates feature lags."""
 
 
 class FeaturizationMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Featurization mode - determines data featurization mode."""
 
-    #: Auto mode, system performs featurization without any custom featurization inputs.
     AUTO = "Auto"
-    #: Custom featurization.
+    """Auto mode, system performs featurization without any custom featurization inputs."""
     CUSTOM = "Custom"
-    #: Featurization off. 'Forecasting' task cannot use this value.
+    """Custom featurization."""
     OFF = "Off"
+    """Featurization off. 'Forecasting' task cannot use this value."""
 
 
 class ForecastHorizonMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum to determine forecast horizon selection mode."""
 
-    #: Forecast horizon to be determined automatically.
     AUTO = "Auto"
-    #: Use the custom forecast horizon.
+    """Forecast horizon to be determined automatically."""
     CUSTOM = "Custom"
+    """Use the custom forecast horizon."""
 
 
 class ForecastingModels(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum for all forecasting models supported by AutoML."""
 
-    #: Auto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and
+    AUTO_ARIMA = "AutoArima"
+    """Auto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and
     #: statistical analysis to interpret the data and make future predictions.
     #: This model aims to explain data by using time series data on its past values and uses linear
-    #: regression to make predictions.
-    AUTO_ARIMA = "AutoArima"
-    #: Prophet is a procedure for forecasting time series data based on an additive model where
+    #: regression to make predictions."""
+    PROPHET = "Prophet"
+    """Prophet is a procedure for forecasting time series data based on an additive model where
     #: non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
     #: It works best with time series that have strong seasonal effects and several seasons of
     #: historical data. Prophet is robust to missing data and shifts in the trend, and typically
-    #: handles outliers well.
-    PROPHET = "Prophet"
-    #: The Naive forecasting model makes predictions by carrying forward the latest target value for
-    #: each time-series in the training data.
+    #: handles outliers well."""
     NAIVE = "Naive"
-    #: The Seasonal Naive forecasting model makes predictions by carrying forward the latest season of
-    #: target values for each time-series in the training data.
+    """The Naive forecasting model makes predictions by carrying forward the latest target value for
+    #: each time-series in the training data."""
     SEASONAL_NAIVE = "SeasonalNaive"
-    #: The Average forecasting model makes predictions by carrying forward the average of the target
-    #: values for each time-series in the training data.
+    """The Seasonal Naive forecasting model makes predictions by carrying forward the latest season of
+    #: target values for each time-series in the training data."""
     AVERAGE = "Average"
-    #: The Seasonal Average forecasting model makes predictions by carrying forward the average value
-    #: of the latest season of data for each time-series in the training data.
+    """The Average forecasting model makes predictions by carrying forward the average of the target
+    #: values for each time-series in the training data."""
     SEASONAL_AVERAGE = "SeasonalAverage"
-    #: Exponential smoothing is a time series forecasting method for univariate data that can be
-    #: extended to support data with a systematic trend or seasonal component.
+    """The Seasonal Average forecasting model makes predictions by carrying forward the average value
+    #: of the latest season of data for each time-series in the training data."""
     EXPONENTIAL_SMOOTHING = "ExponentialSmoothing"
-    #: An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be
+    """Exponential smoothing is a time series forecasting method for univariate data that can be
+    #: extended to support data with a systematic trend or seasonal component."""
+    ARIMAX = "Arimax"
+    """An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be
     #: viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or
     #: more moving average (MA) terms.
     #: This method is suitable for forecasting when data is stationary/non stationary, and
-    #: multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.
-    ARIMAX = "Arimax"
-    #: TCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for
-    #: brief intro.
+    #: multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity."""
     TCN_FORECASTER = "TCNForecaster"
-    #: Elastic net is a popular type of regularized linear regression that combines two popular
-    #: penalties, specifically the L1 and L2 penalty functions.
+    """TCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for
+    #: brief intro."""
     ELASTIC_NET = "ElasticNet"
-    #: The technique of transiting week learners into a strong learner is called Boosting. The
-    #: gradient boosting algorithm process works on this theory of execution.
+    """Elastic net is a popular type of regularized linear regression that combines two popular
+    #: penalties, specifically the L1 and L2 penalty functions."""
     GRADIENT_BOOSTING = "GradientBoosting"
-    #: Decision Trees are a non-parametric supervised learning method used for both classification and
+    """The technique of transiting week learners into a strong learner is called Boosting. The
+    #: gradient boosting algorithm process works on this theory of execution."""
+    DECISION_TREE = "DecisionTree"
+    """Decision Trees are a non-parametric supervised learning method used for both classification and
     #: regression tasks.
     #: The goal is to create a model that predicts the value of a target variable by learning simple
-    #: decision rules inferred from the data features.
-    DECISION_TREE = "DecisionTree"
-    #: K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
+    #: decision rules inferred from the data features."""
+    KNN = "KNN"
+    """K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
     #: datapoints
     #: which further means that the new data point will be assigned a value based on how closely it
-    #: matches the points in the training set.
-    KNN = "KNN"
-    #: Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an
-    #: L1 prior as regularizer.
+    #: matches the points in the training set."""
     LASSO_LARS = "LassoLars"
-    #: SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
+    """Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an
+    #: L1 prior as regularizer."""
+    SGD = "SGD"
+    """SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
     #: applications
     #: to find the model parameters that correspond to the best fit between predicted and actual
     #: outputs.
-    #: It's an inexact but powerful technique.
-    SGD = "SGD"
-    #: Random forest is a supervised learning algorithm.
+    #: It's an inexact but powerful technique."""
+    RANDOM_FOREST = "RandomForest"
+    """Random forest is a supervised learning algorithm.
     #: The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging”
     #: method.
     #: The general idea of the bagging method is that a combination of learning models increases the
-    #: overall result.
-    RANDOM_FOREST = "RandomForest"
-    #: Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
-    #: decision trees. It is related to the widely used random forest algorithm.
+    #: overall result."""
     EXTREME_RANDOM_TREES = "ExtremeRandomTrees"
-    #: LightGBM is a gradient boosting framework that uses tree based learning algorithms.
+    """Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
+    #: decision trees. It is related to the widely used random forest algorithm."""
     LIGHT_GBM = "LightGBM"
-    #: XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model
-    #: using ensemble of base learners.
+    """LightGBM is a gradient boosting framework that uses tree based learning algorithms."""
     XG_BOOST_REGRESSOR = "XGBoostRegressor"
+    """XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model
+    #: using ensemble of base learners."""
 
 
 class ForecastingPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for Forecasting task."""
 
-    #: The Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.
     SPEARMAN_CORRELATION = "SpearmanCorrelation"
-    #: The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between
-    #: models with different scales.
+    """The Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation."""
     NORMALIZED_ROOT_MEAN_SQUARED_ERROR = "NormalizedRootMeanSquaredError"
-    #: The R2 score is one of the performance evaluation measures for forecasting-based machine
-    #: learning models.
+    """The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between
+    #: models with different scales."""
     R2_SCORE = "R2Score"
-    #: The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute
-    #: Error (MAE) of (time) series with different scales.
+    """The R2 score is one of the performance evaluation measures for forecasting-based machine
+    #: learning models."""
     NORMALIZED_MEAN_ABSOLUTE_ERROR = "NormalizedMeanAbsoluteError"
+    """The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute
+    #: Error (MAE) of (time) series with different scales."""
 
 
 class Goal(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -570,9 +570,9 @@ class InputDeliveryMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class InstanceSegmentationPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for InstanceSegmentation tasks."""
 
-    #: Mean Average Precision (MAP) is the average of AP (Average Precision).
-    #: AP is calculated for each class and averaged to get the MAP.
     MEAN_AVERAGE_PRECISION = "MeanAveragePrecision"
+    """Mean Average Precision (MAP) is the average of AP (Average Precision).
+    #: AP is calculated for each class and averaged to get the MAP."""
 
 
 class JobInputType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -608,39 +608,39 @@ class JobOutputType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class JobStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """The status of a job."""
 
-    #: Run hasn't started yet.
     NOT_STARTED = "NotStarted"
-    #: Run has started. The user has a run ID.
+    """Run hasn't started yet."""
     STARTING = "Starting"
-    #: (Not used currently) It will be used if ES is creating the compute target.
+    """Run has started. The user has a run ID."""
     PROVISIONING = "Provisioning"
-    #: The run environment is being prepared.
+    """(Not used currently) It will be used if ES is creating the compute target."""
     PREPARING = "Preparing"
-    #: The job is queued in the compute target. For example, in BatchAI the job is in queued state,
-    #: while waiting for all required nodes to be ready.
+    """The run environment is being prepared."""
     QUEUED = "Queued"
-    #: The job started to run in the compute target.
+    """The job is queued in the compute target. For example, in BatchAI the job is in queued state,
+    #: while waiting for all required nodes to be ready."""
     RUNNING = "Running"
-    #: Job is completed in the target. It is in output collection state now.
+    """The job started to run in the compute target."""
     FINALIZING = "Finalizing"
-    #: Cancellation has been requested for the job.
+    """Job is completed in the target. It is in output collection state now."""
     CANCEL_REQUESTED = "CancelRequested"
-    #: Job completed successfully. This reflects that both the job itself and output collection states
-    #: completed successfully
+    """Cancellation has been requested for the job."""
     COMPLETED = "Completed"
-    #: Job failed.
+    """Job completed successfully. This reflects that both the job itself and output collection states
+    #: completed successfully"""
     FAILED = "Failed"
-    #: Following cancellation request, the job is now successfully canceled.
+    """Job failed."""
     CANCELED = "Canceled"
-    #: When heartbeat is enabled, if the run isn't updating any information to RunHistory then the run
+    """Following cancellation request, the job is now successfully canceled."""
+    NOT_RESPONDING = "NotResponding"
+    """When heartbeat is enabled, if the run isn't updating any information to RunHistory then the run
     #: goes to NotResponding state.
     #: NotResponding is the only state that is exempt from strict transition orders. A run can go from
-    #: NotResponding to any of the previous states.
-    NOT_RESPONDING = "NotResponding"
-    #: The job is paused by users. Some adjustment to labeling jobs can be made only in paused state.
+    #: NotResponding to any of the previous states."""
     PAUSED = "Paused"
-    #: Default job status if not mapped to all other statuses
+    """The job is paused by users. Some adjustment to labeling jobs can be made only in paused state."""
     UNKNOWN = "Unknown"
+    """Default job status if not mapped to all other statuses"""
 
 
 class JobType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -662,12 +662,12 @@ class KeyType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class LearningRateScheduler(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Learning rate scheduler enum."""
 
-    #: No learning rate scheduler selected.
     NONE = "None"
-    #: Cosine Annealing With Warmup.
+    """No learning rate scheduler selected."""
     WARMUP_COSINE = "WarmupCosine"
-    #: Step learning rate scheduler.
+    """Cosine Annealing With Warmup."""
     STEP = "Step"
+    """Step learning rate scheduler."""
 
 
 class ListViewType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -688,18 +688,18 @@ class LoadBalancerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class LogVerbosity(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum for setting log verbosity."""
 
-    #: No logs emitted.
     NOT_SET = "NotSet"
-    #: Debug and above log statements logged.
+    """No logs emitted."""
     DEBUG = "Debug"
-    #: Info and above log statements logged.
+    """Debug and above log statements logged."""
     INFO = "Info"
-    #: Warning and above log statements logged.
+    """Info and above log statements logged."""
     WARNING = "Warning"
-    #: Error and above log statements logged.
+    """Warning and above log statements logged."""
     ERROR = "Error"
-    #: Only critical statements logged.
+    """Error and above log statements logged."""
     CRITICAL = "Critical"
+    """Only critical statements logged."""
 
 
 class ManagedServiceIdentityType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -716,16 +716,16 @@ class ManagedServiceIdentityType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class ModelSize(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Image model size."""
 
-    #: No value selected.
     NONE = "None"
-    #: Small size.
+    """No value selected."""
     SMALL = "Small"
-    #: Medium size.
+    """Small size."""
     MEDIUM = "Medium"
-    #: Large size.
+    """Medium size."""
     LARGE = "Large"
-    #: Extra large size.
+    """Large size."""
     EXTRA_LARGE = "ExtraLarge"
+    """Extra large size."""
 
 
 class MountAction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -749,11 +749,11 @@ class MountState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class NCrossValidationsMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Determines how N-Cross validations value is determined."""
 
-    #: Determine N-Cross validations value automatically. Supported only for 'Forecasting' AutoML
-    #: task.
     AUTO = "Auto"
-    #: Use custom N-Cross validations value.
+    """Determine N-Cross validations value automatically. Supported only for 'Forecasting' AutoML
+    #: task."""
     CUSTOM = "Custom"
+    """Use custom N-Cross validations value."""
 
 
 class Network(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -779,9 +779,9 @@ class NodeState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class ObjectDetectionPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for Image ObjectDetection task."""
 
-    #: Mean Average Precision (MAP) is the average of AP (Average Precision).
-    #: AP is calculated for each class and averaged to get the MAP.
     MEAN_AVERAGE_PRECISION = "MeanAveragePrecision"
+    """Mean Average Precision (MAP) is the average of AP (Average Precision).
+    #: AP is calculated for each class and averaged to get the MAP."""
 
 
 class OperatingSystemType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -917,16 +917,16 @@ class RandomSamplingAlgorithmRule(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class RecurrenceFrequency(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum to describe the frequency of a recurrence schedule."""
 
-    #: Minute frequency
     MINUTE = "Minute"
-    #: Hour frequency
+    """Minute frequency"""
     HOUR = "Hour"
-    #: Day frequency
+    """Hour frequency"""
     DAY = "Day"
-    #: Week frequency
+    """Day frequency"""
     WEEK = "Week"
-    #: Month frequency
+    """Week frequency"""
     MONTH = "Month"
+    """Month frequency"""
 
 
 class ReferenceType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -940,61 +940,61 @@ class ReferenceType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class RegressionModels(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum for all Regression models supported by AutoML."""
 
-    #: Elastic net is a popular type of regularized linear regression that combines two popular
-    #: penalties, specifically the L1 and L2 penalty functions.
     ELASTIC_NET = "ElasticNet"
-    #: The technique of transiting week learners into a strong learner is called Boosting. The
-    #: gradient boosting algorithm process works on this theory of execution.
+    """Elastic net is a popular type of regularized linear regression that combines two popular
+    #: penalties, specifically the L1 and L2 penalty functions."""
     GRADIENT_BOOSTING = "GradientBoosting"
-    #: Decision Trees are a non-parametric supervised learning method used for both classification and
+    """The technique of transiting week learners into a strong learner is called Boosting. The
+    #: gradient boosting algorithm process works on this theory of execution."""
+    DECISION_TREE = "DecisionTree"
+    """Decision Trees are a non-parametric supervised learning method used for both classification and
     #: regression tasks.
     #: The goal is to create a model that predicts the value of a target variable by learning simple
-    #: decision rules inferred from the data features.
-    DECISION_TREE = "DecisionTree"
-    #: K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
+    #: decision rules inferred from the data features."""
+    KNN = "KNN"
+    """K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
     #: datapoints
     #: which further means that the new data point will be assigned a value based on how closely it
-    #: matches the points in the training set.
-    KNN = "KNN"
-    #: Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an
-    #: L1 prior as regularizer.
+    #: matches the points in the training set."""
     LASSO_LARS = "LassoLars"
-    #: SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
+    """Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an
+    #: L1 prior as regularizer."""
+    SGD = "SGD"
+    """SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
     #: applications
     #: to find the model parameters that correspond to the best fit between predicted and actual
     #: outputs.
-    #: It's an inexact but powerful technique.
-    SGD = "SGD"
-    #: Random forest is a supervised learning algorithm.
-    #: The "forest"\  it builds, is an ensemble of decision trees, usually trained with the “bagging”\
+    #: It's an inexact but powerful technique."""
+    RANDOM_FOREST = "RandomForest"
+    """Random forest is a supervised learning algorithm.
+    #: The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging”
     #: method.
     #: The general idea of the bagging method is that a combination of learning models increases the
-    #: overall result.
-    RANDOM_FOREST = "RandomForest"
-    #: Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
-    #: decision trees. It is related to the widely used random forest algorithm.
+    #: overall result."""
     EXTREME_RANDOM_TREES = "ExtremeRandomTrees"
-    #: LightGBM is a gradient boosting framework that uses tree based learning algorithms.
+    """Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
+    #: decision trees. It is related to the widely used random forest algorithm."""
     LIGHT_GBM = "LightGBM"
-    #: XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model
-    #: using ensemble of base learners.
+    """LightGBM is a gradient boosting framework that uses tree based learning algorithms."""
     XG_BOOST_REGRESSOR = "XGBoostRegressor"
+    """XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model
+    #: using ensemble of base learners."""
 
 
 class RegressionPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for Regression task."""
 
-    #: The Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.
     SPEARMAN_CORRELATION = "SpearmanCorrelation"
-    #: The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between
-    #: models with different scales.
+    """The Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation."""
     NORMALIZED_ROOT_MEAN_SQUARED_ERROR = "NormalizedRootMeanSquaredError"
-    #: The R2 score is one of the performance evaluation measures for forecasting-based machine
-    #: learning models.
+    """The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between
+    #: models with different scales."""
     R2_SCORE = "R2Score"
-    #: The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute
-    #: Error (MAE) of (time) series with different scales.
+    """The R2 score is one of the performance evaluation measures for forecasting-based machine
+    #: learning models."""
     NORMALIZED_MEAN_ABSOLUTE_ERROR = "NormalizedMeanAbsoluteError"
+    """The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute
+    #: Error (MAE) of (time) series with different scales."""
 
 
 class RemoteLoginPortPublicAccess(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1070,10 +1070,10 @@ class ScheduleStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class SeasonalityMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Forecasting seasonality mode."""
 
-    #: Seasonality to be determined automatically.
     AUTO = "Auto"
-    #: Use the custom seasonality value.
+    """Seasonality to be determined automatically."""
     CUSTOM = "Custom"
+    """Use the custom seasonality value."""
 
 
 class SecretsType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1088,37 +1088,37 @@ class SecretsType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class ServiceDataAccessAuthIdentity(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """ServiceDataAccessAuthIdentity."""
 
-    #: Do not use any identity for service data access.
     NONE = "None"
-    #: Use the system assigned managed identity of the Workspace to authenticate service data access.
+    """Do not use any identity for service data access."""
     WORKSPACE_SYSTEM_ASSIGNED_IDENTITY = "WorkspaceSystemAssignedIdentity"
-    #: Use the user assigned managed identity of the Workspace to authenticate service data access.
+    """Use the system assigned managed identity of the Workspace to authenticate service data access."""
     WORKSPACE_USER_ASSIGNED_IDENTITY = "WorkspaceUserAssignedIdentity"
+    """Use the user assigned managed identity of the Workspace to authenticate service data access."""
 
 
 class ShortSeriesHandlingConfiguration(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """The parameter defining how if AutoML should handle short time series."""
 
-    #: Represents no/null value.
     NONE = "None"
-    #: Short series will be padded if there are no long series, otherwise short series will be
-    #: dropped.
+    """Represents no/null value."""
     AUTO = "Auto"
-    #: All the short series will be padded.
+    """Short series will be padded if there are no long series, otherwise short series will be
+    #: dropped."""
     PAD = "Pad"
-    #: All the short series will be dropped.
+    """All the short series will be padded."""
     DROP = "Drop"
+    """All the short series will be dropped."""
 
 
 class SkuScaleType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Node scaling setting for the compute sku."""
 
-    #: Automatically scales node count.
     AUTOMATIC = "Automatic"
-    #: Node count scaled upon user request.
+    """Automatically scales node count."""
     MANUAL = "Manual"
-    #: Fixed set of nodes.
+    """Node count scaled upon user request."""
     NONE = "None"
+    """Fixed set of nodes."""
 
 
 class SkuTier(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1168,15 +1168,15 @@ class StackMetaLearnerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """
 
     NONE = "None"
-    #: Default meta-learners are LogisticRegression for classification tasks.
     LOGISTIC_REGRESSION = "LogisticRegression"
-    #: Default meta-learners are LogisticRegression for classification task when CV is on.
+    """Default meta-learners are LogisticRegression for classification tasks."""
     LOGISTIC_REGRESSION_CV = "LogisticRegressionCV"
+    """Default meta-learners are LogisticRegression for classification task when CV is on."""
     LIGHT_GBM_CLASSIFIER = "LightGBMClassifier"
-    #: Default meta-learners are LogisticRegression for regression task.
     ELASTIC_NET = "ElasticNet"
-    #: Default meta-learners are LogisticRegression for regression task when CV is on.
+    """Default meta-learners are LogisticRegression for regression task."""
     ELASTIC_NET_CV = "ElasticNetCV"
+    """Default meta-learners are LogisticRegression for regression task when CV is on."""
     LIGHT_GBM_REGRESSOR = "LightGBMRegressor"
     LINEAR_REGRESSION = "LinearRegression"
 
@@ -1197,15 +1197,15 @@ class Status(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class StochasticOptimizer(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Stochastic optimizer for image models."""
 
-    #: No optimizer selected.
     NONE = "None"
-    #: Stochastic Gradient Descent optimizer.
+    """No optimizer selected."""
     SGD = "Sgd"
-    #: Adam is algorithm the optimizes stochastic objective functions based on adaptive estimates of
-    #: moments
+    """Stochastic Gradient Descent optimizer."""
     ADAM = "Adam"
-    #: AdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.
+    """Adam is algorithm the optimizes stochastic objective functions based on adaptive estimates of
+    #: moments"""
     ADAMW = "Adamw"
+    """AdamW is a variant of the optimizer Adam that has an improved implementation of weight decay."""
 
 
 class StorageAccountType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1218,8 +1218,8 @@ class StorageAccountType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class TargetAggregationFunction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Target aggregate function."""
 
-    #: Represent no value set.
     NONE = "None"
+    """Represent no value set."""
     SUM = "Sum"
     MAX = "Max"
     MIN = "Min"
@@ -1229,62 +1229,62 @@ class TargetAggregationFunction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class TargetLagsMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Target lags selection modes."""
 
-    #: Target lags to be determined automatically.
     AUTO = "Auto"
-    #: Use the custom target lags.
+    """Target lags to be determined automatically."""
     CUSTOM = "Custom"
+    """Use the custom target lags."""
 
 
 class TargetRollingWindowSizeMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Target rolling windows size mode."""
 
-    #: Determine rolling windows size automatically.
     AUTO = "Auto"
-    #: Use the specified rolling window size.
+    """Determine rolling windows size automatically."""
     CUSTOM = "Custom"
+    """Use the specified rolling window size."""
 
 
 class TaskType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """AutoMLJob Task type."""
 
-    #: Classification in machine learning and statistics is a supervised learning approach in which
-    #: the computer program learns from the data given to it and make new observations or
-    #: classifications.
     CLASSIFICATION = "Classification"
-    #: Regression means to predict the value using the input data. Regression models are used to
-    #: predict a continuous value.
+    """Classification in machine learning and statistics is a supervised learning approach in which
+    #: the computer program learns from the data given to it and make new observations or
+    #: classifications."""
     REGRESSION = "Regression"
-    #: Forecasting is a special kind of regression task that deals with time-series data and creates
-    #: forecasting model
-    #: that can be used to predict the near future values based on the inputs.
+    """Regression means to predict the value using the input data. Regression models are used to
+    #: predict a continuous value."""
     FORECASTING = "Forecasting"
-    #: Image Classification. Multi-class image classification is used when an image is classified with
+    """Forecasting is a special kind of regression task that deals with time-series data and creates
+    #: forecasting model
+    #: that can be used to predict the near future values based on the inputs."""
+    IMAGE_CLASSIFICATION = "ImageClassification"
+    """Image Classification. Multi-class image classification is used when an image is classified with
     #: only a single label
     #: from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog'
-    #: or a 'duck'.
-    IMAGE_CLASSIFICATION = "ImageClassification"
-    #: Image Classification Multilabel. Multi-label image classification is used when an image could
-    #: have one or more labels
-    #: from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.
+    #: or a 'duck'."""
     IMAGE_CLASSIFICATION_MULTILABEL = "ImageClassificationMultilabel"
-    #: Image Object Detection. Object detection is used to identify objects in an image and locate
-    #: each object with a
-    #: bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.
+    """Image Classification Multilabel. Multi-label image classification is used when an image could
+    #: have one or more labels
+    #: from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'."""
     IMAGE_OBJECT_DETECTION = "ImageObjectDetection"
-    #: Image Instance Segmentation. Instance segmentation is used to identify objects in an image at
-    #: the pixel level,
-    #: drawing a polygon around each object in the image.
+    """Image Object Detection. Object detection is used to identify objects in an image and locate
+    #: each object with a
+    #: bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each."""
     IMAGE_INSTANCE_SEGMENTATION = "ImageInstanceSegmentation"
-    #: Text classification (also known as text tagging or text categorization) is the process of
-    #: sorting texts into categories.
-    #: Categories are mutually exclusive.
+    """Image Instance Segmentation. Instance segmentation is used to identify objects in an image at
+    #: the pixel level,
+    #: drawing a polygon around each object in the image."""
     TEXT_CLASSIFICATION = "TextClassification"
-    #: Multilabel classification task assigns each sample to a group (zero or more) of target labels.
+    """Text classification (also known as text tagging or text categorization) is the process of
+    #: sorting texts into categories.
+    #: Categories are mutually exclusive."""
     TEXT_CLASSIFICATION_MULTILABEL = "TextClassificationMultilabel"
-    #: Text Named Entity Recognition a.k.a. TextNER.
-    #: Named Entity Recognition (NER) is the ability to take free-form text and identify the
-    #: occurrences of entities such as people, locations, organizations, and more.
+    """Multilabel classification task assigns each sample to a group (zero or more) of target labels."""
     TEXT_NER = "TextNER"
+    """Text Named Entity Recognition a.k.a. TextNER.
+    #: Named Entity Recognition (NER) is the ability to take free-form text and identify the
+    #: occurrences of entities such as people, locations, organizations, and more."""
 
 
 class TriggerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1316,8 +1316,8 @@ class UsageUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class UseStl(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Configure STL Decomposition of the time-series target column."""
 
-    #: No stl decomposition.
     NONE = "None"
+    """No stl decomposition."""
     SEASON = "Season"
     SEASON_TREND = "SeasonTrend"
 
@@ -1325,14 +1325,14 @@ class UseStl(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class ValidationMetricType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Metric computation method to use for validation metrics in image tasks."""
 
-    #: No metric.
     NONE = "None"
-    #: Coco metric.
+    """No metric."""
     COCO = "Coco"
-    #: Voc metric.
+    """Coco metric."""
     VOC = "Voc"
-    #: CocoVoc metric.
+    """Voc metric."""
     COCO_VOC = "CocoVoc"
+    """CocoVoc metric."""
 
 
 class ValueFormat(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1366,17 +1366,17 @@ class VMTier(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class WeekDay(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum of weekday."""
 
-    #: Monday weekday
     MONDAY = "Monday"
-    #: Tuesday weekday
+    """Monday weekday"""
     TUESDAY = "Tuesday"
-    #: Wednesday weekday
+    """Tuesday weekday"""
     WEDNESDAY = "Wednesday"
-    #: Thursday weekday
+    """Wednesday weekday"""
     THURSDAY = "Thursday"
-    #: Friday weekday
+    """Thursday weekday"""
     FRIDAY = "Friday"
-    #: Saturday weekday
+    """Friday weekday"""
     SATURDAY = "Saturday"
-    #: Sunday weekday
+    """Saturday weekday"""
     SUNDAY = "Sunday"
+    """Sunday weekday"""
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_models_py3.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_models_py3.py
index a0c8196e7297..f4a8e4d87a6f 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_models_py3.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_models_py3.py
@@ -56,7 +56,7 @@ class DatastoreCredentials(_serialization.Model):
         }
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.credentials_type: Optional[str] = None
@@ -84,7 +84,7 @@ class AccountKeyDatastoreCredentials(DatastoreCredentials):
         "secrets": {"key": "secrets", "type": "AccountKeyDatastoreSecrets"},
     }
 
-    def __init__(self, *, secrets: "_models.AccountKeyDatastoreSecrets", **kwargs):
+    def __init__(self, *, secrets: "_models.AccountKeyDatastoreSecrets", **kwargs: Any) -> None:
         """
         :keyword secrets: [Required] Storage account secrets. Required.
         :paramtype secrets: ~azure.mgmt.machinelearningservices.models.AccountKeyDatastoreSecrets
@@ -125,7 +125,7 @@ class DatastoreSecrets(_serialization.Model):
         }
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.secrets_type: Optional[str] = None
@@ -152,7 +152,7 @@ class AccountKeyDatastoreSecrets(DatastoreSecrets):
         "key": {"key": "key", "type": "str"},
     }
 
-    def __init__(self, *, key: Optional[str] = None, **kwargs):
+    def __init__(self, *, key: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword key: Storage account key.
         :paramtype key: str
@@ -162,6 +162,42 @@ def __init__(self, *, key: Optional[str] = None, **kwargs):
         self.key = key
 
 
+class AcrDetails(_serialization.Model):
+    """Details of ACR account to be used for the Registry.
+
+    :ivar system_created_acr_account:
+    :vartype system_created_acr_account:
+     ~azure.mgmt.machinelearningservices.models.SystemCreatedAcrAccount
+    :ivar user_created_acr_account:
+    :vartype user_created_acr_account:
+     ~azure.mgmt.machinelearningservices.models.UserCreatedAcrAccount
+    """
+
+    _attribute_map = {
+        "system_created_acr_account": {"key": "systemCreatedAcrAccount", "type": "SystemCreatedAcrAccount"},
+        "user_created_acr_account": {"key": "userCreatedAcrAccount", "type": "UserCreatedAcrAccount"},
+    }
+
+    def __init__(
+        self,
+        *,
+        system_created_acr_account: Optional["_models.SystemCreatedAcrAccount"] = None,
+        user_created_acr_account: Optional["_models.UserCreatedAcrAccount"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword system_created_acr_account:
+        :paramtype system_created_acr_account:
+         ~azure.mgmt.machinelearningservices.models.SystemCreatedAcrAccount
+        :keyword user_created_acr_account:
+        :paramtype user_created_acr_account:
+         ~azure.mgmt.machinelearningservices.models.UserCreatedAcrAccount
+        """
+        super().__init__(**kwargs)
+        self.system_created_acr_account = system_created_acr_account
+        self.user_created_acr_account = user_created_acr_account
+
+
 class AKSSchema(_serialization.Model):
     """AKSSchema.
 
@@ -173,7 +209,7 @@ class AKSSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "AKSSchemaProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.AKSSchemaProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.AKSSchemaProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: AKS properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
@@ -266,8 +302,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword compute_location: Location for the underlying compute.
         :paramtype compute_location: str
@@ -361,8 +397,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: AKS properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
@@ -422,8 +458,8 @@ def __init__(
         user_kube_config: Optional[str] = None,
         admin_kube_config: Optional[str] = None,
         image_pull_secret_name: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword user_kube_config: Content of kubeconfig file that can be used to connect to the
          Kubernetes cluster.
@@ -470,7 +506,7 @@ class ComputeSecrets(_serialization.Model):
         }
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.compute_type: Optional[str] = None
@@ -512,8 +548,8 @@ def __init__(
         user_kube_config: Optional[str] = None,
         admin_kube_config: Optional[str] = None,
         image_pull_secret_name: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword user_kube_config: Content of kubeconfig file that can be used to connect to the
          Kubernetes cluster.
@@ -574,8 +610,8 @@ def __init__(
         service_cidr: Optional[str] = None,
         dns_service_ip: Optional[str] = None,
         docker_bridge_cidr: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword subnet_id: Virtual network subnet resource ID the compute nodes belong to.
         :paramtype subnet_id: str
@@ -652,8 +688,8 @@ def __init__(
         aks_networking_configuration: Optional["_models.AksNetworkingConfiguration"] = None,
         load_balancer_type: Union[str, "_models.LoadBalancerType"] = "PublicIp",
         load_balancer_subnet: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword cluster_fqdn: Cluster full qualified domain name.
         :paramtype cluster_fqdn: str
@@ -699,7 +735,7 @@ class AmlComputeSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "AmlComputeProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.AmlComputeProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.AmlComputeProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Properties of AmlCompute.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
@@ -777,8 +813,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Properties of AmlCompute.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
@@ -852,7 +888,7 @@ class AmlComputeNodeInformation(_serialization.Model):
         "run_id": {"key": "runId", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.node_id = None
@@ -884,7 +920,7 @@ class AmlComputeNodesInformation(_serialization.Model):
         "next_link": {"key": "nextLink", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.nodes = None
@@ -996,8 +1032,8 @@ def __init__(
         remote_login_port_public_access: Union[str, "_models.RemoteLoginPortPublicAccess"] = "NotSpecified",
         enable_node_public_ip: bool = True,
         property_bag: Optional[JSON] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword os_type: Compute OS Type. Known values are: "Linux" and "Windows".
         :paramtype os_type: str or ~azure.mgmt.machinelearningservices.models.OsType
@@ -1079,8 +1115,8 @@ def __init__(
         name: Optional[str] = None,
         display: Optional["_models.AmlOperationDisplay"] = None,
         is_data_action: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword name: Operation name: {provider}/{resource}/{operation}.
         :paramtype name: str
@@ -1122,8 +1158,8 @@ def __init__(
         resource: Optional[str] = None,
         operation: Optional[str] = None,
         description: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword provider: The resource provider name: Microsoft.MachineLearningExperimentation.
         :paramtype provider: str
@@ -1152,7 +1188,7 @@ class AmlOperationListResult(_serialization.Model):
         "value": {"key": "value", "type": "[AmlOperation]"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.AmlOperation"]] = None, **kwargs):
+    def __init__(self, *, value: Optional[List["_models.AmlOperation"]] = None, **kwargs: Any) -> None:
         """
         :keyword value: List of AML workspace operations supported by the AML workspace resource
          provider.
@@ -1188,7 +1224,7 @@ class IdentityConfiguration(_serialization.Model):
         "identity_type": {"AMLToken": "AmlToken", "Managed": "ManagedIdentity", "UserIdentity": "UserIdentity"}
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.identity_type: Optional[str] = None
@@ -1213,7 +1249,7 @@ class AmlToken(IdentityConfiguration):
         "identity_type": {"key": "identityType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.identity_type: str = "AMLToken"
@@ -1242,8 +1278,8 @@ def __init__(
         id: Optional[str] = None,  # pylint: disable=redefined-builtin
         display_name: Optional[str] = None,
         description: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword id: Specifies the feature ID.
         :paramtype id: str
@@ -1258,6 +1294,32 @@ def __init__(
         self.description = description
 
 
+class ArmResourceId(_serialization.Model):
+    """ARM ResourceId of a resource.
+
+    :ivar resource_id: Arm ResourceId is in the format
+     "/subscriptions/{SubscriptionId}/resourceGroups/{ResourceGroupName}/providers/Microsoft.Storage/storageAccounts/{StorageAccountName}"
+     or
+     "/subscriptions/{SubscriptionId}/resourceGroups/{ResourceGroupName}/providers/Microsoft.ContainerRegistry/registries/{AcrName}".
+    :vartype resource_id: str
+    """
+
+    _attribute_map = {
+        "resource_id": {"key": "resourceId", "type": "str"},
+    }
+
+    def __init__(self, *, resource_id: Optional[str] = None, **kwargs: Any) -> None:
+        """
+        :keyword resource_id: Arm ResourceId is in the format
+         "/subscriptions/{SubscriptionId}/resourceGroups/{ResourceGroupName}/providers/Microsoft.Storage/storageAccounts/{StorageAccountName}"
+         or
+         "/subscriptions/{SubscriptionId}/resourceGroups/{ResourceGroupName}/providers/Microsoft.ContainerRegistry/registries/{AcrName}".
+        :paramtype resource_id: str
+        """
+        super().__init__(**kwargs)
+        self.resource_id = resource_id
+
+
 class ResourceBase(_serialization.Model):
     """ResourceBase.
 
@@ -1281,8 +1343,8 @@ def __init__(
         description: Optional[str] = None,
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -1328,8 +1390,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         is_anonymous: bool = False,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -1387,8 +1449,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -1426,7 +1488,9 @@ class AssetJobInput(_serialization.Model):
         "uri": {"key": "uri", "type": "str"},
     }
 
-    def __init__(self, *, uri: str, mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None, **kwargs):
+    def __init__(
+        self, *, uri: str, mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword mode: Input Asset Delivery Mode. Known values are: "ReadOnlyMount", "ReadWriteMount",
          "Download", "Direct", "EvalMount", and "EvalDownload".
@@ -1454,8 +1518,12 @@ class AssetJobOutput(_serialization.Model):
     }
 
     def __init__(
-        self, *, mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None, uri: Optional[str] = None, **kwargs
-    ):
+        self,
+        *,
+        mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
+        uri: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
         :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
@@ -1496,7 +1564,7 @@ class AssetReferenceBase(_serialization.Model):
         }
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.reference_type: Optional[str] = None
@@ -1523,7 +1591,7 @@ class AssignedUser(_serialization.Model):
         "tenant_id": {"key": "tenantId", "type": "str"},
     }
 
-    def __init__(self, *, object_id: str, tenant_id: str, **kwargs):
+    def __init__(self, *, object_id: str, tenant_id: str, **kwargs: Any) -> None:
         """
         :keyword object_id: User’s AAD Object Id. Required.
         :paramtype object_id: str
@@ -1558,7 +1626,7 @@ class ForecastHorizon(_serialization.Model):
 
     _subtype_map = {"mode": {"Auto": "AutoForecastHorizon", "Custom": "CustomForecastHorizon"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: Optional[str] = None
@@ -1582,7 +1650,7 @@ class AutoForecastHorizon(ForecastHorizon):
         "mode": {"key": "mode", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: str = "Auto"
@@ -1668,8 +1736,8 @@ def __init__(
         identity: Optional["_models.IdentityConfiguration"] = None,
         is_archived: bool = False,
         services: Optional[Dict[str, "_models.JobService"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -1713,54 +1781,54 @@ class AutoMLJob(JobBaseProperties):  # pylint: disable=too-many-instance-attribu
     Use this class for executing AutoML tasks like Classification/Regression etc.
     See TaskType enum for all the tasks supported.
 
-        Variables are only populated by the server, and will be ignored when sending a request.
-
-        All required parameters must be populated in order to send to Azure.
-
-        :ivar description: The asset description text.
-        :vartype description: str
-        :ivar properties: The asset property dictionary.
-        :vartype properties: dict[str, str]
-        :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
-        :vartype tags: dict[str, str]
-        :ivar component_id: ARM resource ID of the component resource.
-        :vartype component_id: str
-        :ivar compute_id: ARM resource ID of the compute resource.
-        :vartype compute_id: str
-        :ivar display_name: Display name of job.
-        :vartype display_name: str
-        :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
-         placed in the "Default" experiment.
-        :vartype experiment_name: str
-        :ivar identity: Identity configuration. If set, this should be one of AmlToken,
-         ManagedIdentity, UserIdentity or null.
-         Defaults to AmlToken if null.
-        :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
-        :ivar is_archived: Is the asset archived?.
-        :vartype is_archived: bool
-        :ivar job_type: [Required] Specifies the type of job. Required. Known values are: "AutoML",
-         "Command", "Sweep", and "Pipeline".
-        :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
-        :ivar services: List of JobEndpoints.
-         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
-        :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
-        :ivar status: Status of the job. Known values are: "NotStarted", "Starting", "Provisioning",
-         "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
-         "Canceled", "NotResponding", "Paused", and "Unknown".
-        :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
-        :ivar environment_id: The ARM resource ID of the Environment specification for the job.
-         This is optional value to provide, if not provided, AutoML will default this to Production
-         AutoML curated environment version when running the job.
-        :vartype environment_id: str
-        :ivar environment_variables: Environment variables included in the job.
-        :vartype environment_variables: dict[str, str]
-        :ivar outputs: Mapping of output data bindings used in the job.
-        :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
-        :ivar resources: Compute Resource configuration for the job.
-        :vartype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
-        :ivar task_details: [Required] This represents scenario which can be one of Tables/NLP/Image.
-         Required.
-        :vartype task_details: ~azure.mgmt.machinelearningservices.models.AutoMLVertical
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: [Required] Specifies the type of job. Required. Known values are: "AutoML",
+     "Command", "Sweep", and "Pipeline".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Known values are: "NotStarted", "Starting", "Provisioning",
+     "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
+     "Canceled", "NotResponding", "Paused", and "Unknown".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar environment_id: The ARM resource ID of the Environment specification for the job.
+     This is optional value to provide, if not provided, AutoML will default this to Production
+     AutoML curated environment version when running the job.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables included in the job.
+    :vartype environment_variables: dict[str, str]
+    :ivar outputs: Mapping of output data bindings used in the job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar resources: Compute Resource configuration for the job.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+    :ivar task_details: [Required] This represents scenario which can be one of Tables/NLP/Image.
+     Required.
+    :vartype task_details: ~azure.mgmt.machinelearningservices.models.AutoMLVertical
     """
 
     _validation = {
@@ -1807,8 +1875,8 @@ def __init__(
         environment_variables: Optional[Dict[str, str]] = None,
         outputs: Optional[Dict[str, "_models.JobOutput"]] = None,
         resources: Optional["_models.JobResourceConfiguration"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -1873,26 +1941,26 @@ class AutoMLVertical(_serialization.Model):
     """AutoML vertical class.
     Base class for AutoML verticals - TableVertical/ImageVertical/NLPVertical.
 
-        You probably want to use the sub-classes and not this class directly. Known sub-classes are:
-        Classification, Forecasting, ImageClassification, ImageClassificationMultilabel,
-        ImageInstanceSegmentation, ImageObjectDetection, Regression, TextClassification,
-        TextClassificationMultilabel, TextNer
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    Classification, Forecasting, ImageClassification, ImageClassificationMultilabel,
+    ImageInstanceSegmentation, ImageObjectDetection, Regression, TextClassification,
+    TextClassificationMultilabel, TextNer
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
     """
 
     _validation = {
@@ -1928,8 +1996,8 @@ def __init__(
         training_data: "_models.MLTableJobInput",
         log_verbosity: Optional[Union[str, "_models.LogVerbosity"]] = None,
         target_column_name: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -1970,7 +2038,7 @@ class NCrossValidations(_serialization.Model):
 
     _subtype_map = {"mode": {"Auto": "AutoNCrossValidations", "Custom": "CustomNCrossValidations"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: Optional[str] = None
@@ -1994,7 +2062,7 @@ class AutoNCrossValidations(NCrossValidations):
         "mode": {"key": "mode", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: str = "Auto"
@@ -2014,7 +2082,9 @@ class AutoPauseProperties(_serialization.Model):
         "enabled": {"key": "enabled", "type": "bool"},
     }
 
-    def __init__(self, *, delay_in_minutes: Optional[int] = None, enabled: Optional[bool] = None, **kwargs):
+    def __init__(
+        self, *, delay_in_minutes: Optional[int] = None, enabled: Optional[bool] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword delay_in_minutes:
         :paramtype delay_in_minutes: int
@@ -2049,8 +2119,8 @@ def __init__(
         min_node_count: Optional[int] = None,
         enabled: Optional[bool] = None,
         max_node_count: Optional[int] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword min_node_count:
         :paramtype min_node_count: int
@@ -2087,7 +2157,7 @@ class Seasonality(_serialization.Model):
 
     _subtype_map = {"mode": {"Auto": "AutoSeasonality", "Custom": "CustomSeasonality"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: Optional[str] = None
@@ -2110,7 +2180,7 @@ class AutoSeasonality(Seasonality):
         "mode": {"key": "mode", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: str = "Auto"
@@ -2139,7 +2209,7 @@ class TargetLags(_serialization.Model):
 
     _subtype_map = {"mode": {"Auto": "AutoTargetLags", "Custom": "CustomTargetLags"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: Optional[str] = None
@@ -2163,7 +2233,7 @@ class AutoTargetLags(TargetLags):
         "mode": {"key": "mode", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: str = "Auto"
@@ -2192,7 +2262,7 @@ class TargetRollingWindowSize(_serialization.Model):
 
     _subtype_map = {"mode": {"Auto": "AutoTargetRollingWindowSize", "Custom": "CustomTargetRollingWindowSize"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: Optional[str] = None
@@ -2216,7 +2286,7 @@ class AutoTargetRollingWindowSize(TargetRollingWindowSize):
         "mode": {"key": "mode", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: str = "Auto"
@@ -2279,8 +2349,8 @@ def __init__(
         description: Optional[str] = None,
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -2365,8 +2435,8 @@ def __init__(
         endpoint: Optional[str] = None,
         protocol: Optional[str] = None,
         service_data_access_auth_identity: Optional[Union[str, "_models.ServiceDataAccessAuthIdentity"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -2456,8 +2526,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         service_data_access_auth_identity: Optional[Union[str, "_models.ServiceDataAccessAuthIdentity"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -2551,8 +2621,8 @@ def __init__(
         endpoint: Optional[str] = None,
         protocol: Optional[str] = None,
         service_data_access_auth_identity: Optional[Union[str, "_models.ServiceDataAccessAuthIdentity"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -2656,8 +2726,8 @@ def __init__(
         endpoint: Optional[str] = None,
         protocol: Optional[str] = None,
         service_data_access_auth_identity: Optional[Union[str, "_models.ServiceDataAccessAuthIdentity"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -2727,7 +2797,7 @@ class EarlyTerminationPolicy(_serialization.Model):
         }
     }
 
-    def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, **kwargs):
+    def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, **kwargs: Any) -> None:
         """
         :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
         :paramtype delay_evaluation: int
@@ -2741,7 +2811,8 @@ def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, *
 
 
 class BanditPolicy(EarlyTerminationPolicy):
-    """Defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation.
+    """Defines an early termination policy based on slack criteria, and a frequency and delay interval
+    for evaluation.
 
     All required parameters must be populated in order to send to Azure.
 
@@ -2778,8 +2849,8 @@ def __init__(
         evaluation_interval: int = 0,
         slack_amount: float = 0,
         slack_factor: float = 0,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
         :paramtype delay_evaluation: int
@@ -2828,7 +2899,7 @@ class Resource(_serialization.Model):
         "system_data": {"key": "systemData", "type": "SystemData"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.id = None
@@ -2838,7 +2909,8 @@ def __init__(self, **kwargs):
 
 
 class TrackedResource(Resource):
-    """The resource model definition for an Azure Resource Manager tracked top level resource which has 'tags' and a 'location'.
+    """The resource model definition for an Azure Resource Manager tracked top level resource which
+    has 'tags' and a 'location'.
 
     Variables are only populated by the server, and will be ignored when sending a request.
 
@@ -2878,7 +2950,7 @@ class TrackedResource(Resource):
         "location": {"key": "location", "type": "str"},
     }
 
-    def __init__(self, *, location: str, tags: Optional[Dict[str, str]] = None, **kwargs):
+    def __init__(self, *, location: str, tags: Optional[Dict[str, str]] = None, **kwargs: Any) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -2954,8 +3026,8 @@ def __init__(
         identity: Optional["_models.ManagedServiceIdentity"] = None,
         kind: Optional[str] = None,
         sku: Optional["_models.Sku"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -3010,8 +3082,8 @@ def __init__(
         environment_id: Optional[str] = None,
         environment_variables: Optional[Dict[str, str]] = None,
         properties: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_configuration: Code configuration for the endpoint deployment.
         :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
@@ -3127,8 +3199,8 @@ def __init__(
         output_file_name: str = "predictions.csv",
         resources: Optional["_models.DeploymentResourceConfiguration"] = None,
         retry_settings: Optional["_models.BatchRetrySettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_configuration: Code configuration for the endpoint deployment.
         :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
@@ -3211,8 +3283,8 @@ class BatchDeploymentTrackedResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.BatchDeployment"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.BatchDeployment"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of BatchDeployment objects. If null, there are no
          additional pages.
@@ -3289,8 +3361,8 @@ def __init__(
         identity: Optional["_models.ManagedServiceIdentity"] = None,
         kind: Optional[str] = None,
         sku: Optional["_models.Sku"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -3325,7 +3397,7 @@ class BatchEndpointDefaults(_serialization.Model):
         "deployment_name": {"key": "deploymentName", "type": "str"},
     }
 
-    def __init__(self, *, deployment_name: Optional[str] = None, **kwargs):
+    def __init__(self, *, deployment_name: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword deployment_name: Name of the deployment that will be default for the endpoint.
          This deployment will end up getting 100% traffic when the endpoint scoring URL is invoked.
@@ -3382,8 +3454,8 @@ def __init__(
         description: Optional[str] = None,
         keys: Optional["_models.EndpointAuthKeys"] = None,
         properties: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword auth_mode: [Required] Use 'Key' for key based authentication and 'AMLToken' for Azure
          Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
@@ -3464,8 +3536,8 @@ def __init__(
         keys: Optional["_models.EndpointAuthKeys"] = None,
         properties: Optional[Dict[str, str]] = None,
         defaults: Optional["_models.BatchEndpointDefaults"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword auth_mode: [Required] Use 'Key' for key based authentication and 'AMLToken' for Azure
          Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
@@ -3503,8 +3575,8 @@ class BatchEndpointTrackedResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.BatchEndpoint"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.BatchEndpoint"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of BatchEndpoint objects. If null, there are no
          additional pages.
@@ -3531,7 +3603,7 @@ class BatchRetrySettings(_serialization.Model):
         "timeout": {"key": "timeout", "type": "duration"},
     }
 
-    def __init__(self, *, max_retries: int = 3, timeout: datetime.timedelta = "PT30S", **kwargs):
+    def __init__(self, *, max_retries: int = 3, timeout: datetime.timedelta = "PT30S", **kwargs: Any) -> None:
         """
         :keyword max_retries: Maximum retry count for a mini-batch.
         :paramtype max_retries: int
@@ -3547,16 +3619,16 @@ class SamplingAlgorithm(_serialization.Model):
     """The Sampling Algorithm used to generate hyperparameter values, along with properties to
     configure the algorithm.
 
-        You probably want to use the sub-classes and not this class directly. Known sub-classes are:
-        BayesianSamplingAlgorithm, GridSamplingAlgorithm, RandomSamplingAlgorithm
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    BayesianSamplingAlgorithm, GridSamplingAlgorithm, RandomSamplingAlgorithm
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar sampling_algorithm_type: [Required] The algorithm used for generating hyperparameter
-         values, along with configuration properties. Required. Known values are: "Grid", "Random", and
-         "Bayesian".
-        :vartype sampling_algorithm_type: str or
-         ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    :ivar sampling_algorithm_type: [Required] The algorithm used for generating hyperparameter
+     values, along with configuration properties. Required. Known values are: "Grid", "Random", and
+     "Bayesian".
+    :vartype sampling_algorithm_type: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
     """
 
     _validation = {
@@ -3575,7 +3647,7 @@ class SamplingAlgorithm(_serialization.Model):
         }
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.sampling_algorithm_type: Optional[str] = None
@@ -3601,7 +3673,7 @@ class BayesianSamplingAlgorithm(SamplingAlgorithm):
         "sampling_algorithm_type": {"key": "samplingAlgorithmType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.sampling_algorithm_type: str = "Bayesian"
@@ -3640,7 +3712,7 @@ class BuildContext(_serialization.Model):
         "dockerfile_path": {"key": "dockerfilePath", "type": "str"},
     }
 
-    def __init__(self, *, context_uri: str, dockerfile_path: str = "Dockerfile", **kwargs):
+    def __init__(self, *, context_uri: str, dockerfile_path: str = "Dockerfile", **kwargs: Any) -> None:
         """
         :keyword context_uri: [Required] URI of the Docker build context used to build the image.
          Supports blob URIs on environment creation and may return blob or Git URIs.
@@ -3714,8 +3786,8 @@ def __init__(
         thumbprint: str,
         authority_url: Optional[str] = None,
         resource_url: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword authority_url: Authority URL used for authentication.
         :paramtype authority_url: str
@@ -3763,7 +3835,7 @@ class CertificateDatastoreSecrets(DatastoreSecrets):
         "certificate": {"key": "certificate", "type": "str"},
     }
 
-    def __init__(self, *, certificate: Optional[str] = None, **kwargs):
+    def __init__(self, *, certificate: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword certificate: Service principal certificate.
         :paramtype certificate: str
@@ -3774,7 +3846,8 @@ def __init__(self, *, certificate: Optional[str] = None, **kwargs):
 
 
 class TableVertical(_serialization.Model):
-    """Abstract class for AutoML tasks that use table dataset as input - such as Classification/Regression/Forecasting.
+    """Abstract class for AutoML tasks that use table dataset as input - such as
+    Classification/Regression/Forecasting.
 
     :ivar cv_split_column_names: Columns to use for CVSplit data.
     :vartype cv_split_column_names: list[str]
@@ -3829,8 +3902,8 @@ def __init__(
         validation_data: Optional["_models.MLTableJobInput"] = None,
         validation_data_size: Optional[float] = None,
         weight_column_name: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword cv_split_column_names: Columns to use for CVSplit data.
         :paramtype cv_split_column_names: list[str]
@@ -3972,8 +4045,8 @@ def __init__(
         positive_label: Optional[str] = None,
         primary_metric: Optional[Union[str, "_models.ClassificationPrimaryMetrics"]] = None,
         training_settings: Optional["_models.ClassificationTrainingSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -4097,8 +4170,8 @@ def __init__(
         enable_vote_ensemble: bool = True,
         ensemble_model_download_timeout: datetime.timedelta = "PT5M",
         stack_ensemble_settings: Optional["_models.StackEnsembleSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword enable_dnn_training: Enable recommendation of DNN models.
         :paramtype enable_dnn_training: bool
@@ -4180,8 +4253,8 @@ def __init__(
         stack_ensemble_settings: Optional["_models.StackEnsembleSettings"] = None,
         allowed_training_algorithms: Optional[List[Union[str, "_models.ClassificationModels"]]] = None,
         blocked_training_algorithms: Optional[List[Union[str, "_models.ClassificationModels"]]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword enable_dnn_training: Enable recommendation of DNN models.
         :paramtype enable_dnn_training: bool
@@ -4232,7 +4305,7 @@ class ClusterUpdateParameters(_serialization.Model):
         "properties": {"key": "properties.properties", "type": "ScaleSettingsInformation"},
     }
 
-    def __init__(self, *, properties: Optional["_models.ScaleSettingsInformation"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.ScaleSettingsInformation"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Properties of ClusterUpdate.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ScaleSettingsInformation
@@ -4261,7 +4334,7 @@ class CodeConfiguration(_serialization.Model):
         "scoring_script": {"key": "scoringScript", "type": "str"},
     }
 
-    def __init__(self, *, scoring_script: str, code_id: Optional[str] = None, **kwargs):
+    def __init__(self, *, scoring_script: str, code_id: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword code_id: ARM resource ID of the code asset.
         :paramtype code_id: str
@@ -4311,7 +4384,7 @@ class CodeContainer(Resource):
         "properties": {"key": "properties", "type": "CodeContainerProperties"},
     }
 
-    def __init__(self, *, properties: "_models.CodeContainerProperties", **kwargs):
+    def __init__(self, *, properties: "_models.CodeContainerProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.CodeContainerProperties
@@ -4360,8 +4433,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -4391,8 +4464,8 @@ class CodeContainerResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.CodeContainer"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.CodeContainer"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of CodeContainer objects. If null, there are no
          additional pages.
@@ -4443,7 +4516,7 @@ class CodeVersion(Resource):
         "properties": {"key": "properties", "type": "CodeVersionProperties"},
     }
 
-    def __init__(self, *, properties: "_models.CodeVersionProperties", **kwargs):
+    def __init__(self, *, properties: "_models.CodeVersionProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.CodeVersionProperties
@@ -4487,8 +4560,8 @@ def __init__(
         is_anonymous: bool = False,
         is_archived: bool = False,
         code_uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -4530,8 +4603,8 @@ class CodeVersionResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.CodeVersion"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.CodeVersion"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of CodeVersion objects. If null, there are no
          additional pages.
@@ -4559,7 +4632,7 @@ class ColumnTransformer(_serialization.Model):
         "parameters": {"key": "parameters", "type": "object"},
     }
 
-    def __init__(self, *, fields: Optional[List[str]] = None, parameters: Optional[JSON] = None, **kwargs):
+    def __init__(self, *, fields: Optional[List[str]] = None, parameters: Optional[JSON] = None, **kwargs: Any) -> None:
         """
         :keyword fields: Fields to apply transformer logic on.
         :paramtype fields: list[str]
@@ -4690,8 +4763,8 @@ def __init__(
         limits: Optional["_models.CommandJobLimits"] = None,
         outputs: Optional[Dict[str, "_models.JobOutput"]] = None,
         resources: Optional["_models.JobResourceConfiguration"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -4792,7 +4865,7 @@ class JobLimits(_serialization.Model):
 
     _subtype_map = {"job_limits_type": {"Command": "CommandJobLimits", "Sweep": "SweepJobLimits"}}
 
-    def __init__(self, *, timeout: Optional[datetime.timedelta] = None, **kwargs):
+    def __init__(self, *, timeout: Optional[datetime.timedelta] = None, **kwargs: Any) -> None:
         """
         :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
          cancelled. Only supports duration with precision as low as Seconds.
@@ -4825,7 +4898,7 @@ class CommandJobLimits(JobLimits):
         "timeout": {"key": "timeout", "type": "duration"},
     }
 
-    def __init__(self, *, timeout: Optional[datetime.timedelta] = None, **kwargs):
+    def __init__(self, *, timeout: Optional[datetime.timedelta] = None, **kwargs: Any) -> None:
         """
         :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
          cancelled. Only supports duration with precision as low as Seconds.
@@ -4873,7 +4946,7 @@ class ComponentContainer(Resource):
         "properties": {"key": "properties", "type": "ComponentContainerProperties"},
     }
 
-    def __init__(self, *, properties: "_models.ComponentContainerProperties", **kwargs):
+    def __init__(self, *, properties: "_models.ComponentContainerProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComponentContainerProperties
@@ -4888,22 +4961,24 @@ class ComponentContainerProperties(AssetContainer):
 
     .. raw:: html
 
-       <see href="https://docs.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-command" />.
+       <see
+    href="https://docs.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-command"
+    />.
 
-        Variables are only populated by the server, and will be ignored when sending a request.
+    Variables are only populated by the server, and will be ignored when sending a request.
 
-        :ivar description: The asset description text.
-        :vartype description: str
-        :ivar properties: The asset property dictionary.
-        :vartype properties: dict[str, str]
-        :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
-        :vartype tags: dict[str, str]
-        :ivar is_archived: Is the asset archived?.
-        :vartype is_archived: bool
-        :ivar latest_version: The latest version inside this container.
-        :vartype latest_version: str
-        :ivar next_version: The next auto incremental version.
-        :vartype next_version: str
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
     """
 
     _validation = {
@@ -4927,8 +5002,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -4958,8 +5033,12 @@ class ComponentContainerResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ComponentContainer"]] = None, **kwargs
-    ):
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["_models.ComponentContainer"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of ComponentContainer objects. If null, there are
          no additional pages.
@@ -5010,7 +5089,7 @@ class ComponentVersion(Resource):
         "properties": {"key": "properties", "type": "ComponentVersionProperties"},
     }
 
-    def __init__(self, *, properties: "_models.ComponentVersionProperties", **kwargs):
+    def __init__(self, *, properties: "_models.ComponentVersionProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComponentVersionProperties
@@ -5061,8 +5140,8 @@ def __init__(
         is_anonymous: bool = False,
         is_archived: bool = False,
         component_spec: Optional[JSON] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -5111,8 +5190,12 @@ class ComponentVersionResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ComponentVersion"]] = None, **kwargs
-    ):
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["_models.ComponentVersion"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of ComponentVersion objects. If null, there are
          no additional pages.
@@ -5136,7 +5219,7 @@ class ComputeInstanceSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "ComputeInstanceProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.ComputeInstanceProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.ComputeInstanceProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Properties of ComputeInstance.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
@@ -5214,8 +5297,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Properties of ComputeInstance.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
@@ -5264,7 +5347,9 @@ class ComputeInstanceApplication(_serialization.Model):
         "endpoint_uri": {"key": "endpointUri", "type": "str"},
     }
 
-    def __init__(self, *, display_name: Optional[str] = None, endpoint_uri: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, display_name: Optional[str] = None, endpoint_uri: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword display_name: Name of the ComputeInstance application.
         :paramtype display_name: str
@@ -5298,7 +5383,7 @@ class ComputeInstanceConnectivityEndpoints(_serialization.Model):
         "private_ip_address": {"key": "privateIpAddress", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.public_ip_address = None
@@ -5345,8 +5430,8 @@ def __init__(
         gpu: Optional[str] = None,
         network: Optional[Union[str, "_models.Network"]] = None,
         environment: Optional["_models.ComputeInstanceEnvironmentInfo"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword name: Name of the ComputeInstance container.
         :paramtype name: str
@@ -5394,7 +5479,7 @@ class ComputeInstanceCreatedBy(_serialization.Model):
         "user_id": {"key": "userId", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.user_name = None
@@ -5433,8 +5518,8 @@ def __init__(
         disk_size_gb: Optional[int] = None,
         lun: Optional[int] = None,
         storage_account_type: Union[str, "_models.StorageAccountType"] = "Standard_LRS",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword caching: Caching type of Data Disk. Known values are: "None", "ReadOnly", and
          "ReadWrite".
@@ -5504,8 +5589,8 @@ def __init__(
         mount_state: Optional[Union[str, "_models.MountState"]] = None,
         mounted_on: Optional[datetime.datetime] = None,
         error: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword source: Source of the ComputeInstance data mount.
         :paramtype source: str
@@ -5553,7 +5638,7 @@ class ComputeInstanceEnvironmentInfo(_serialization.Model):
         "version": {"key": "version", "type": "str"},
     }
 
-    def __init__(self, *, name: Optional[str] = None, version: Optional[str] = None, **kwargs):
+    def __init__(self, *, name: Optional[str] = None, version: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword name: name of environment.
         :paramtype name: str
@@ -5596,8 +5681,8 @@ def __init__(
         operation_time: Optional[datetime.datetime] = None,
         operation_status: Optional[Union[str, "_models.OperationStatus"]] = None,
         operation_trigger: Optional[Union[str, "_models.OperationTrigger"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword operation_name: Name of the last operation. Known values are: "Create", "Start",
          "Stop", "Restart", "Reimage", and "Delete".
@@ -5690,7 +5775,6 @@ class ComputeInstanceProperties(_serialization.Model):  # pylint: disable=too-ma
         "errors": {"readonly": True},
         "state": {"readonly": True},
         "last_operation": {"readonly": True},
-        "schedules": {"readonly": True},
         "containers": {"readonly": True},
         "data_disks": {"readonly": True},
         "data_mounts": {"readonly": True},
@@ -5732,9 +5816,10 @@ def __init__(
         compute_instance_authorization_type: Union[str, "_models.ComputeInstanceAuthorizationType"] = "personal",
         personal_compute_instance_settings: Optional["_models.PersonalComputeInstanceSettings"] = None,
         setup_scripts: Optional["_models.SetupScripts"] = None,
+        schedules: Optional["_models.ComputeSchedules"] = None,
         enable_node_public_ip: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword vm_size: Virtual Machine Size.
         :paramtype vm_size: str
@@ -5757,6 +5842,8 @@ def __init__(
          ~azure.mgmt.machinelearningservices.models.PersonalComputeInstanceSettings
         :keyword setup_scripts: Details of customized scripts to execute for setting up the cluster.
         :paramtype setup_scripts: ~azure.mgmt.machinelearningservices.models.SetupScripts
+        :keyword schedules: The list of schedules to be applied on the computes.
+        :paramtype schedules: ~azure.mgmt.machinelearningservices.models.ComputeSchedules
         :keyword enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
          values are: Possible values are: true - Indicates that the compute nodes will have public IPs
          provisioned. false - Indicates that the compute nodes will have a private endpoint and no
@@ -5777,7 +5864,7 @@ def __init__(
         self.personal_compute_instance_settings = personal_compute_instance_settings
         self.setup_scripts = setup_scripts
         self.last_operation = None
-        self.schedules = None
+        self.schedules = schedules
         self.enable_node_public_ip = enable_node_public_ip
         self.containers = None
         self.data_disks = None
@@ -5821,8 +5908,8 @@ def __init__(
         *,
         ssh_public_access: Union[str, "_models.SshPublicAccess"] = "Disabled",
         admin_public_key: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword ssh_public_access: State of the public SSH port. Possible values are: Disabled -
          Indicates that the public ssh port is closed on this instance. Enabled - Indicates that the
@@ -5851,7 +5938,7 @@ class ComputeInstanceVersion(_serialization.Model):
         "runtime": {"key": "runtime", "type": "str"},
     }
 
-    def __init__(self, *, runtime: Optional[str] = None, **kwargs):
+    def __init__(self, *, runtime: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword runtime: Runtime of compute instance.
         :paramtype runtime: str
@@ -5871,7 +5958,7 @@ class ComputeResourceSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "Compute"},
     }
 
-    def __init__(self, *, properties: Optional["_models.Compute"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.Compute"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Compute properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.Compute
@@ -5935,8 +6022,8 @@ def __init__(
         location: Optional[str] = None,
         tags: Optional[Dict[str, str]] = None,
         sku: Optional["_models.Sku"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Compute properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.Compute
@@ -5973,7 +6060,9 @@ class ComputeSchedules(_serialization.Model):
         "compute_start_stop": {"key": "computeStartStop", "type": "[ComputeStartStopSchedule]"},
     }
 
-    def __init__(self, *, compute_start_stop: Optional[List["_models.ComputeStartStopSchedule"]] = None, **kwargs):
+    def __init__(
+        self, *, compute_start_stop: Optional[List["_models.ComputeStartStopSchedule"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword compute_start_stop: The list of compute start stop schedules to be applied.
         :paramtype compute_start_stop:
@@ -6002,9 +6091,9 @@ class ComputeStartStopSchedule(_serialization.Model):
      "Cron".
     :vartype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
     :ivar recurrence: Required if triggerType is Recurrence.
-    :vartype recurrence: ~azure.mgmt.machinelearningservices.models.RecurrenceTrigger
+    :vartype recurrence: ~azure.mgmt.machinelearningservices.models.Recurrence
     :ivar cron: Required if triggerType is Cron.
-    :vartype cron: ~azure.mgmt.machinelearningservices.models.CronTrigger
+    :vartype cron: ~azure.mgmt.machinelearningservices.models.Cron
     :ivar schedule: [Deprecated] Not used any more.
     :vartype schedule: ~azure.mgmt.machinelearningservices.models.ScheduleBase
     """
@@ -6020,8 +6109,8 @@ class ComputeStartStopSchedule(_serialization.Model):
         "status": {"key": "status", "type": "str"},
         "action": {"key": "action", "type": "str"},
         "trigger_type": {"key": "triggerType", "type": "str"},
-        "recurrence": {"key": "recurrence", "type": "RecurrenceTrigger"},
-        "cron": {"key": "cron", "type": "CronTrigger"},
+        "recurrence": {"key": "recurrence", "type": "Recurrence"},
+        "cron": {"key": "cron", "type": "Cron"},
         "schedule": {"key": "schedule", "type": "ScheduleBase"},
     }
 
@@ -6031,11 +6120,11 @@ def __init__(
         status: Optional[Union[str, "_models.ScheduleStatus"]] = None,
         action: Optional[Union[str, "_models.ComputePowerAction"]] = None,
         trigger_type: Optional[Union[str, "_models.TriggerType"]] = None,
-        recurrence: Optional["_models.RecurrenceTrigger"] = None,
-        cron: Optional["_models.CronTrigger"] = None,
+        recurrence: Optional["_models.Recurrence"] = None,
+        cron: Optional["_models.Cron"] = None,
         schedule: Optional["_models.ScheduleBase"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword status: Is the schedule enabled or disabled?. Known values are: "Enabled" and
          "Disabled".
@@ -6046,9 +6135,9 @@ def __init__(
          "Cron".
         :paramtype trigger_type: str or ~azure.mgmt.machinelearningservices.models.TriggerType
         :keyword recurrence: Required if triggerType is Recurrence.
-        :paramtype recurrence: ~azure.mgmt.machinelearningservices.models.RecurrenceTrigger
+        :paramtype recurrence: ~azure.mgmt.machinelearningservices.models.Recurrence
         :keyword cron: Required if triggerType is Cron.
-        :paramtype cron: ~azure.mgmt.machinelearningservices.models.CronTrigger
+        :paramtype cron: ~azure.mgmt.machinelearningservices.models.Cron
         :keyword schedule: [Deprecated] Not used any more.
         :paramtype schedule: ~azure.mgmt.machinelearningservices.models.ScheduleBase
         """
@@ -6084,8 +6173,8 @@ def __init__(
         *,
         container_resource_limits: Optional["_models.ContainerResourceSettings"] = None,
         container_resource_requests: Optional["_models.ContainerResourceSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword container_resource_limits: Container resource limit info:.
         :paramtype container_resource_limits:
@@ -6119,7 +6208,9 @@ class ContainerResourceSettings(_serialization.Model):
         "memory": {"key": "memory", "type": "str"},
     }
 
-    def __init__(self, *, cpu: Optional[str] = None, gpu: Optional[str] = None, memory: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, cpu: Optional[str] = None, gpu: Optional[str] = None, memory: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword cpu: Number of vCPUs request/limit for container. More info:
          https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
@@ -6148,7 +6239,7 @@ class CosmosDbSettings(_serialization.Model):
         "collections_throughput": {"key": "collectionsThroughput", "type": "int"},
     }
 
-    def __init__(self, *, collections_throughput: Optional[int] = None, **kwargs):
+    def __init__(self, *, collections_throughput: Optional[int] = None, **kwargs: Any) -> None:
         """
         :keyword collections_throughput: The throughput of the collections in cosmosdb database.
         :paramtype collections_throughput: int
@@ -6157,6 +6248,51 @@ def __init__(self, *, collections_throughput: Optional[int] = None, **kwargs):
         self.collections_throughput = collections_throughput
 
 
+class Cron(_serialization.Model):
+    """The workflow trigger cron for ComputeStartStop schedule type.
+
+    :ivar start_time: The start time in yyyy-MM-ddTHH:mm:ss format.
+    :vartype start_time: str
+    :ivar time_zone: Specifies time zone in which the schedule runs.
+     TimeZone should follow Windows time zone format. Refer:
+     https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+    :vartype time_zone: str
+    :ivar expression: [Required] Specifies cron expression of schedule.
+     The expression should follow NCronTab format.
+    :vartype expression: str
+    """
+
+    _attribute_map = {
+        "start_time": {"key": "startTime", "type": "str"},
+        "time_zone": {"key": "timeZone", "type": "str"},
+        "expression": {"key": "expression", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        start_time: Optional[str] = None,
+        time_zone: str = "UTC",
+        expression: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword start_time: The start time in yyyy-MM-ddTHH:mm:ss format.
+        :paramtype start_time: str
+        :keyword time_zone: Specifies time zone in which the schedule runs.
+         TimeZone should follow Windows time zone format. Refer:
+         https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+        :paramtype time_zone: str
+        :keyword expression: [Required] Specifies cron expression of schedule.
+         The expression should follow NCronTab format.
+        :paramtype expression: str
+        """
+        super().__init__(**kwargs)
+        self.start_time = start_time
+        self.time_zone = time_zone
+        self.expression = expression
+
+
 class TriggerBase(_serialization.Model):
     """TriggerBase.
 
@@ -6195,8 +6331,8 @@ class TriggerBase(_serialization.Model):
     _subtype_map = {"trigger_type": {"Cron": "CronTrigger", "Recurrence": "RecurrenceTrigger"}}
 
     def __init__(
-        self, *, end_time: Optional[str] = None, start_time: Optional[str] = None, time_zone: str = "UTC", **kwargs
-    ):
+        self, *, end_time: Optional[str] = None, start_time: Optional[str] = None, time_zone: str = "UTC", **kwargs: Any
+    ) -> None:
         """
         :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
          https://en.wikipedia.org/wiki/ISO_8601.
@@ -6262,8 +6398,8 @@ def __init__(
         end_time: Optional[str] = None,
         start_time: Optional[str] = None,
         time_zone: str = "UTC",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
          https://en.wikipedia.org/wiki/ISO_8601.
@@ -6308,7 +6444,7 @@ class CustomForecastHorizon(ForecastHorizon):
         "value": {"key": "value", "type": "int"},
     }
 
-    def __init__(self, *, value: int, **kwargs):
+    def __init__(self, *, value: int, **kwargs: Any) -> None:
         """
         :keyword value: [Required] Forecast horizon value. Required.
         :paramtype value: int
@@ -6356,7 +6492,7 @@ class JobInput(_serialization.Model):
         }
     }
 
-    def __init__(self, *, description: Optional[str] = None, **kwargs):
+    def __init__(self, *, description: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -6402,8 +6538,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -6456,7 +6592,7 @@ class JobOutput(_serialization.Model):
         }
     }
 
-    def __init__(self, *, description: Optional[str] = None, **kwargs):
+    def __init__(self, *, description: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
@@ -6499,8 +6635,8 @@ def __init__(
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
@@ -6538,7 +6674,7 @@ class CustomNCrossValidations(NCrossValidations):
         "value": {"key": "value", "type": "int"},
     }
 
-    def __init__(self, *, value: int, **kwargs):
+    def __init__(self, *, value: int, **kwargs: Any) -> None:
         """
         :keyword value: [Required] N-Cross validations value. Required.
         :paramtype value: int
@@ -6569,7 +6705,7 @@ class CustomSeasonality(Seasonality):
         "value": {"key": "value", "type": "int"},
     }
 
-    def __init__(self, *, value: int, **kwargs):
+    def __init__(self, *, value: int, **kwargs: Any) -> None:
         """
         :keyword value: [Required] Seasonality value. Required.
         :paramtype value: int
@@ -6601,7 +6737,7 @@ class CustomTargetLags(TargetLags):
         "values": {"key": "values", "type": "[int]"},
     }
 
-    def __init__(self, *, values: List[int], **kwargs):
+    def __init__(self, *, values: List[int], **kwargs: Any) -> None:
         """
         :keyword values: [Required] Set target lags values. Required.
         :paramtype values: list[int]
@@ -6633,7 +6769,7 @@ class CustomTargetRollingWindowSize(TargetRollingWindowSize):
         "value": {"key": "value", "type": "int"},
     }
 
-    def __init__(self, *, value: int, **kwargs):
+    def __init__(self, *, value: int, **kwargs: Any) -> None:
         """
         :keyword value: [Required] TargetRollingWindowSize value. Required.
         :paramtype value: int
@@ -6654,7 +6790,7 @@ class DatabricksSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "DatabricksProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.DatabricksProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.DatabricksProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Properties of Databricks.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
@@ -6732,8 +6868,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Properties of Databricks.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
@@ -6779,7 +6915,7 @@ class DatabricksComputeSecretsProperties(_serialization.Model):
         "databricks_access_token": {"key": "databricksAccessToken", "type": "str"},
     }
 
-    def __init__(self, *, databricks_access_token: Optional[str] = None, **kwargs):
+    def __init__(self, *, databricks_access_token: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword databricks_access_token: access token for databricks account.
         :paramtype databricks_access_token: str
@@ -6810,7 +6946,7 @@ class DatabricksComputeSecrets(ComputeSecrets, DatabricksComputeSecretsPropertie
         "compute_type": {"key": "computeType", "type": "str"},
     }
 
-    def __init__(self, *, databricks_access_token: Optional[str] = None, **kwargs):
+    def __init__(self, *, databricks_access_token: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword databricks_access_token: access token for databricks account.
         :paramtype databricks_access_token: str
@@ -6834,7 +6970,9 @@ class DatabricksProperties(_serialization.Model):
         "workspace_url": {"key": "workspaceUrl", "type": "str"},
     }
 
-    def __init__(self, *, databricks_access_token: Optional[str] = None, workspace_url: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, databricks_access_token: Optional[str] = None, workspace_url: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword databricks_access_token: Databricks access token.
         :paramtype databricks_access_token: str
@@ -6884,7 +7022,7 @@ class DataContainer(Resource):
         "properties": {"key": "properties", "type": "DataContainerProperties"},
     }
 
-    def __init__(self, *, properties: "_models.DataContainerProperties", **kwargs):
+    def __init__(self, *, properties: "_models.DataContainerProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.DataContainerProperties
@@ -6941,8 +7079,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -6976,8 +7114,8 @@ class DataContainerResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.DataContainer"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.DataContainer"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of DataContainer objects. If null, there are no
          additional pages.
@@ -7055,8 +7193,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword compute_location: Location for the underlying compute.
         :paramtype compute_location: str
@@ -7090,7 +7228,9 @@ class DataLakeAnalyticsSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "DataLakeAnalyticsSchemaProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.DataLakeAnalyticsSchemaProperties"] = None, **kwargs):
+    def __init__(
+        self, *, properties: Optional["_models.DataLakeAnalyticsSchemaProperties"] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword properties:
         :paramtype properties:
@@ -7170,8 +7310,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties:
         :paramtype properties:
@@ -7218,7 +7358,7 @@ class DataLakeAnalyticsSchemaProperties(_serialization.Model):
         "data_lake_store_account_name": {"key": "dataLakeStoreAccountName", "type": "str"},
     }
 
-    def __init__(self, *, data_lake_store_account_name: Optional[str] = None, **kwargs):
+    def __init__(self, *, data_lake_store_account_name: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword data_lake_store_account_name: DataLake Store Account Name.
         :paramtype data_lake_store_account_name: str
@@ -7251,7 +7391,7 @@ class DataPathAssetReference(AssetReferenceBase):
         "path": {"key": "path", "type": "str"},
     }
 
-    def __init__(self, *, datastore_id: Optional[str] = None, path: Optional[str] = None, **kwargs):
+    def __init__(self, *, datastore_id: Optional[str] = None, path: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword datastore_id: ARM resource ID of the datastore where the asset is located.
         :paramtype datastore_id: str
@@ -7302,7 +7442,7 @@ class Datastore(Resource):
         "properties": {"key": "properties", "type": "DatastoreProperties"},
     }
 
-    def __init__(self, *, properties: "_models.DatastoreProperties", **kwargs):
+    def __init__(self, *, properties: "_models.DatastoreProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatastoreProperties
@@ -7326,7 +7466,9 @@ class DatastoreResourceArmPaginatedResult(_serialization.Model):
         "value": {"key": "value", "type": "[Datastore]"},
     }
 
-    def __init__(self, *, next_link: Optional[str] = None, value: Optional[List["_models.Datastore"]] = None, **kwargs):
+    def __init__(
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.Datastore"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of Datastore objects. If null, there are no
          additional pages.
@@ -7377,7 +7519,7 @@ class DataVersionBase(Resource):
         "properties": {"key": "properties", "type": "DataVersionBaseProperties"},
     }
 
-    def __init__(self, *, properties: "_models.DataVersionBaseProperties", **kwargs):
+    def __init__(self, *, properties: "_models.DataVersionBaseProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.DataVersionBaseProperties
@@ -7441,8 +7583,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         is_anonymous: bool = False,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -7487,8 +7629,8 @@ class DataVersionBaseResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.DataVersionBase"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.DataVersionBase"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of DataVersionBase objects. If null, there are no
          additional pages.
@@ -7526,7 +7668,7 @@ class OnlineScaleSettings(_serialization.Model):
         "scale_type": {"Default": "DefaultScaleSettings", "TargetUtilization": "TargetUtilizationScaleSettings"}
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.scale_type: Optional[str] = None
@@ -7550,7 +7692,7 @@ class DefaultScaleSettings(OnlineScaleSettings):
         "scale_type": {"key": "scaleType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.scale_type: str = "Default"
@@ -7567,7 +7709,7 @@ class DeploymentLogs(_serialization.Model):
         "content": {"key": "content", "type": "str"},
     }
 
-    def __init__(self, *, content: Optional[str] = None, **kwargs):
+    def __init__(self, *, content: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword content: The retrieved online deployment logs.
         :paramtype content: str
@@ -7596,8 +7738,8 @@ def __init__(
         *,
         container_type: Optional[Union[str, "_models.ContainerType"]] = None,
         tail: Optional[int] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword container_type: The type of container to retrieve logs from. Known values are:
          "StorageInitializer" and "InferenceServer".
@@ -7633,8 +7775,8 @@ def __init__(
         instance_count: int = 1,
         instance_type: Optional[str] = None,
         properties: Optional[Dict[str, JSON]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword instance_count: Optional number of instances or nodes used by the compute target.
         :paramtype instance_count: int
@@ -7672,8 +7814,8 @@ def __init__(
         instance_count: int = 1,
         instance_type: Optional[str] = None,
         properties: Optional[Dict[str, JSON]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword instance_count: Optional number of instances or nodes used by the compute target.
         :paramtype instance_count: int
@@ -7732,8 +7874,8 @@ def __init__(
         container_registry: Optional[Dict[str, JSON]] = None,
         application_insights: Optional[Dict[str, JSON]] = None,
         others: Optional[Dict[str, JSON]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword udr: Setting for diagnosing user defined routing.
         :paramtype udr: dict[str, JSON]
@@ -7777,7 +7919,7 @@ class DiagnoseResponseResult(_serialization.Model):
         "value": {"key": "value", "type": "DiagnoseResponseResultValue"},
     }
 
-    def __init__(self, *, value: Optional["_models.DiagnoseResponseResultValue"] = None, **kwargs):
+    def __init__(self, *, value: Optional["_models.DiagnoseResponseResultValue"] = None, **kwargs: Any) -> None:
         """
         :keyword value:
         :paramtype value: ~azure.mgmt.machinelearningservices.models.DiagnoseResponseResultValue
@@ -7839,8 +7981,8 @@ def __init__(
         container_registry_results: Optional[List["_models.DiagnoseResult"]] = None,
         application_insights_results: Optional[List["_models.DiagnoseResult"]] = None,
         other_results: Optional[List["_models.DiagnoseResult"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword user_defined_route_results:
         :paramtype user_defined_route_results:
@@ -7906,7 +8048,7 @@ class DiagnoseResult(_serialization.Model):
         "message": {"key": "message", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.code = None
@@ -7925,7 +8067,7 @@ class DiagnoseWorkspaceParameters(_serialization.Model):
         "value": {"key": "value", "type": "DiagnoseRequestProperties"},
     }
 
-    def __init__(self, *, value: Optional["_models.DiagnoseRequestProperties"] = None, **kwargs):
+    def __init__(self, *, value: Optional["_models.DiagnoseRequestProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword value: Value of Parameters.
         :paramtype value: ~azure.mgmt.machinelearningservices.models.DiagnoseRequestProperties
@@ -7957,7 +8099,7 @@ class DistributionConfiguration(_serialization.Model):
 
     _subtype_map = {"distribution_type": {"Mpi": "Mpi", "PyTorch": "PyTorch", "TensorFlow": "TensorFlow"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.distribution_type: Optional[str] = None
@@ -7990,8 +8132,8 @@ class EncryptionKeyVaultProperties(_serialization.Model):
     }
 
     def __init__(
-        self, *, key_vault_arm_id: str, key_identifier: str, identity_client_id: Optional[str] = None, **kwargs
-    ):
+        self, *, key_vault_arm_id: str, key_identifier: str, identity_client_id: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword key_vault_arm_id: The ArmId of the keyVault where the customer owned encryption key is
          present. Required.
@@ -8040,8 +8182,8 @@ def __init__(
         status: Union[str, "_models.EncryptionStatus"],
         key_vault_properties: "_models.EncryptionKeyVaultProperties",
         identity: Optional["_models.IdentityForCmk"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword status: Indicates whether or not the encryption is enabled for the workspace.
          Required. Known values are: "Enabled" and "Disabled".
@@ -8073,7 +8215,9 @@ class EndpointAuthKeys(_serialization.Model):
         "secondary_key": {"key": "secondaryKey", "type": "str"},
     }
 
-    def __init__(self, *, primary_key: Optional[str] = None, secondary_key: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, primary_key: Optional[str] = None, secondary_key: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword primary_key: The primary key.
         :paramtype primary_key: str
@@ -8112,8 +8256,8 @@ def __init__(
         expiry_time_utc: int = 0,
         refresh_after_time_utc: int = 0,
         token_type: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword access_token: Access token for endpoint authentication.
         :paramtype access_token: str
@@ -8154,7 +8298,7 @@ class ScheduleActionBase(_serialization.Model):
 
     _subtype_map = {"action_type": {"CreateJob": "JobScheduleAction", "InvokeBatchEndpoint": "EndpointScheduleAction"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.action_type: Optional[str] = None
@@ -8187,7 +8331,7 @@ class EndpointScheduleAction(ScheduleActionBase):
         "endpoint_invocation_definition": {"key": "endpointInvocationDefinition", "type": "object"},
     }
 
-    def __init__(self, *, endpoint_invocation_definition: JSON, **kwargs):
+    def __init__(self, *, endpoint_invocation_definition: JSON, **kwargs: Any) -> None:
         """
         :keyword endpoint_invocation_definition: [Required] Defines Schedule action definition details.
 
@@ -8240,7 +8384,7 @@ class EnvironmentContainer(Resource):
         "properties": {"key": "properties", "type": "EnvironmentContainerProperties"},
     }
 
-    def __init__(self, *, properties: "_models.EnvironmentContainerProperties", **kwargs):
+    def __init__(self, *, properties: "_models.EnvironmentContainerProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties:
@@ -8290,8 +8434,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -8321,8 +8465,12 @@ class EnvironmentContainerResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.EnvironmentContainer"]] = None, **kwargs
-    ):
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["_models.EnvironmentContainer"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of EnvironmentContainer objects. If null, there
          are no additional pages.
@@ -8373,7 +8521,7 @@ class EnvironmentVersion(Resource):
         "properties": {"key": "properties", "type": "EnvironmentVersionProperties"},
     }
 
-    def __init__(self, *, properties: "_models.EnvironmentVersionProperties", **kwargs):
+    def __init__(self, *, properties: "_models.EnvironmentVersionProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.EnvironmentVersionProperties
@@ -8471,8 +8619,8 @@ def __init__(
         image: Optional[str] = None,
         inference_config: Optional["_models.InferenceContainerProperties"] = None,
         os_type: Optional[Union[str, "_models.OperatingSystemType"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -8547,8 +8695,12 @@ class EnvironmentVersionResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.EnvironmentVersion"]] = None, **kwargs
-    ):
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["_models.EnvironmentVersion"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of EnvironmentVersion objects. If null, there are
          no additional pages.
@@ -8582,7 +8734,7 @@ class ErrorAdditionalInfo(_serialization.Model):
         "info": {"key": "info", "type": "object"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.type = None
@@ -8622,7 +8774,7 @@ class ErrorDetail(_serialization.Model):
         "additional_info": {"key": "additionalInfo", "type": "[ErrorAdditionalInfo]"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.code = None
@@ -8633,7 +8785,8 @@ def __init__(self, **kwargs):
 
 
 class ErrorResponse(_serialization.Model):
-    """Common error response for all Azure Resource Manager APIs to return error details for failed operations. (This also follows the OData error response format.).
+    """Common error response for all Azure Resource Manager APIs to return error details for failed
+    operations. (This also follows the OData error response format.).
 
     :ivar error: The error object.
     :vartype error: ~azure.mgmt.machinelearningservices.models.ErrorDetail
@@ -8643,7 +8796,7 @@ class ErrorResponse(_serialization.Model):
         "error": {"key": "error", "type": "ErrorDetail"},
     }
 
-    def __init__(self, *, error: Optional["_models.ErrorDetail"] = None, **kwargs):
+    def __init__(self, *, error: Optional["_models.ErrorDetail"] = None, **kwargs: Any) -> None:
         """
         :keyword error: The error object.
         :paramtype error: ~azure.mgmt.machinelearningservices.models.ErrorDetail
@@ -8685,8 +8838,8 @@ def __init__(
         retail_price: float,
         os_type: Union[str, "_models.VMPriceOSType"],
         vm_tier: Union[str, "_models.VMTier"],
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword retail_price: The price charged for using the VM. Required.
         :paramtype retail_price: float
@@ -8737,8 +8890,8 @@ def __init__(
         billing_currency: Union[str, "_models.BillingCurrency"],
         unit_of_measure: Union[str, "_models.UnitOfMeasure"],
         values: List["_models.EstimatedVMPrice"],
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword billing_currency: Three lettered code specifying the currency of the VM price.
          Example: USD. Required. "USD"
@@ -8767,7 +8920,7 @@ class ExternalFQDNResponse(_serialization.Model):
         "value": {"key": "value", "type": "[FQDNEndpoints]"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.FQDNEndpoints"]] = None, **kwargs):
+    def __init__(self, *, value: Optional[List["_models.FQDNEndpoints"]] = None, **kwargs: Any) -> None:
         """
         :keyword value:
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.FQDNEndpoints]
@@ -8787,7 +8940,7 @@ class FeaturizationSettings(_serialization.Model):
         "dataset_language": {"key": "datasetLanguage", "type": "str"},
     }
 
-    def __init__(self, *, dataset_language: Optional[str] = None, **kwargs):
+    def __init__(self, *, dataset_language: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword dataset_language: Dataset language, useful for the text data.
         :paramtype dataset_language: str
@@ -8807,7 +8960,7 @@ class FlavorData(_serialization.Model):
         "data": {"key": "data", "type": "{str}"},
     }
 
-    def __init__(self, *, data: Optional[Dict[str, str]] = None, **kwargs):
+    def __init__(self, *, data: Optional[Dict[str, str]] = None, **kwargs: Any) -> None:
         """
         :keyword data: Model flavor-specific data.
         :paramtype data: dict[str, str]
@@ -8915,8 +9068,8 @@ def __init__(
         forecasting_settings: Optional["_models.ForecastingSettings"] = None,
         primary_metric: Optional[Union[str, "_models.ForecastingPrimaryMetrics"]] = None,
         training_settings: Optional["_models.ForecastingTrainingSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -9085,8 +9238,8 @@ def __init__(
         time_column_name: Optional[str] = None,
         time_series_id_column_names: Optional[List[str]] = None,
         use_stl: Optional[Union[str, "_models.UseStl"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword country_or_region_for_holidays: Country or region for holidays for forecasting tasks.
          These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
@@ -9208,8 +9361,8 @@ def __init__(
         stack_ensemble_settings: Optional["_models.StackEnsembleSettings"] = None,
         allowed_training_algorithms: Optional[List[Union[str, "_models.ForecastingModels"]]] = None,
         blocked_training_algorithms: Optional[List[Union[str, "_models.ForecastingModels"]]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword enable_dnn_training: Enable recommendation of DNN models.
         :paramtype enable_dnn_training: bool
@@ -9268,8 +9421,8 @@ def __init__(
         *,
         domain_name: Optional[str] = None,
         endpoint_details: Optional[List["_models.FQDNEndpointDetail"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword domain_name:
         :paramtype domain_name: str
@@ -9293,7 +9446,7 @@ class FQDNEndpointDetail(_serialization.Model):
         "port": {"key": "port", "type": "int"},
     }
 
-    def __init__(self, *, port: Optional[int] = None, **kwargs):
+    def __init__(self, *, port: Optional[int] = None, **kwargs: Any) -> None:
         """
         :keyword port:
         :paramtype port: int
@@ -9313,7 +9466,7 @@ class FQDNEndpoints(_serialization.Model):
         "properties": {"key": "properties", "type": "FQDNEndpointsProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.FQDNEndpointsProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.FQDNEndpointsProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties:
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.FQDNEndpointsProperties
@@ -9337,8 +9490,8 @@ class FQDNEndpointsProperties(_serialization.Model):
     }
 
     def __init__(
-        self, *, category: Optional[str] = None, endpoints: Optional[List["_models.FQDNEndpoint"]] = None, **kwargs
-    ):
+        self, *, category: Optional[str] = None, endpoints: Optional[List["_models.FQDNEndpoint"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword category:
         :paramtype category: str
@@ -9370,7 +9523,7 @@ class GridSamplingAlgorithm(SamplingAlgorithm):
         "sampling_algorithm_type": {"key": "samplingAlgorithmType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.sampling_algorithm_type: str = "Grid"
@@ -9387,7 +9540,7 @@ class HDInsightSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "HDInsightProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.HDInsightProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.HDInsightProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: HDInsight compute properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
@@ -9465,8 +9618,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: HDInsight compute properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
@@ -9525,8 +9678,8 @@ def __init__(
         ssh_port: Optional[int] = None,
         address: Optional[str] = None,
         administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword ssh_port: Port open for ssh connections on the master node of the cluster.
         :paramtype ssh_port: int
@@ -9564,7 +9717,7 @@ class IdAssetReference(AssetReferenceBase):
         "asset_id": {"key": "assetId", "type": "str"},
     }
 
-    def __init__(self, *, asset_id: str, **kwargs):
+    def __init__(self, *, asset_id: str, **kwargs: Any) -> None:
         """
         :keyword asset_id: [Required] ARM resource ID of the asset. Required.
         :paramtype asset_id: str
@@ -9586,7 +9739,7 @@ class IdentityForCmk(_serialization.Model):
         "user_assigned_identity": {"key": "userAssignedIdentity", "type": "str"},
     }
 
-    def __init__(self, *, user_assigned_identity: Optional[str] = None, **kwargs):
+    def __init__(self, *, user_assigned_identity: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword user_assigned_identity: The ArmId of the user assigned identity that will be used to
          access the customer managed key vault.
@@ -9598,21 +9751,22 @@ def __init__(self, *, user_assigned_identity: Optional[str] = None, **kwargs):
 
 class ImageVertical(_serialization.Model):
     """Abstract class for AutoML tasks that train image (computer vision) models -
-    such as Image Classification / Image Classification Multilabel / Image Object Detection / Image Instance Segmentation.
+    such as Image Classification / Image Classification Multilabel / Image Object Detection / Image
+    Instance Segmentation.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
-        :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
-        :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
-         validation purpose.
-         Values between (0.0 , 1.0)
-         Applied when validation dataset is not provided.
-        :vartype validation_data_size: float
+    :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
     """
 
     _validation = {
@@ -9633,8 +9787,8 @@ def __init__(
         sweep_settings: Optional["_models.ImageSweepSettings"] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
         validation_data_size: Optional[float] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword limit_settings: [Required] Limit settings for the AutoML job. Required.
         :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
@@ -9702,8 +9856,8 @@ def __init__(
         validation_data_size: Optional[float] = None,
         model_settings: Optional["_models.ImageModelSettingsClassification"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsClassification"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword limit_settings: [Required] Limit settings for the AutoML job. Required.
         :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
@@ -9736,47 +9890,49 @@ def __init__(
 
 
 class ImageClassification(ImageClassificationBase, AutoMLVertical):  # pylint: disable=too-many-instance-attributes
-    """Image Classification. Multi-class image classification is used when an image is classified with only a single label
-    from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog' or a 'duck'.
+    """Image Classification. Multi-class image classification is used when an image is classified with
+    only a single label
+    from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog'
+    or a 'duck'.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
-        :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
-        :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
-         validation purpose.
-         Values between (0.0 , 1.0)
-         Applied when validation dataset is not provided.
-        :vartype validation_data_size: float
-        :ivar model_settings: Settings used for training the model.
-        :vartype model_settings:
-         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
-        :ivar search_space: Search space for sampling different combinations of models and their
-         hyperparameters.
-        :vartype search_space:
-         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
-        :ivar primary_metric: Primary metric to optimize for this task. Known values are:
-         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
-         "PrecisionScoreWeighted".
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+    :ivar primary_metric: Primary metric to optimize for this task. Known values are:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
     """
 
     _validation = {
@@ -9812,8 +9968,8 @@ def __init__(
         model_settings: Optional["_models.ImageModelSettingsClassification"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsClassification"]] = None,
         primary_metric: Optional[Union[str, "_models.ClassificationPrimaryMetrics"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -9875,57 +10031,58 @@ def __init__(
 class ImageClassificationMultilabel(
     ImageClassificationBase, AutoMLVertical
 ):  # pylint: disable=too-many-instance-attributes
-    """Image Classification Multilabel. Multi-label image classification is used when an image could have one or more labels
+    """Image Classification Multilabel. Multi-label image classification is used when an image could
+    have one or more labels
     from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.
 
-        All required parameters must be populated in order to send to Azure.
-
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
-        :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
-        :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
-         validation purpose.
-         Values between (0.0 , 1.0)
-         Applied when validation dataset is not provided.
-        :vartype validation_data_size: float
-        :ivar model_settings: Settings used for training the model.
-        :vartype model_settings:
-         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
-        :ivar search_space: Search space for sampling different combinations of models and their
-         hyperparameters.
-        :vartype search_space:
-         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
-        :ivar primary_metric: Primary metric to optimize for this task. Known values are:
-         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
-         "PrecisionScoreWeighted", and "IOU".
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
-    """
-
-    _validation = {
-        "task_type": {"required": True},
-        "training_data": {"required": True},
-        "limit_settings": {"required": True},
-    }
+    All required parameters must be populated in order to send to Azure.
 
-    _attribute_map = {
-        "log_verbosity": {"key": "logVerbosity", "type": "str"},
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+    :ivar primary_metric: Primary metric to optimize for this task. Known values are:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+     "PrecisionScoreWeighted", and "IOU".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
+    """
+
+    _validation = {
+        "task_type": {"required": True},
+        "training_data": {"required": True},
+        "limit_settings": {"required": True},
+    }
+
+    _attribute_map = {
+        "log_verbosity": {"key": "logVerbosity", "type": "str"},
         "target_column_name": {"key": "targetColumnName", "type": "str"},
         "task_type": {"key": "taskType", "type": "str"},
         "training_data": {"key": "trainingData", "type": "MLTableJobInput"},
@@ -9951,8 +10108,8 @@ def __init__(
         model_settings: Optional["_models.ImageModelSettingsClassification"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsClassification"]] = None,
         primary_metric: Optional[Union[str, "_models.ClassificationMultilabelPrimaryMetrics"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -10058,8 +10215,8 @@ def __init__(
         validation_data_size: Optional[float] = None,
         model_settings: Optional["_models.ImageModelSettingsObjectDetection"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsObjectDetection"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword limit_settings: [Required] Limit settings for the AutoML job. Required.
         :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
@@ -10094,45 +10251,46 @@ def __init__(
 class ImageInstanceSegmentation(
     ImageObjectDetectionBase, AutoMLVertical
 ):  # pylint: disable=too-many-instance-attributes
-    """Image Instance Segmentation. Instance segmentation is used to identify objects in an image at the pixel level,
+    """Image Instance Segmentation. Instance segmentation is used to identify objects in an image at
+    the pixel level,
     drawing a polygon around each object in the image.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
-        :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
-        :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
-         validation purpose.
-         Values between (0.0 , 1.0)
-         Applied when validation dataset is not provided.
-        :vartype validation_data_size: float
-        :ivar model_settings: Settings used for training the model.
-        :vartype model_settings:
-         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
-        :ivar search_space: Search space for sampling different combinations of models and their
-         hyperparameters.
-        :vartype search_space:
-         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
-        :ivar primary_metric: Primary metric to optimize for this task. "MeanAveragePrecision"
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.InstanceSegmentationPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+    :ivar primary_metric: Primary metric to optimize for this task. "MeanAveragePrecision"
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.InstanceSegmentationPrimaryMetrics
     """
 
     _validation = {
@@ -10168,8 +10326,8 @@ def __init__(
         model_settings: Optional["_models.ImageModelSettingsObjectDetection"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsObjectDetection"]] = None,
         primary_metric: Optional[Union[str, "_models.InstanceSegmentationPrimaryMetrics"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -10244,8 +10402,8 @@ class ImageLimitSettings(_serialization.Model):
     }
 
     def __init__(
-        self, *, max_concurrent_trials: int = 1, max_trials: int = 1, timeout: datetime.timedelta = "P7D", **kwargs
-    ):
+        self, *, max_concurrent_trials: int = 1, max_trials: int = 1, timeout: datetime.timedelta = "P7D", **kwargs: Any
+    ) -> None:
         """
         :keyword max_concurrent_trials: Maximum number of concurrent AutoML iterations.
         :paramtype max_concurrent_trials: int
@@ -10270,91 +10428,92 @@ class ImageModelDistributionSettings(_serialization.Model):  # pylint: disable=t
     LearningRate = "uniform(0.001, 0.01)";
     LayersToFreeze = "choice(0, 2)";
     </code></example>`
-    All distributions can be specified as distribution_name(min, max) or choice(val1, val2, ..., valn)
+    All distributions can be specified as distribution_name(min, max) or choice(val1, val2, ...,
+    valn)
     where distribution name can be: uniform, quniform, loguniform, etc
     For more details on how to compose distribution expressions please check the documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: str
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: str
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: str
-        :ivar distributed: Whether to use distributer training.
-        :vartype distributed: str
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: str
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: str
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: str
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: str
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: str
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: str
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: str
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: str
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'.
-        :vartype learning_rate_scheduler: str
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: str
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: str
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: str
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: str
-        :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
-        :vartype optimizer: str
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: str
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: str
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: str
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: str
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: str
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: str
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: str
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
     """
 
     _attribute_map = {
@@ -10419,8 +10578,8 @@ def __init__(  # pylint: disable=too-many-locals
         warmup_cosine_lr_cycles: Optional[str] = None,
         warmup_cosine_lr_warmup_epochs: Optional[str] = None,
         weight_decay: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
         :paramtype ams_gradient: str
@@ -10551,97 +10710,97 @@ class ImageModelDistributionSettingsClassification(
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: str
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: str
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: str
-        :ivar distributed: Whether to use distributer training.
-        :vartype distributed: str
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: str
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: str
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: str
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: str
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: str
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: str
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: str
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: str
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'.
-        :vartype learning_rate_scheduler: str
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: str
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: str
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: str
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: str
-        :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
-        :vartype optimizer: str
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: str
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: str
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: str
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: str
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: str
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: str
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: str
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: str
-        :ivar training_crop_size: Image crop size that is input to the neural network for the training
-         dataset. Must be a positive integer.
-        :vartype training_crop_size: str
-        :ivar validation_crop_size: Image crop size that is input to the neural network for the
-         validation dataset. Must be a positive integer.
-        :vartype validation_crop_size: str
-        :ivar validation_resize_size: Image size to which to resize before cropping for validation
-         dataset. Must be a positive integer.
-        :vartype validation_resize_size: str
-        :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
-         1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
-         0 or 1 or 2.
-        :vartype weighted_loss: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
+    :ivar training_crop_size: Image crop size that is input to the neural network for the training
+     dataset. Must be a positive integer.
+    :vartype training_crop_size: str
+    :ivar validation_crop_size: Image crop size that is input to the neural network for the
+     validation dataset. Must be a positive integer.
+    :vartype validation_crop_size: str
+    :ivar validation_resize_size: Image size to which to resize before cropping for validation
+     dataset. Must be a positive integer.
+    :vartype validation_resize_size: str
+    :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+     1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+     0 or 1 or 2.
+    :vartype weighted_loss: str
     """
 
     _attribute_map = {
@@ -10714,8 +10873,8 @@ def __init__(  # pylint: disable=too-many-locals
         validation_crop_size: Optional[str] = None,
         validation_resize_size: Optional[str] = None,
         weighted_loss: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
         :paramtype ams_gradient: str
@@ -10865,136 +11024,136 @@ class ImageModelDistributionSettingsObjectDetection(
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: str
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: str
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: str
-        :ivar distributed: Whether to use distributer training.
-        :vartype distributed: str
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: str
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: str
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: str
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: str
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: str
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: str
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: str
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: str
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'.
-        :vartype learning_rate_scheduler: str
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: str
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: str
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: str
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: str
-        :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
-        :vartype optimizer: str
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: str
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: str
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: str
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: str
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: str
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: str
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: str
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: str
-        :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
-         be a positive integer.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype box_detections_per_image: str
-        :ivar box_score_threshold: During inference, only return proposals with a classification score
-         greater than
-         BoxScoreThreshold. Must be a float in the range[0, 1].
-        :vartype box_score_threshold: str
-        :ivar image_size: Image size for train and validation. Must be a positive integer.
-         Note: The training run may get into CUDA OOM if the size is too big.
-         Note: This settings is only supported for the 'yolov5' algorithm.
-        :vartype image_size: str
-        :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
-         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype max_size: str
-        :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
-         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype min_size: str
-        :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
-         Note: training run may get into CUDA OOM if the model size is too big.
-         Note: This settings is only supported for the 'yolov5' algorithm.
-        :vartype model_size: str
-        :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
-         Note: training run may get into CUDA OOM if no sufficient GPU memory.
-         Note: This settings is only supported for the 'yolov5' algorithm.
-        :vartype multi_scale: str
-        :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
-         float in the range [0, 1].
-        :vartype nms_iou_threshold: str
-        :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
-         be
-         None to enable small object detection logic. A string containing two integers in mxn format.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype tile_grid_size: str
-        :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
-         in the range [0, 1).
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype tile_overlap_ratio: str
-        :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
-         predictions from tiles and image.
-         Used in validation/ inference. Must be float in the range [0, 1].
-         Note: This settings is not supported for the 'yolov5' algorithm.
-         NMS: Non-maximum suppression.
-        :vartype tile_predictions_nms_threshold: str
-        :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
-         float in the range [0, 1].
-        :vartype validation_iou_threshold: str
-        :ivar validation_metric_type: Metric computation method to use for validation metrics. Must be
-         'none', 'coco', 'voc', or 'coco_voc'.
-        :vartype validation_metric_type: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
+    :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
+     be a positive integer.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype box_detections_per_image: str
+    :ivar box_score_threshold: During inference, only return proposals with a classification score
+     greater than
+     BoxScoreThreshold. Must be a float in the range[0, 1].
+    :vartype box_score_threshold: str
+    :ivar image_size: Image size for train and validation. Must be a positive integer.
+     Note: The training run may get into CUDA OOM if the size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype image_size: str
+    :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype max_size: str
+    :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype min_size: str
+    :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+     Note: training run may get into CUDA OOM if the model size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype model_size: str
+    :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+     Note: training run may get into CUDA OOM if no sufficient GPU memory.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype multi_scale: str
+    :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
+     float in the range [0, 1].
+    :vartype nms_iou_threshold: str
+    :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
+     be
+     None to enable small object detection logic. A string containing two integers in mxn format.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_grid_size: str
+    :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
+     in the range [0, 1).
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_overlap_ratio: str
+    :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+     predictions from tiles and image.
+     Used in validation/ inference. Must be float in the range [0, 1].
+     Note: This settings is not supported for the 'yolov5' algorithm.
+     NMS: Non-maximum suppression.
+    :vartype tile_predictions_nms_threshold: str
+    :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
+     float in the range [0, 1].
+    :vartype validation_iou_threshold: str
+    :ivar validation_metric_type: Metric computation method to use for validation metrics. Must be
+     'none', 'coco', 'voc', or 'coco_voc'.
+    :vartype validation_metric_type: str
     """
 
     _attribute_map = {
@@ -11085,8 +11244,8 @@ def __init__(  # pylint: disable=too-many-locals
         tile_predictions_nms_threshold: Optional[str] = None,
         validation_iou_threshold: Optional[str] = None,
         validation_metric_type: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
         :paramtype ams_gradient: str
@@ -11272,94 +11431,94 @@ class ImageModelSettings(_serialization.Model):  # pylint: disable=too-many-inst
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar advanced_settings: Settings for advanced scenarios.
-        :vartype advanced_settings: str
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: bool
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: float
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: float
-        :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
-        :vartype checkpoint_frequency: int
-        :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
-        :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
-        :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
-         incremental training.
-        :vartype checkpoint_run_id: str
-        :ivar distributed: Whether to use distributed training.
-        :vartype distributed: bool
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: bool
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: int
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: int
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: bool
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: int
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: int
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: int
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: float
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'. Known values are: "None", "WarmupCosine", and "Step".
-        :vartype learning_rate_scheduler: str or
-         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: float
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: bool
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: int
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: int
-        :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
-        :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: int
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: float
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: int
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: int
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: int
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: float
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: int
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: float
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Known values are: "None", "WarmupCosine", and "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
     """
 
     _attribute_map = {
@@ -11432,8 +11591,8 @@ def __init__(  # pylint: disable=too-many-locals
         warmup_cosine_lr_cycles: Optional[float] = None,
         warmup_cosine_lr_warmup_epochs: Optional[int] = None,
         weight_decay: Optional[float] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword advanced_settings: Settings for advanced scenarios.
         :paramtype advanced_settings: str
@@ -11567,107 +11726,107 @@ class ImageModelSettingsClassification(ImageModelSettings):  # pylint: disable=t
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar advanced_settings: Settings for advanced scenarios.
-        :vartype advanced_settings: str
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: bool
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: float
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: float
-        :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
-        :vartype checkpoint_frequency: int
-        :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
-        :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
-        :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
-         incremental training.
-        :vartype checkpoint_run_id: str
-        :ivar distributed: Whether to use distributed training.
-        :vartype distributed: bool
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: bool
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: int
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: int
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: bool
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: int
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: int
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: int
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: float
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'. Known values are: "None", "WarmupCosine", and "Step".
-        :vartype learning_rate_scheduler: str or
-         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: float
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: bool
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: int
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: int
-        :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
-        :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: int
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: float
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: int
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: int
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: int
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: float
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: int
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: float
-        :ivar training_crop_size: Image crop size that is input to the neural network for the training
-         dataset. Must be a positive integer.
-        :vartype training_crop_size: int
-        :ivar validation_crop_size: Image crop size that is input to the neural network for the
-         validation dataset. Must be a positive integer.
-        :vartype validation_crop_size: int
-        :ivar validation_resize_size: Image size to which to resize before cropping for validation
-         dataset. Must be a positive integer.
-        :vartype validation_resize_size: int
-        :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
-         1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
-         0 or 1 or 2.
-        :vartype weighted_loss: int
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Known values are: "None", "WarmupCosine", and "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
+    :ivar training_crop_size: Image crop size that is input to the neural network for the training
+     dataset. Must be a positive integer.
+    :vartype training_crop_size: int
+    :ivar validation_crop_size: Image crop size that is input to the neural network for the
+     validation dataset. Must be a positive integer.
+    :vartype validation_crop_size: int
+    :ivar validation_resize_size: Image size to which to resize before cropping for validation
+     dataset. Must be a positive integer.
+    :vartype validation_resize_size: int
+    :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+     1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+     0 or 1 or 2.
+    :vartype weighted_loss: int
     """
 
     _attribute_map = {
@@ -11748,8 +11907,8 @@ def __init__(  # pylint: disable=too-many-locals
         validation_crop_size: Optional[int] = None,
         validation_resize_size: Optional[int] = None,
         weighted_loss: Optional[int] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword advanced_settings: Settings for advanced scenarios.
         :paramtype advanced_settings: str
@@ -11902,147 +12061,147 @@ class ImageModelSettingsObjectDetection(ImageModelSettings):  # pylint: disable=
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar advanced_settings: Settings for advanced scenarios.
-        :vartype advanced_settings: str
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: bool
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: float
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: float
-        :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
-        :vartype checkpoint_frequency: int
-        :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
-        :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
-        :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
-         incremental training.
-        :vartype checkpoint_run_id: str
-        :ivar distributed: Whether to use distributed training.
-        :vartype distributed: bool
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: bool
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: int
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: int
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: bool
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: int
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: int
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: int
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: float
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'. Known values are: "None", "WarmupCosine", and "Step".
-        :vartype learning_rate_scheduler: str or
-         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: float
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: bool
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: int
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: int
-        :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
-        :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: int
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: float
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: int
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: int
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: int
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: float
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: int
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: float
-        :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
-         be a positive integer.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype box_detections_per_image: int
-        :ivar box_score_threshold: During inference, only return proposals with a classification score
-         greater than
-         BoxScoreThreshold. Must be a float in the range[0, 1].
-        :vartype box_score_threshold: float
-        :ivar image_size: Image size for train and validation. Must be a positive integer.
-         Note: The training run may get into CUDA OOM if the size is too big.
-         Note: This settings is only supported for the 'yolov5' algorithm.
-        :vartype image_size: int
-        :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
-         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype max_size: int
-        :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
-         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype min_size: int
-        :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
-         Note: training run may get into CUDA OOM if the model size is too big.
-         Note: This settings is only supported for the 'yolov5' algorithm. Known values are: "None",
-         "Small", "Medium", "Large", and "ExtraLarge".
-        :vartype model_size: str or ~azure.mgmt.machinelearningservices.models.ModelSize
-        :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
-         Note: training run may get into CUDA OOM if no sufficient GPU memory.
-         Note: This settings is only supported for the 'yolov5' algorithm.
-        :vartype multi_scale: bool
-        :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be a
-         float in the range [0, 1].
-        :vartype nms_iou_threshold: float
-        :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
-         be
-         None to enable small object detection logic. A string containing two integers in mxn format.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype tile_grid_size: str
-        :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
-         in the range [0, 1).
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype tile_overlap_ratio: float
-        :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
-         predictions from tiles and image.
-         Used in validation/ inference. Must be float in the range [0, 1].
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype tile_predictions_nms_threshold: float
-        :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
-         float in the range [0, 1].
-        :vartype validation_iou_threshold: float
-        :ivar validation_metric_type: Metric computation method to use for validation metrics. Known
-         values are: "None", "Coco", "Voc", and "CocoVoc".
-        :vartype validation_metric_type: str or
-         ~azure.mgmt.machinelearningservices.models.ValidationMetricType
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Known values are: "None", "WarmupCosine", and "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
+    :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
+     be a positive integer.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype box_detections_per_image: int
+    :ivar box_score_threshold: During inference, only return proposals with a classification score
+     greater than
+     BoxScoreThreshold. Must be a float in the range[0, 1].
+    :vartype box_score_threshold: float
+    :ivar image_size: Image size for train and validation. Must be a positive integer.
+     Note: The training run may get into CUDA OOM if the size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype image_size: int
+    :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype max_size: int
+    :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype min_size: int
+    :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+     Note: training run may get into CUDA OOM if the model size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm. Known values are: "None",
+     "Small", "Medium", "Large", and "ExtraLarge".
+    :vartype model_size: str or ~azure.mgmt.machinelearningservices.models.ModelSize
+    :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+     Note: training run may get into CUDA OOM if no sufficient GPU memory.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype multi_scale: bool
+    :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be a
+     float in the range [0, 1].
+    :vartype nms_iou_threshold: float
+    :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
+     be
+     None to enable small object detection logic. A string containing two integers in mxn format.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_grid_size: str
+    :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
+     in the range [0, 1).
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_overlap_ratio: float
+    :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+     predictions from tiles and image.
+     Used in validation/ inference. Must be float in the range [0, 1].
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_predictions_nms_threshold: float
+    :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
+     float in the range [0, 1].
+    :vartype validation_iou_threshold: float
+    :ivar validation_metric_type: Metric computation method to use for validation metrics. Known
+     values are: "None", "Coco", "Voc", and "CocoVoc".
+    :vartype validation_metric_type: str or
+     ~azure.mgmt.machinelearningservices.models.ValidationMetricType
     """
 
     _attribute_map = {
@@ -12141,8 +12300,8 @@ def __init__(  # pylint: disable=too-many-locals
         tile_predictions_nms_threshold: Optional[float] = None,
         validation_iou_threshold: Optional[float] = None,
         validation_metric_type: Optional[Union[str, "_models.ValidationMetricType"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword advanced_settings: Settings for advanced scenarios.
         :paramtype advanced_settings: str
@@ -12340,45 +12499,46 @@ def __init__(  # pylint: disable=too-many-locals
 
 
 class ImageObjectDetection(ImageObjectDetectionBase, AutoMLVertical):  # pylint: disable=too-many-instance-attributes
-    """Image Object Detection. Object detection is used to identify objects in an image and locate each object with a
+    """Image Object Detection. Object detection is used to identify objects in an image and locate
+    each object with a
     bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
-        :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
-        :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
-         validation purpose.
-         Values between (0.0 , 1.0)
-         Applied when validation dataset is not provided.
-        :vartype validation_data_size: float
-        :ivar model_settings: Settings used for training the model.
-        :vartype model_settings:
-         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
-        :ivar search_space: Search space for sampling different combinations of models and their
-         hyperparameters.
-        :vartype search_space:
-         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
-        :ivar primary_metric: Primary metric to optimize for this task. "MeanAveragePrecision"
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ObjectDetectionPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+    :ivar primary_metric: Primary metric to optimize for this task. "MeanAveragePrecision"
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ObjectDetectionPrimaryMetrics
     """
 
     _validation = {
@@ -12414,8 +12574,8 @@ def __init__(
         model_settings: Optional["_models.ImageModelSettingsObjectDetection"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsObjectDetection"]] = None,
         primary_metric: Optional[Union[str, "_models.ObjectDetectionPrimaryMetrics"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -12499,8 +12659,8 @@ def __init__(
         *,
         sampling_algorithm: Union[str, "_models.SamplingAlgorithmType"],
         early_termination: Optional["_models.EarlyTerminationPolicy"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword early_termination: Type of early termination policy.
         :paramtype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
@@ -12538,8 +12698,8 @@ def __init__(
         liveness_route: Optional["_models.Route"] = None,
         readiness_route: Optional["_models.Route"] = None,
         scoring_route: Optional["_models.Route"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword liveness_route: The route to check the liveness of the inference server container.
         :paramtype liveness_route: ~azure.mgmt.machinelearningservices.models.Route
@@ -12574,8 +12734,8 @@ def __init__(
         *,
         node_selector: Optional[Dict[str, str]] = None,
         resources: Optional["_models.InstanceTypeSchemaResources"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword node_selector: Node Selector.
         :paramtype node_selector: dict[str, str]
@@ -12601,7 +12761,9 @@ class InstanceTypeSchemaResources(_serialization.Model):
         "limits": {"key": "limits", "type": "{str}"},
     }
 
-    def __init__(self, *, requests: Optional[Dict[str, str]] = None, limits: Optional[Dict[str, str]] = None, **kwargs):
+    def __init__(
+        self, *, requests: Optional[Dict[str, str]] = None, limits: Optional[Dict[str, str]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword requests: Resource requests for this instance type.
         :paramtype requests: dict[str, str]
@@ -12651,7 +12813,7 @@ class JobBase(Resource):
         "properties": {"key": "properties", "type": "JobBaseProperties"},
     }
 
-    def __init__(self, *, properties: "_models.JobBaseProperties", **kwargs):
+    def __init__(self, *, properties: "_models.JobBaseProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
@@ -12675,7 +12837,9 @@ class JobBaseResourceArmPaginatedResult(_serialization.Model):
         "value": {"key": "value", "type": "[JobBase]"},
     }
 
-    def __init__(self, *, next_link: Optional[str] = None, value: Optional[List["_models.JobBase"]] = None, **kwargs):
+    def __init__(
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.JobBase"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of JobBase objects. If null, there are no
          additional pages.
@@ -12727,8 +12891,8 @@ def __init__(
         properties: Optional[Dict[str, JSON]] = None,
         docker_args: Optional[str] = None,
         shm_size: str = "2g",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword instance_count: Optional number of instances or nodes used by the compute target.
         :paramtype instance_count: int
@@ -12772,7 +12936,7 @@ class JobScheduleAction(ScheduleActionBase):
         "job_definition": {"key": "jobDefinition", "type": "JobBaseProperties"},
     }
 
-    def __init__(self, *, job_definition: "_models.JobBaseProperties", **kwargs):
+    def __init__(self, *, job_definition: "_models.JobBaseProperties", **kwargs: Any) -> None:
         """
         :keyword job_definition: [Required] Defines Schedule action definition details. Required.
         :paramtype job_definition: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
@@ -12822,8 +12986,8 @@ def __init__(
         job_service_type: Optional[str] = None,
         port: Optional[int] = None,
         properties: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword endpoint: Url for endpoint.
         :paramtype endpoint: str
@@ -12854,7 +13018,7 @@ class KubernetesSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "KubernetesProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.KubernetesProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.KubernetesProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Properties of Kubernetes.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
@@ -12932,8 +13096,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Properties of Kubernetes.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
@@ -13069,8 +13233,8 @@ def __init__(
         readiness_probe: Optional["_models.ProbeSettings"] = None,
         request_settings: Optional["_models.OnlineRequestSettings"] = None,
         scale_settings: Optional["_models.OnlineScaleSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_configuration: Code configuration for the endpoint deployment.
         :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
@@ -13233,8 +13397,8 @@ def __init__(
         request_settings: Optional["_models.OnlineRequestSettings"] = None,
         scale_settings: Optional["_models.OnlineScaleSettings"] = None,
         container_resource_requirements: Optional["_models.ContainerResourceRequirements"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_configuration: Code configuration for the endpoint deployment.
         :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
@@ -13342,8 +13506,8 @@ def __init__(
         namespace: str = "default",
         default_instance_type: Optional[str] = None,
         instance_types: Optional[Dict[str, "_models.InstanceTypeSchema"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword relay_connection_string: Relay connection string.
         :paramtype relay_connection_string: str
@@ -13396,7 +13560,7 @@ class ListAmlUserFeatureResult(_serialization.Model):
         "next_link": {"key": "nextLink", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.value = None
@@ -13424,7 +13588,7 @@ class ListNotebookKeysResult(_serialization.Model):
         "secondary_access_key": {"key": "secondaryAccessKey", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.primary_access_key = None
@@ -13448,7 +13612,7 @@ class ListStorageAccountKeysResult(_serialization.Model):
         "user_storage_key": {"key": "userStorageKey", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.user_storage_key = None
@@ -13476,7 +13640,7 @@ class ListUsagesResult(_serialization.Model):
         "next_link": {"key": "nextLink", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.value = None
@@ -13521,7 +13685,7 @@ class ListWorkspaceKeysResult(_serialization.Model):
         "notebook_access_keys": {"key": "notebookAccessKeys", "type": "ListNotebookKeysResult"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.user_storage_key = None
@@ -13553,7 +13717,7 @@ class ListWorkspaceQuotas(_serialization.Model):
         "next_link": {"key": "nextLink", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.value = None
@@ -13586,7 +13750,7 @@ class LiteralJobInput(JobInput):
         "value": {"key": "value", "type": "str"},
     }
 
-    def __init__(self, *, value: str, description: Optional[str] = None, **kwargs):
+    def __init__(self, *, value: str, description: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -13635,8 +13799,8 @@ def __init__(
         client_id: Optional[str] = None,
         object_id: Optional[str] = None,
         resource_id: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword client_id: Specifies a user-assigned identity by client ID. For system-assigned, do
          not set this field.
@@ -13708,8 +13872,8 @@ def __init__(
         target: Optional[str] = None,
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
          "ContainerRegistry", and "Git".
@@ -13772,8 +13936,8 @@ def __init__(
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
         credentials: Optional["_models.WorkspaceConnectionManagedIdentity"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
          "ContainerRegistry", and "Git".
@@ -13887,8 +14051,8 @@ def __init__(
         readiness_probe: Optional["_models.ProbeSettings"] = None,
         request_settings: Optional["_models.OnlineRequestSettings"] = None,
         scale_settings: Optional["_models.OnlineScaleSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_configuration: Code configuration for the endpoint deployment.
         :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
@@ -13990,8 +14154,8 @@ def __init__(
         *,
         type: Union[str, "_models.ManagedServiceIdentityType"],
         user_assigned_identities: Optional[Dict[str, "_models.UserAssignedIdentity"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword type: Type of managed service identity (where both SystemAssigned and UserAssigned
          types are allowed). Required. Known values are: "None", "SystemAssigned", "UserAssigned", and
@@ -14012,7 +14176,8 @@ def __init__(
 
 
 class MedianStoppingPolicy(EarlyTerminationPolicy):
-    """Defines an early termination policy based on running averages of the primary metric of all runs.
+    """Defines an early termination policy based on running averages of the primary metric of all
+    runs.
 
     All required parameters must be populated in order to send to Azure.
 
@@ -14036,7 +14201,7 @@ class MedianStoppingPolicy(EarlyTerminationPolicy):
         "policy_type": {"key": "policyType", "type": "str"},
     }
 
-    def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, **kwargs):
+    def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, **kwargs: Any) -> None:
         """
         :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
         :paramtype delay_evaluation: int
@@ -14083,8 +14248,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -14134,8 +14299,8 @@ def __init__(
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
@@ -14203,8 +14368,8 @@ def __init__(
         is_anonymous: bool = False,
         is_archived: bool = False,
         referenced_uris: Optional[List[str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -14272,8 +14437,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -14323,8 +14488,8 @@ def __init__(
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
@@ -14378,7 +14543,7 @@ class ModelContainer(Resource):
         "properties": {"key": "properties", "type": "ModelContainerProperties"},
     }
 
-    def __init__(self, *, properties: "_models.ModelContainerProperties", **kwargs):
+    def __init__(self, *, properties: "_models.ModelContainerProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ModelContainerProperties
@@ -14427,8 +14592,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -14458,8 +14623,8 @@ class ModelContainerResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ModelContainer"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ModelContainer"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of ModelContainer objects. If null, there are no
          additional pages.
@@ -14510,7 +14675,7 @@ class ModelVersion(Resource):
         "properties": {"key": "properties", "type": "ModelVersionProperties"},
     }
 
-    def __init__(self, *, properties: "_models.ModelVersionProperties", **kwargs):
+    def __init__(self, *, properties: "_models.ModelVersionProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ModelVersionProperties
@@ -14566,8 +14731,8 @@ def __init__(
         job_name: Optional[str] = None,
         model_type: Optional[str] = None,
         model_uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -14618,8 +14783,8 @@ class ModelVersionResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ModelVersion"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ModelVersion"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of ModelVersion objects. If null, there are no
          additional pages.
@@ -14653,7 +14818,7 @@ class Mpi(DistributionConfiguration):
         "process_count_per_instance": {"key": "processCountPerInstance", "type": "int"},
     }
 
-    def __init__(self, *, process_count_per_instance: Optional[int] = None, **kwargs):
+    def __init__(self, *, process_count_per_instance: Optional[int] = None, **kwargs: Any) -> None:
         """
         :keyword process_count_per_instance: Number of processes per MPI node.
         :paramtype process_count_per_instance: int
@@ -14667,13 +14832,13 @@ class NlpVertical(_serialization.Model):
     """Abstract class for NLP related AutoML tasks.
     NLP - Natural Language Processing.
 
-        :ivar featurization_settings: Featurization inputs needed for AutoML job.
-        :vartype featurization_settings:
-         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
-        :ivar limit_settings: Execution constraints for AutoMLJob.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
     """
 
     _attribute_map = {
@@ -14688,8 +14853,8 @@ def __init__(
         featurization_settings: Optional["_models.NlpVerticalFeaturizationSettings"] = None,
         limit_settings: Optional["_models.NlpVerticalLimitSettings"] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword featurization_settings: Featurization inputs needed for AutoML job.
         :paramtype featurization_settings:
@@ -14716,7 +14881,7 @@ class NlpVerticalFeaturizationSettings(FeaturizationSettings):
         "dataset_language": {"key": "datasetLanguage", "type": "str"},
     }
 
-    def __init__(self, *, dataset_language: Optional[str] = None, **kwargs):
+    def __init__(self, *, dataset_language: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword dataset_language: Dataset language, useful for the text data.
         :paramtype dataset_language: str
@@ -14747,8 +14912,8 @@ def __init__(
         max_concurrent_trials: int = 1,
         max_trials: int = 1,
         timeout: Optional[datetime.timedelta] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword max_concurrent_trials: Maximum Concurrent AutoML iterations.
         :paramtype max_concurrent_trials: int
@@ -14800,7 +14965,7 @@ class NodeStateCounts(_serialization.Model):
         "preempted_node_count": {"key": "preemptedNodeCount", "type": "int"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.idle_node_count = None
@@ -14849,8 +15014,8 @@ def __init__(
         target: Optional[str] = None,
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
          "ContainerRegistry", and "Git".
@@ -14884,7 +15049,7 @@ class NoneDatastoreCredentials(DatastoreCredentials):
         "credentials_type": {"key": "credentialsType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.credentials_type: str = "None"
@@ -14935,7 +15100,7 @@ class NotebookAccessTokenResult(_serialization.Model):
         "scope": {"key": "scope", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.notebook_resource_id = None
@@ -14962,7 +15127,9 @@ class NotebookPreparationError(_serialization.Model):
         "status_code": {"key": "statusCode", "type": "int"},
     }
 
-    def __init__(self, *, error_message: Optional[str] = None, status_code: Optional[int] = None, **kwargs):
+    def __init__(
+        self, *, error_message: Optional[str] = None, status_code: Optional[int] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword error_message:
         :paramtype error_message: str
@@ -14998,8 +15165,8 @@ def __init__(
         fqdn: Optional[str] = None,
         resource_id: Optional[str] = None,
         notebook_preparation_error: Optional["_models.NotebookPreparationError"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword fqdn:
         :paramtype fqdn: str
@@ -15037,7 +15204,7 @@ class Objective(_serialization.Model):
         "primary_metric": {"key": "primaryMetric", "type": "str"},
     }
 
-    def __init__(self, *, goal: Union[str, "_models.Goal"], primary_metric: str, **kwargs):
+    def __init__(self, *, goal: Union[str, "_models.Goal"], primary_metric: str, **kwargs: Any) -> None:
         """
         :keyword goal: [Required] Defines supported metric goals for hyperparameter tuning. Required.
          Known values are: "Minimize" and "Maximize".
@@ -15114,8 +15281,8 @@ def __init__(
         identity: Optional["_models.ManagedServiceIdentity"] = None,
         kind: Optional[str] = None,
         sku: Optional["_models.Sku"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -15154,8 +15321,12 @@ class OnlineDeploymentTrackedResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.OnlineDeployment"]] = None, **kwargs
-    ):
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["_models.OnlineDeployment"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of OnlineDeployment objects. If null, there are
          no additional pages.
@@ -15232,8 +15403,8 @@ def __init__(
         identity: Optional["_models.ManagedServiceIdentity"] = None,
         kind: Optional[str] = None,
         sku: Optional["_models.Sku"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -15325,8 +15496,8 @@ def __init__(
         compute: Optional[str] = None,
         public_network_access: Optional[Union[str, "_models.PublicNetworkAccessType"]] = None,
         traffic: Optional[Dict[str, int]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword auth_mode: [Required] Use 'Key' for key based authentication and 'AMLToken' for Azure
          Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
@@ -15374,8 +15545,8 @@ class OnlineEndpointTrackedResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.OnlineEndpoint"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.OnlineEndpoint"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of OnlineEndpoint objects. If null, there are no
          additional pages.
@@ -15415,8 +15586,8 @@ def __init__(
         max_concurrent_requests_per_instance: int = 1,
         max_queue_wait: datetime.timedelta = "PT0.5S",
         request_timeout: datetime.timedelta = "PT5S",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword max_concurrent_requests_per_instance: The number of maximum concurrent requests per
          node allowed per deployment. Defaults to 1.
@@ -15459,7 +15630,7 @@ class OutputPathAssetReference(AssetReferenceBase):
         "path": {"key": "path", "type": "str"},
     }
 
-    def __init__(self, *, job_id: Optional[str] = None, path: Optional[str] = None, **kwargs):
+    def __init__(self, *, job_id: Optional[str] = None, path: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword job_id: ARM resource ID of the job.
         :paramtype job_id: str
@@ -15487,8 +15658,8 @@ class PaginatedComputeResourcesList(_serialization.Model):
     }
 
     def __init__(
-        self, *, value: Optional[List["_models.ComputeResource"]] = None, next_link: Optional[str] = None, **kwargs
-    ):
+        self, *, value: Optional[List["_models.ComputeResource"]] = None, next_link: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword value: An array of Machine Learning compute objects wrapped in ARM resource envelope.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.ComputeResource]
@@ -15511,7 +15682,7 @@ class PartialBatchDeployment(_serialization.Model):
         "description": {"key": "description", "type": "str"},
     }
 
-    def __init__(self, *, description: Optional[str] = None, **kwargs):
+    def __init__(self, *, description: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword description: Description of the endpoint deployment.
         :paramtype description: str
@@ -15539,8 +15710,8 @@ def __init__(
         *,
         properties: Optional["_models.PartialBatchDeployment"] = None,
         tags: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Additional attributes of the entity.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.PartialBatchDeployment
@@ -15575,8 +15746,8 @@ def __init__(
         *,
         type: Optional[Union[str, "_models.ManagedServiceIdentityType"]] = None,
         user_assigned_identities: Optional[Dict[str, JSON]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword type: Managed service identity (system assigned and/or user assigned identities).
          Known values are: "None", "SystemAssigned", "UserAssigned", and "SystemAssigned,UserAssigned".
@@ -15603,7 +15774,7 @@ class PartialMinimalTrackedResource(_serialization.Model):
         "tags": {"key": "tags", "type": "{str}"},
     }
 
-    def __init__(self, *, tags: Optional[Dict[str, str]] = None, **kwargs):
+    def __init__(self, *, tags: Optional[Dict[str, str]] = None, **kwargs: Any) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -15631,8 +15802,8 @@ def __init__(
         *,
         tags: Optional[Dict[str, str]] = None,
         identity: Optional["_models.PartialManagedServiceIdentity"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -15657,7 +15828,9 @@ class PartialMinimalTrackedResourceWithSku(PartialMinimalTrackedResource):
         "sku": {"key": "sku", "type": "PartialSku"},
     }
 
-    def __init__(self, *, tags: Optional[Dict[str, str]] = None, sku: Optional["_models.PartialSku"] = None, **kwargs):
+    def __init__(
+        self, *, tags: Optional[Dict[str, str]] = None, sku: Optional["_models.PartialSku"] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -15668,6 +15841,61 @@ def __init__(self, *, tags: Optional[Dict[str, str]] = None, sku: Optional["_mod
         self.sku = sku
 
 
+class PartialRegistryPartialTrackedResource(_serialization.Model):
+    """Strictly used in update requests.
+
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.PartialManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: Additional attributes of the entity.
+    :vartype properties: JSON
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.PartialSku
+    :ivar tags: Resource tags.
+    :vartype tags: dict[str, str]
+    """
+
+    _attribute_map = {
+        "identity": {"key": "identity", "type": "PartialManagedServiceIdentity"},
+        "kind": {"key": "kind", "type": "str"},
+        "properties": {"key": "properties", "type": "object"},
+        "sku": {"key": "sku", "type": "PartialSku"},
+        "tags": {"key": "tags", "type": "{str}"},
+    }
+
+    def __init__(
+        self,
+        *,
+        identity: Optional["_models.PartialManagedServiceIdentity"] = None,
+        kind: Optional[str] = None,
+        properties: Optional[JSON] = None,
+        sku: Optional["_models.PartialSku"] = None,
+        tags: Optional[Dict[str, str]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.PartialManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: Additional attributes of the entity.
+        :paramtype properties: JSON
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.PartialSku
+        :keyword tags: Resource tags.
+        :paramtype tags: dict[str, str]
+        """
+        super().__init__(**kwargs)
+        self.identity = identity
+        self.kind = kind
+        self.properties = properties
+        self.sku = sku
+        self.tags = tags
+
+
 class PartialSku(_serialization.Model):
     """Common SKU definition.
 
@@ -15704,8 +15932,8 @@ def __init__(
         name: Optional[str] = None,
         size: Optional[str] = None,
         tier: Optional[Union[str, "_models.SkuTier"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword capacity: If the SKU supports scale out/in then the capacity integer should be
          included. If scale out/in is not possible for the resource this may be omitted.
@@ -15752,7 +15980,7 @@ class Password(_serialization.Model):
         "value": {"key": "value", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.name = None
@@ -15802,8 +16030,8 @@ def __init__(
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
         credentials: Optional["_models.WorkspaceConnectionPersonalAccessToken"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
          "ContainerRegistry", and "Git".
@@ -15834,7 +16062,7 @@ class PersonalComputeInstanceSettings(_serialization.Model):
         "assigned_user": {"key": "assignedUser", "type": "AssignedUser"},
     }
 
-    def __init__(self, *, assigned_user: Optional["_models.AssignedUser"] = None, **kwargs):
+    def __init__(self, *, assigned_user: Optional["_models.AssignedUser"] = None, **kwargs: Any) -> None:
         """
         :keyword assigned_user: A user explicitly assigned to a personal compute instance.
         :paramtype assigned_user: ~azure.mgmt.machinelearningservices.models.AssignedUser
@@ -15936,8 +16164,8 @@ def __init__(
         outputs: Optional[Dict[str, "_models.JobOutput"]] = None,
         settings: Optional[JSON] = None,
         source_job_id: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -16016,7 +16244,7 @@ class PrivateEndpoint(_serialization.Model):
         "subnet_arm_id": {"key": "subnetArmId", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.id = None
@@ -16093,8 +16321,8 @@ def __init__(
         sku: Optional["_models.Sku"] = None,
         private_endpoint: Optional["_models.PrivateEndpoint"] = None,
         private_link_service_connection_state: Optional["_models.PrivateLinkServiceConnectionState"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword identity: The identity of the resource.
         :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
@@ -16132,7 +16360,7 @@ class PrivateEndpointConnectionListResult(_serialization.Model):
         "value": {"key": "value", "type": "[PrivateEndpointConnection]"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.PrivateEndpointConnection"]] = None, **kwargs):
+    def __init__(self, *, value: Optional[List["_models.PrivateEndpointConnection"]] = None, **kwargs: Any) -> None:
         """
         :keyword value: Array of private endpoint connections.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection]
@@ -16204,8 +16432,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         sku: Optional["_models.Sku"] = None,
         required_zone_names: Optional[List[str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword identity: The identity of the resource.
         :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
@@ -16239,7 +16467,7 @@ class PrivateLinkResourceListResult(_serialization.Model):
         "value": {"key": "value", "type": "[PrivateLinkResource]"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.PrivateLinkResource"]] = None, **kwargs):
+    def __init__(self, *, value: Optional[List["_models.PrivateLinkResource"]] = None, **kwargs: Any) -> None:
         """
         :keyword value: Array of private link resources.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.PrivateLinkResource]
@@ -16249,7 +16477,8 @@ def __init__(self, *, value: Optional[List["_models.PrivateLinkResource"]] = Non
 
 
 class PrivateLinkServiceConnectionState(_serialization.Model):
-    """A collection of information about the state of the connection between service consumer and provider.
+    """A collection of information about the state of the connection between service consumer and
+    provider.
 
     :ivar status: Indicates whether the connection has been Approved/Rejected/Removed by the owner
      of the service. Known values are: "Pending", "Approved", "Rejected", "Disconnected", and
@@ -16275,8 +16504,8 @@ def __init__(
         status: Optional[Union[str, "_models.PrivateEndpointServiceConnectionStatus"]] = None,
         description: Optional[str] = None,
         actions_required: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword status: Indicates whether the connection has been Approved/Rejected/Removed by the
          owner of the service. Known values are: "Pending", "Approved", "Rejected", "Disconnected", and
@@ -16326,8 +16555,8 @@ def __init__(
         period: datetime.timedelta = "PT10S",
         success_threshold: int = 1,
         timeout: datetime.timedelta = "PT2S",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword failure_threshold: The number of failures to allow before returning an unhealthy
          status.
@@ -16370,7 +16599,7 @@ class PyTorch(DistributionConfiguration):
         "process_count_per_instance": {"key": "processCountPerInstance", "type": "int"},
     }
 
-    def __init__(self, *, process_count_per_instance: Optional[int] = None, **kwargs):
+    def __init__(self, *, process_count_per_instance: Optional[int] = None, **kwargs: Any) -> None:
         """
         :keyword process_count_per_instance: Number of processes per node.
         :paramtype process_count_per_instance: int
@@ -16407,8 +16636,8 @@ def __init__(
         type: Optional[str] = None,
         limit: Optional[int] = None,
         unit: Optional[Union[str, "_models.QuotaUnit"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword id: Specifies the resource ID.
         :paramtype id: str
@@ -16441,8 +16670,12 @@ class QuotaUpdateParameters(_serialization.Model):
     }
 
     def __init__(
-        self, *, value: Optional[List["_models.QuotaBaseProperties"]] = None, location: Optional[str] = None, **kwargs
-    ):
+        self,
+        *,
+        value: Optional[List["_models.QuotaBaseProperties"]] = None,
+        location: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword value: The list for update quota.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.QuotaBaseProperties]
@@ -16485,8 +16718,8 @@ def __init__(
         *,
         rule: Optional[Union[str, "_models.RandomSamplingAlgorithmRule"]] = None,
         seed: Optional[int] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword rule: The specific type of random algorithm. Known values are: "Random" and "Sobol".
         :paramtype rule: str or ~azure.mgmt.machinelearningservices.models.RandomSamplingAlgorithmRule
@@ -16499,6 +16732,65 @@ def __init__(
         self.seed = seed
 
 
+class Recurrence(_serialization.Model):
+    """The workflow trigger recurrence for ComputeStartStop schedule type.
+
+    :ivar frequency: [Required] The frequency to trigger schedule. Known values are: "Minute",
+     "Hour", "Day", "Week", and "Month".
+    :vartype frequency: str or ~azure.mgmt.machinelearningservices.models.RecurrenceFrequency
+    :ivar interval: [Required] Specifies schedule interval in conjunction with frequency.
+    :vartype interval: int
+    :ivar start_time: The start time in yyyy-MM-ddTHH:mm:ss format.
+    :vartype start_time: str
+    :ivar time_zone: Specifies time zone in which the schedule runs.
+     TimeZone should follow Windows time zone format. Refer:
+     https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+    :vartype time_zone: str
+    :ivar schedule: [Required] The recurrence schedule.
+    :vartype schedule: ~azure.mgmt.machinelearningservices.models.RecurrenceSchedule
+    """
+
+    _attribute_map = {
+        "frequency": {"key": "frequency", "type": "str"},
+        "interval": {"key": "interval", "type": "int"},
+        "start_time": {"key": "startTime", "type": "str"},
+        "time_zone": {"key": "timeZone", "type": "str"},
+        "schedule": {"key": "schedule", "type": "RecurrenceSchedule"},
+    }
+
+    def __init__(
+        self,
+        *,
+        frequency: Optional[Union[str, "_models.RecurrenceFrequency"]] = None,
+        interval: Optional[int] = None,
+        start_time: Optional[str] = None,
+        time_zone: str = "UTC",
+        schedule: Optional["_models.RecurrenceSchedule"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword frequency: [Required] The frequency to trigger schedule. Known values are: "Minute",
+         "Hour", "Day", "Week", and "Month".
+        :paramtype frequency: str or ~azure.mgmt.machinelearningservices.models.RecurrenceFrequency
+        :keyword interval: [Required] Specifies schedule interval in conjunction with frequency.
+        :paramtype interval: int
+        :keyword start_time: The start time in yyyy-MM-ddTHH:mm:ss format.
+        :paramtype start_time: str
+        :keyword time_zone: Specifies time zone in which the schedule runs.
+         TimeZone should follow Windows time zone format. Refer:
+         https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11.
+        :paramtype time_zone: str
+        :keyword schedule: [Required] The recurrence schedule.
+        :paramtype schedule: ~azure.mgmt.machinelearningservices.models.RecurrenceSchedule
+        """
+        super().__init__(**kwargs)
+        self.frequency = frequency
+        self.interval = interval
+        self.start_time = start_time
+        self.time_zone = time_zone
+        self.schedule = schedule
+
+
 class RecurrenceSchedule(_serialization.Model):
     """RecurrenceSchedule.
 
@@ -16533,8 +16825,8 @@ def __init__(
         minutes: List[int],
         month_days: Optional[List[int]] = None,
         week_days: Optional[List[Union[str, "_models.WeekDay"]]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword hours: [Required] List of hours for the schedule. Required.
         :paramtype hours: list[int]
@@ -16605,8 +16897,8 @@ def __init__(
         start_time: Optional[str] = None,
         time_zone: str = "UTC",
         schedule: Optional["_models.RecurrenceSchedule"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
          https://en.wikipedia.org/wiki/ISO_8601.
@@ -16657,17 +16949,107 @@ class RegenerateEndpointKeysRequest(_serialization.Model):
         "key_value": {"key": "keyValue", "type": "str"},
     }
 
-    def __init__(self, *, key_type: Union[str, "_models.KeyType"], key_value: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, key_type: Union[str, "_models.KeyType"], key_value: Optional[str] = None, **kwargs: Any
+    ) -> None:
+        """
+        :keyword key_type: [Required] Specification for which type of key to generate. Primary or
+         Secondary. Required. Known values are: "Primary" and "Secondary".
+        :paramtype key_type: str or ~azure.mgmt.machinelearningservices.models.KeyType
+        :keyword key_value: The value the key is set to.
+        :paramtype key_value: str
+        """
+        super().__init__(**kwargs)
+        self.key_type = key_type
+        self.key_value = key_value
+
+
+class Registry(TrackedResource):
+    """Registry.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: The geo-location where the resource lives. Required.
+    :vartype location: str
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: [Required] Additional attributes of the entity. Required.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.RegistryProperties
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        "id": {"readonly": True},
+        "name": {"readonly": True},
+        "type": {"readonly": True},
+        "system_data": {"readonly": True},
+        "location": {"required": True},
+        "properties": {"required": True},
+    }
+
+    _attribute_map = {
+        "id": {"key": "id", "type": "str"},
+        "name": {"key": "name", "type": "str"},
+        "type": {"key": "type", "type": "str"},
+        "system_data": {"key": "systemData", "type": "SystemData"},
+        "tags": {"key": "tags", "type": "{str}"},
+        "location": {"key": "location", "type": "str"},
+        "identity": {"key": "identity", "type": "ManagedServiceIdentity"},
+        "kind": {"key": "kind", "type": "str"},
+        "properties": {"key": "properties", "type": "RegistryProperties"},
+        "sku": {"key": "sku", "type": "Sku"},
+    }
+
+    def __init__(
+        self,
+        *,
+        location: str,
+        properties: "_models.RegistryProperties",
+        tags: Optional[Dict[str, str]] = None,
+        identity: Optional["_models.ManagedServiceIdentity"] = None,
+        kind: Optional[str] = None,
+        sku: Optional["_models.Sku"] = None,
+        **kwargs: Any
+    ) -> None:
         """
-        :keyword key_type: [Required] Specification for which type of key to generate. Primary or
-         Secondary. Required. Known values are: "Primary" and "Secondary".
-        :paramtype key_type: str or ~azure.mgmt.machinelearningservices.models.KeyType
-        :keyword key_value: The value the key is set to.
-        :paramtype key_value: str
+        :keyword tags: Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: The geo-location where the resource lives. Required.
+        :paramtype location: str
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: [Required] Additional attributes of the entity. Required.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.RegistryProperties
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
         """
-        super().__init__(**kwargs)
-        self.key_type = key_type
-        self.key_value = key_value
+        super().__init__(tags=tags, location=location, **kwargs)
+        self.identity = identity
+        self.kind = kind
+        self.properties = properties
+        self.sku = sku
 
 
 class RegistryListCredentialsResult(_serialization.Model):
@@ -16694,7 +17076,7 @@ class RegistryListCredentialsResult(_serialization.Model):
         "passwords": {"key": "passwords", "type": "[Password]"},
     }
 
-    def __init__(self, *, passwords: Optional[List["_models.Password"]] = None, **kwargs):
+    def __init__(self, *, passwords: Optional[List["_models.Password"]] = None, **kwargs: Any) -> None:
         """
         :keyword passwords:
         :paramtype passwords: list[~azure.mgmt.machinelearningservices.models.Password]
@@ -16705,6 +17087,173 @@ def __init__(self, *, passwords: Optional[List["_models.Password"]] = None, **kw
         self.passwords = passwords
 
 
+class RegistryProperties(ResourceBase):  # pylint: disable=too-many-instance-attributes
+    """Details of the Registry.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar public_network_access:
+    :vartype public_network_access: str
+    :ivar discovery_url:
+    :vartype discovery_url: str
+    :ivar intellectual_property_publisher:
+    :vartype intellectual_property_publisher: str
+    :ivar managed_resource_group: Managed resource group created for the registry.
+    :vartype managed_resource_group: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+    :ivar ml_flow_registry_uri:
+    :vartype ml_flow_registry_uri: str
+    :ivar private_link_count:
+    :vartype private_link_count: int
+    :ivar region_details: Details of each region the registry is in.
+    :vartype region_details:
+     list[~azure.mgmt.machinelearningservices.models.RegistryRegionArmDetails]
+    :ivar managed_resource_group_tags: Tags to be applied to the managed resource group associated
+     with this registry.
+    :vartype managed_resource_group_tags: dict[str, str]
+    """
+
+    _attribute_map = {
+        "description": {"key": "description", "type": "str"},
+        "properties": {"key": "properties", "type": "{str}"},
+        "tags": {"key": "tags", "type": "{str}"},
+        "public_network_access": {"key": "publicNetworkAccess", "type": "str"},
+        "discovery_url": {"key": "discoveryUrl", "type": "str"},
+        "intellectual_property_publisher": {"key": "intellectualPropertyPublisher", "type": "str"},
+        "managed_resource_group": {"key": "managedResourceGroup", "type": "ArmResourceId"},
+        "ml_flow_registry_uri": {"key": "mlFlowRegistryUri", "type": "str"},
+        "private_link_count": {"key": "privateLinkCount", "type": "int"},
+        "region_details": {"key": "regionDetails", "type": "[RegistryRegionArmDetails]"},
+        "managed_resource_group_tags": {"key": "managedResourceGroupTags", "type": "{str}"},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        public_network_access: Optional[str] = None,
+        discovery_url: Optional[str] = None,
+        intellectual_property_publisher: Optional[str] = None,
+        managed_resource_group: Optional["_models.ArmResourceId"] = None,
+        ml_flow_registry_uri: Optional[str] = None,
+        private_link_count: Optional[int] = None,
+        region_details: Optional[List["_models.RegistryRegionArmDetails"]] = None,
+        managed_resource_group_tags: Optional[Dict[str, str]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword public_network_access:
+        :paramtype public_network_access: str
+        :keyword discovery_url:
+        :paramtype discovery_url: str
+        :keyword intellectual_property_publisher:
+        :paramtype intellectual_property_publisher: str
+        :keyword managed_resource_group: Managed resource group created for the registry.
+        :paramtype managed_resource_group: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+        :keyword ml_flow_registry_uri:
+        :paramtype ml_flow_registry_uri: str
+        :keyword private_link_count:
+        :paramtype private_link_count: int
+        :keyword region_details: Details of each region the registry is in.
+        :paramtype region_details:
+         list[~azure.mgmt.machinelearningservices.models.RegistryRegionArmDetails]
+        :keyword managed_resource_group_tags: Tags to be applied to the managed resource group
+         associated with this registry.
+        :paramtype managed_resource_group_tags: dict[str, str]
+        """
+        super().__init__(description=description, properties=properties, tags=tags, **kwargs)
+        self.public_network_access = public_network_access
+        self.discovery_url = discovery_url
+        self.intellectual_property_publisher = intellectual_property_publisher
+        self.managed_resource_group = managed_resource_group
+        self.ml_flow_registry_uri = ml_flow_registry_uri
+        self.private_link_count = private_link_count
+        self.region_details = region_details
+        self.managed_resource_group_tags = managed_resource_group_tags
+
+
+class RegistryRegionArmDetails(_serialization.Model):
+    """Details for each region the registry is in.
+
+    :ivar acr_details: List of ACR accounts.
+    :vartype acr_details: list[~azure.mgmt.machinelearningservices.models.AcrDetails]
+    :ivar location: The location where the registry exists.
+    :vartype location: str
+    :ivar storage_account_details: List of storage accounts.
+    :vartype storage_account_details:
+     list[~azure.mgmt.machinelearningservices.models.StorageAccountDetails]
+    """
+
+    _attribute_map = {
+        "acr_details": {"key": "acrDetails", "type": "[AcrDetails]"},
+        "location": {"key": "location", "type": "str"},
+        "storage_account_details": {"key": "storageAccountDetails", "type": "[StorageAccountDetails]"},
+    }
+
+    def __init__(
+        self,
+        *,
+        acr_details: Optional[List["_models.AcrDetails"]] = None,
+        location: Optional[str] = None,
+        storage_account_details: Optional[List["_models.StorageAccountDetails"]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword acr_details: List of ACR accounts.
+        :paramtype acr_details: list[~azure.mgmt.machinelearningservices.models.AcrDetails]
+        :keyword location: The location where the registry exists.
+        :paramtype location: str
+        :keyword storage_account_details: List of storage accounts.
+        :paramtype storage_account_details:
+         list[~azure.mgmt.machinelearningservices.models.StorageAccountDetails]
+        """
+        super().__init__(**kwargs)
+        self.acr_details = acr_details
+        self.location = location
+        self.storage_account_details = storage_account_details
+
+
+class RegistryTrackedResourceArmPaginatedResult(_serialization.Model):
+    """A paginated list of Registry entities.
+
+    :ivar next_link: The link to the next page of Registry objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type Registry.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Registry]
+    """
+
+    _attribute_map = {
+        "next_link": {"key": "nextLink", "type": "str"},
+        "value": {"key": "value", "type": "[Registry]"},
+    }
+
+    def __init__(
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.Registry"]] = None, **kwargs: Any
+    ) -> None:
+        """
+        :keyword next_link: The link to the next page of Registry objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type Registry.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.Registry]
+        """
+        super().__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
 class Regression(TableVertical, AutoMLVertical):  # pylint: disable=too-many-instance-attributes
     """Regression task in AutoML Table vertical.
 
@@ -16800,8 +17349,8 @@ def __init__(
         weight_column_name: Optional[str] = None,
         primary_metric: Optional[Union[str, "_models.RegressionPrimaryMetrics"]] = None,
         training_settings: Optional["_models.RegressionTrainingSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -16933,8 +17482,8 @@ def __init__(
         stack_ensemble_settings: Optional["_models.StackEnsembleSettings"] = None,
         allowed_training_algorithms: Optional[List[Union[str, "_models.RegressionModels"]]] = None,
         blocked_training_algorithms: Optional[List[Union[str, "_models.RegressionModels"]]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword enable_dnn_training: Enable recommendation of DNN models.
         :paramtype enable_dnn_training: bool
@@ -16991,7 +17540,7 @@ class ResourceId(_serialization.Model):
         "id": {"key": "id", "type": "str"},
     }
 
-    def __init__(self, *, id: str, **kwargs):  # pylint: disable=redefined-builtin
+    def __init__(self, *, id: str, **kwargs: Any) -> None:  # pylint: disable=redefined-builtin
         """
         :keyword id: The ID of the resource. Required.
         :paramtype id: str
@@ -17021,7 +17570,7 @@ class ResourceName(_serialization.Model):
         "localized_value": {"key": "localizedValue", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.value = None
@@ -17065,7 +17614,7 @@ class ResourceQuota(_serialization.Model):
         "unit": {"key": "unit", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.id = None
@@ -17097,7 +17646,7 @@ class Route(_serialization.Model):
         "port": {"key": "port", "type": "int"},
     }
 
-    def __init__(self, *, path: str, port: int, **kwargs):
+    def __init__(self, *, path: str, port: int, **kwargs: Any) -> None:
         """
         :keyword path: [Required] The path for the route. Required.
         :paramtype path: str
@@ -17152,8 +17701,8 @@ def __init__(
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
         credentials: Optional["_models.WorkspaceConnectionSharedAccessSignature"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
          "ContainerRegistry", and "Git".
@@ -17195,7 +17744,7 @@ class SasDatastoreCredentials(DatastoreCredentials):
         "secrets": {"key": "secrets", "type": "SasDatastoreSecrets"},
     }
 
-    def __init__(self, *, secrets: "_models.SasDatastoreSecrets", **kwargs):
+    def __init__(self, *, secrets: "_models.SasDatastoreSecrets", **kwargs: Any) -> None:
         """
         :keyword secrets: [Required] Storage container secrets. Required.
         :paramtype secrets: ~azure.mgmt.machinelearningservices.models.SasDatastoreSecrets
@@ -17226,7 +17775,7 @@ class SasDatastoreSecrets(DatastoreSecrets):
         "sas_token": {"key": "sasToken", "type": "str"},
     }
 
-    def __init__(self, *, sas_token: Optional[str] = None, **kwargs):
+    def __init__(self, *, sas_token: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword sas_token: Storage container SAS token.
         :paramtype sas_token: str
@@ -17266,8 +17815,8 @@ def __init__(
         max_node_count: int,
         min_node_count: int = 0,
         node_idle_time_before_scale_down: Optional[datetime.timedelta] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword max_node_count: Max number of nodes to use. Required.
         :paramtype max_node_count: int
@@ -17294,7 +17843,7 @@ class ScaleSettingsInformation(_serialization.Model):
         "scale_settings": {"key": "scaleSettings", "type": "ScaleSettings"},
     }
 
-    def __init__(self, *, scale_settings: Optional["_models.ScaleSettings"] = None, **kwargs):
+    def __init__(self, *, scale_settings: Optional["_models.ScaleSettings"] = None, **kwargs: Any) -> None:
         """
         :keyword scale_settings: scale settings for AML Compute.
         :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.ScaleSettings
@@ -17341,7 +17890,7 @@ class Schedule(Resource):
         "properties": {"key": "properties", "type": "ScheduleProperties"},
     }
 
-    def __init__(self, *, properties: "_models.ScheduleProperties", **kwargs):
+    def __init__(self, *, properties: "_models.ScheduleProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ScheduleProperties
@@ -17375,8 +17924,8 @@ def __init__(
         id: Optional[str] = None,  # pylint: disable=redefined-builtin
         provisioning_status: Optional[Union[str, "_models.ScheduleProvisioningState"]] = None,
         status: Optional[Union[str, "_models.ScheduleStatus"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword id: A system assigned id for the schedule.
         :paramtype id: str
@@ -17448,8 +17997,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         display_name: Optional[str] = None,
         is_enabled: bool = True,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -17489,7 +18038,9 @@ class ScheduleResourceArmPaginatedResult(_serialization.Model):
         "value": {"key": "value", "type": "[Schedule]"},
     }
 
-    def __init__(self, *, next_link: Optional[str] = None, value: Optional[List["_models.Schedule"]] = None, **kwargs):
+    def __init__(
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.Schedule"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of Schedule objects. If null, there are no
          additional pages.
@@ -17529,8 +18080,8 @@ def __init__(
         script_data: Optional[str] = None,
         script_arguments: Optional[str] = None,
         timeout: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword script_source: The storage source of the script: workspace.
         :paramtype script_source: str
@@ -17567,8 +18118,8 @@ def __init__(
         *,
         startup_script: Optional["_models.ScriptReference"] = None,
         creation_script: Optional["_models.ScriptReference"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword startup_script: Script that's run every time the machine starts.
         :paramtype startup_script: ~azure.mgmt.machinelearningservices.models.ScriptReference
@@ -17591,7 +18142,7 @@ class ServiceManagedResourcesSettings(_serialization.Model):
         "cosmos_db": {"key": "cosmosDb", "type": "CosmosDbSettings"},
     }
 
-    def __init__(self, *, cosmos_db: Optional["_models.CosmosDbSettings"] = None, **kwargs):
+    def __init__(self, *, cosmos_db: Optional["_models.CosmosDbSettings"] = None, **kwargs: Any) -> None:
         """
         :keyword cosmos_db: The settings for the service managed cosmosdb account.
         :paramtype cosmos_db: ~azure.mgmt.machinelearningservices.models.CosmosDbSettings
@@ -17644,8 +18195,8 @@ def __init__(
         tenant_id: str,
         authority_url: Optional[str] = None,
         resource_url: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword authority_url: Authority URL used for authentication.
         :paramtype authority_url: str
@@ -17689,7 +18240,7 @@ class ServicePrincipalDatastoreSecrets(DatastoreSecrets):
         "client_secret": {"key": "clientSecret", "type": "str"},
     }
 
-    def __init__(self, *, client_secret: Optional[str] = None, **kwargs):
+    def __init__(self, *, client_secret: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword client_secret: Service principal secret.
         :paramtype client_secret: str
@@ -17710,7 +18261,7 @@ class SetupScripts(_serialization.Model):
         "scripts": {"key": "scripts", "type": "ScriptsToExecute"},
     }
 
-    def __init__(self, *, scripts: Optional["_models.ScriptsToExecute"] = None, **kwargs):
+    def __init__(self, *, scripts: Optional["_models.ScriptsToExecute"] = None, **kwargs: Any) -> None:
         """
         :keyword scripts: Customized setup scripts.
         :paramtype scripts: ~azure.mgmt.machinelearningservices.models.ScriptsToExecute
@@ -17753,8 +18304,8 @@ def __init__(
         group_id: Optional[str] = None,
         request_message: Optional[str] = None,
         status: Optional[Union[str, "_models.PrivateEndpointServiceConnectionStatus"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword name: Unique name of the private link.
         :paramtype name: str
@@ -17820,8 +18371,8 @@ def __init__(
         size: Optional[str] = None,
         family: Optional[str] = None,
         capacity: Optional[int] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword name: The name of the SKU. Ex - P3. It is typically a letter+number code. Required.
         :paramtype name: str
@@ -17875,8 +18426,8 @@ def __init__(
         maximum: int = 0,
         minimum: int = 0,
         scale_type: Optional[Union[str, "_models.SkuScaleType"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword default: Gets or sets the default capacity.
         :paramtype default: int
@@ -17919,8 +18470,12 @@ class SkuResource(_serialization.Model):
     }
 
     def __init__(
-        self, *, capacity: Optional["_models.SkuCapacity"] = None, sku: Optional["_models.SkuSetting"] = None, **kwargs
-    ):
+        self,
+        *,
+        capacity: Optional["_models.SkuCapacity"] = None,
+        sku: Optional["_models.SkuSetting"] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword capacity: Gets or sets the Sku Capacity.
         :paramtype capacity: ~azure.mgmt.machinelearningservices.models.SkuCapacity
@@ -17949,8 +18504,8 @@ class SkuResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.SkuResource"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.SkuResource"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of SkuResource objects. If null, there are no
          additional pages.
@@ -17986,7 +18541,7 @@ class SkuSetting(_serialization.Model):
         "tier": {"key": "tier", "type": "str"},
     }
 
-    def __init__(self, *, name: str, tier: Optional[Union[str, "_models.SkuTier"]] = None, **kwargs):
+    def __init__(self, *, name: str, tier: Optional[Union[str, "_models.SkuTier"]] = None, **kwargs: Any) -> None:
         """
         :keyword name: [Required] The name of the SKU. Ex - P3. It is typically a letter+number code.
          Required.
@@ -18037,8 +18592,8 @@ def __init__(
         cname: Optional[str] = None,
         leaf_domain_label: Optional[str] = None,
         overwrite_existing_domain: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword status: Enable or disable ssl for scoring. Known values are: "Disabled", "Enabled",
          and "Auto".
@@ -18093,8 +18648,8 @@ def __init__(
         stack_meta_learner_k_wargs: Optional[JSON] = None,
         stack_meta_learner_train_percentage: float = 0.2,
         stack_meta_learner_type: Optional[Union[str, "_models.StackMetaLearnerType"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword stack_meta_learner_k_wargs: Optional parameters to pass to the initializer of the
          meta-learner.
@@ -18116,6 +18671,42 @@ def __init__(
         self.stack_meta_learner_type = stack_meta_learner_type
 
 
+class StorageAccountDetails(_serialization.Model):
+    """Details of storage account to be used for the Registry.
+
+    :ivar system_created_storage_account:
+    :vartype system_created_storage_account:
+     ~azure.mgmt.machinelearningservices.models.SystemCreatedStorageAccount
+    :ivar user_created_storage_account:
+    :vartype user_created_storage_account:
+     ~azure.mgmt.machinelearningservices.models.UserCreatedStorageAccount
+    """
+
+    _attribute_map = {
+        "system_created_storage_account": {"key": "systemCreatedStorageAccount", "type": "SystemCreatedStorageAccount"},
+        "user_created_storage_account": {"key": "userCreatedStorageAccount", "type": "UserCreatedStorageAccount"},
+    }
+
+    def __init__(
+        self,
+        *,
+        system_created_storage_account: Optional["_models.SystemCreatedStorageAccount"] = None,
+        user_created_storage_account: Optional["_models.UserCreatedStorageAccount"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword system_created_storage_account:
+        :paramtype system_created_storage_account:
+         ~azure.mgmt.machinelearningservices.models.SystemCreatedStorageAccount
+        :keyword user_created_storage_account:
+        :paramtype user_created_storage_account:
+         ~azure.mgmt.machinelearningservices.models.UserCreatedStorageAccount
+        """
+        super().__init__(**kwargs)
+        self.system_created_storage_account = system_created_storage_account
+        self.user_created_storage_account = user_created_storage_account
+
+
 class SweepJob(JobBaseProperties):  # pylint: disable=too-many-instance-attributes
     """Sweep job definition.
 
@@ -18227,8 +18818,8 @@ def __init__(
         inputs: Optional[Dict[str, "_models.JobInput"]] = None,
         limits: Optional["_models.SweepJobLimits"] = None,
         outputs: Optional[Dict[str, "_models.JobOutput"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -18335,8 +18926,8 @@ def __init__(
         max_concurrent_trials: Optional[int] = None,
         max_total_trials: Optional[int] = None,
         trial_timeout: Optional[datetime.timedelta] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
          cancelled. Only supports duration with precision as low as Seconds.
@@ -18424,8 +19015,8 @@ def __init__(
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
         properties: Optional["_models.SynapseSparkProperties"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword compute_location: Location for the underlying compute.
         :paramtype compute_location: str
@@ -18501,8 +19092,8 @@ def __init__(
         resource_group: Optional[str] = None,
         workspace_name: Optional[str] = None,
         pool_name: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword auto_scale_properties: Auto scale properties.
         :paramtype auto_scale_properties:
@@ -18540,6 +19131,100 @@ def __init__(
         self.pool_name = pool_name
 
 
+class SystemCreatedAcrAccount(_serialization.Model):
+    """SystemCreatedAcrAccount.
+
+    :ivar acr_account_sku:
+    :vartype acr_account_sku: str
+    :ivar arm_resource_id: ARM ResourceId of a resource.
+    :vartype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+    """
+
+    _attribute_map = {
+        "acr_account_sku": {"key": "acrAccountSku", "type": "str"},
+        "arm_resource_id": {"key": "armResourceId", "type": "ArmResourceId"},
+    }
+
+    def __init__(
+        self,
+        *,
+        acr_account_sku: Optional[str] = None,
+        arm_resource_id: Optional["_models.ArmResourceId"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword acr_account_sku:
+        :paramtype acr_account_sku: str
+        :keyword arm_resource_id: ARM ResourceId of a resource.
+        :paramtype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+        """
+        super().__init__(**kwargs)
+        self.acr_account_sku = acr_account_sku
+        self.arm_resource_id = arm_resource_id
+
+
+class SystemCreatedStorageAccount(_serialization.Model):
+    """SystemCreatedStorageAccount.
+
+    :ivar arm_resource_id: ARM ResourceId of a resource.
+    :vartype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+    :ivar storage_account_hns_enabled:
+    :vartype storage_account_hns_enabled: bool
+    :ivar storage_account_type: Allowed values:
+     "Standard_LRS",
+     "Standard_GRS",
+     "Standard_RAGRS",
+     "Standard_ZRS",
+     "Standard_GZRS",
+     "Standard_RAGZRS",
+     "Premium_LRS",
+     "Premium_ZRS".
+    :vartype storage_account_type: str
+    :ivar allow_blob_public_access:
+    :vartype allow_blob_public_access: bool
+    """
+
+    _attribute_map = {
+        "arm_resource_id": {"key": "armResourceId", "type": "ArmResourceId"},
+        "storage_account_hns_enabled": {"key": "storageAccountHnsEnabled", "type": "bool"},
+        "storage_account_type": {"key": "storageAccountType", "type": "str"},
+        "allow_blob_public_access": {"key": "allowBlobPublicAccess", "type": "bool"},
+    }
+
+    def __init__(
+        self,
+        *,
+        arm_resource_id: Optional["_models.ArmResourceId"] = None,
+        storage_account_hns_enabled: Optional[bool] = None,
+        storage_account_type: Optional[str] = None,
+        allow_blob_public_access: Optional[bool] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword arm_resource_id: ARM ResourceId of a resource.
+        :paramtype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+        :keyword storage_account_hns_enabled:
+        :paramtype storage_account_hns_enabled: bool
+        :keyword storage_account_type: Allowed values:
+         "Standard_LRS",
+         "Standard_GRS",
+         "Standard_RAGRS",
+         "Standard_ZRS",
+         "Standard_GZRS",
+         "Standard_RAGZRS",
+         "Premium_LRS",
+         "Premium_ZRS".
+        :paramtype storage_account_type: str
+        :keyword allow_blob_public_access:
+        :paramtype allow_blob_public_access: bool
+        """
+        super().__init__(**kwargs)
+        self.arm_resource_id = arm_resource_id
+        self.storage_account_hns_enabled = storage_account_hns_enabled
+        self.storage_account_type = storage_account_type
+        self.allow_blob_public_access = allow_blob_public_access
+
+
 class SystemData(_serialization.Model):
     """Metadata pertaining to creation and last modification of the resource.
 
@@ -18577,8 +19262,8 @@ def __init__(
         last_modified_by: Optional[str] = None,
         last_modified_by_type: Optional[Union[str, "_models.CreatedByType"]] = None,
         last_modified_at: Optional[datetime.datetime] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword created_by: The identity that created the resource.
         :paramtype created_by: str
@@ -18630,7 +19315,7 @@ class SystemService(_serialization.Model):
         "version": {"key": "version", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.system_service_type = None
@@ -18682,8 +19367,8 @@ def __init__(
         enable_dnn_featurization: bool = False,
         mode: Optional[Union[str, "_models.FeaturizationMode"]] = None,
         transformer_params: Optional[Dict[str, List["_models.ColumnTransformer"]]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword dataset_language: Dataset language, useful for the text data.
         :paramtype dataset_language: str
@@ -18755,8 +19440,8 @@ def __init__(
         max_trials: int = 1000,
         timeout: datetime.timedelta = "PT6H",
         trial_timeout: datetime.timedelta = "PT30M",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword enable_early_termination: Enable early termination, determines whether or not if
          AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
@@ -18823,8 +19508,8 @@ def __init__(
         min_instances: int = 1,
         polling_interval: datetime.timedelta = "PT1S",
         target_utilization_percentage: int = 70,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword max_instances: The maximum number of instances that the deployment can scale to. The
          quota will be reserved for max_instances.
@@ -18869,7 +19554,7 @@ class TensorFlow(DistributionConfiguration):
         "worker_count": {"key": "workerCount", "type": "int"},
     }
 
-    def __init__(self, *, parameter_server_count: int = 0, worker_count: Optional[int] = None, **kwargs):
+    def __init__(self, *, parameter_server_count: int = 0, worker_count: Optional[int] = None, **kwargs: Any) -> None:
         """
         :keyword parameter_server_count: Number of parameter server tasks.
         :paramtype parameter_server_count: int
@@ -18886,33 +19571,33 @@ class TextClassification(NlpVertical, AutoMLVertical):
     """Text Classification task in AutoML NLP vertical.
     NLP - Natural Language Processing.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar featurization_settings: Featurization inputs needed for AutoML job.
-        :vartype featurization_settings:
-         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
-        :ivar limit_settings: Execution constraints for AutoMLJob.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar primary_metric: Primary metric for Text-Classification task. Known values are:
-         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
-         "PrecisionScoreWeighted".
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-Classification task. Known values are:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
     """
 
     _validation = {
@@ -18941,8 +19626,8 @@ def __init__(
         limit_settings: Optional["_models.NlpVerticalLimitSettings"] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
         primary_metric: Optional[Union[str, "_models.ClassificationPrimaryMetrics"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -18988,36 +19673,36 @@ class TextClassificationMultilabel(NlpVertical, AutoMLVertical):
     """Text Classification Multilabel task in AutoML NLP vertical.
     NLP - Natural Language Processing.
 
-        Variables are only populated by the server, and will be ignored when sending a request.
+    Variables are only populated by the server, and will be ignored when sending a request.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar featurization_settings: Featurization inputs needed for AutoML job.
-        :vartype featurization_settings:
-         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
-        :ivar limit_settings: Execution constraints for AutoMLJob.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar primary_metric: Primary metric for Text-Classification-Multilabel task.
-         Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
-         Known values are: "AUCWeighted", "Accuracy", "NormMacroRecall",
-         "AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", and "IOU".
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-Classification-Multilabel task.
+     Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
+     Known values are: "AUCWeighted", "Accuracy", "NormMacroRecall",
+     "AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", and "IOU".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
     """
 
     _validation = {
@@ -19046,8 +19731,8 @@ def __init__(
         featurization_settings: Optional["_models.NlpVerticalFeaturizationSettings"] = None,
         limit_settings: Optional["_models.NlpVerticalLimitSettings"] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -19089,36 +19774,36 @@ class TextNer(NlpVertical, AutoMLVertical):
     NER - Named Entity Recognition.
     NLP - Natural Language Processing.
 
-        Variables are only populated by the server, and will be ignored when sending a request.
+    Variables are only populated by the server, and will be ignored when sending a request.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar featurization_settings: Featurization inputs needed for AutoML job.
-        :vartype featurization_settings:
-         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
-        :ivar limit_settings: Execution constraints for AutoMLJob.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar primary_metric: Primary metric for Text-NER task.
-         Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly. Known values
-         are: "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
-         "PrecisionScoreWeighted".
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-NER task.
+     Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly. Known values
+     are: "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
     """
 
     _validation = {
@@ -19147,8 +19832,8 @@ def __init__(
         featurization_settings: Optional["_models.NlpVerticalFeaturizationSettings"] = None,
         limit_settings: Optional["_models.NlpVerticalLimitSettings"] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -19230,8 +19915,8 @@ def __init__(
         distribution: Optional["_models.DistributionConfiguration"] = None,
         environment_variables: Optional[Dict[str, str]] = None,
         resources: Optional["_models.JobResourceConfiguration"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_id: ARM resource ID of the code asset.
         :paramtype code_id: str
@@ -19294,8 +19979,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -19345,8 +20030,8 @@ def __init__(
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
@@ -19363,7 +20048,8 @@ def __init__(
 
 
 class TruncationSelectionPolicy(EarlyTerminationPolicy):
-    """Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.
+    """Defines an early termination policy that cancels a given percentage of runs at each evaluation
+    interval.
 
     All required parameters must be populated in order to send to Azure.
 
@@ -19391,8 +20077,8 @@ class TruncationSelectionPolicy(EarlyTerminationPolicy):
     }
 
     def __init__(
-        self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, truncation_percentage: int = 0, **kwargs
-    ):
+        self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, truncation_percentage: int = 0, **kwargs: Any
+    ) -> None:
         """
         :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
         :paramtype delay_evaluation: int
@@ -19439,7 +20125,9 @@ class UpdateWorkspaceQuotas(_serialization.Model):
         "status": {"key": "status", "type": "str"},
     }
 
-    def __init__(self, *, limit: Optional[int] = None, status: Optional[Union[str, "_models.Status"]] = None, **kwargs):
+    def __init__(
+        self, *, limit: Optional[int] = None, status: Optional[Union[str, "_models.Status"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword limit: The maximum permitted quota of the resource.
         :paramtype limit: int
@@ -19478,7 +20166,7 @@ class UpdateWorkspaceQuotasResult(_serialization.Model):
         "next_link": {"key": "nextLink", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.value = None
@@ -19533,8 +20221,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         is_anonymous: bool = False,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -19599,8 +20287,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -19650,8 +20338,8 @@ def __init__(
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
@@ -19715,8 +20403,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         is_anonymous: bool = False,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -19781,8 +20469,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -19832,8 +20520,8 @@ def __init__(
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
@@ -19890,7 +20578,7 @@ class Usage(_serialization.Model):
         "name": {"key": "name", "type": "UsageName"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.id = None
@@ -19923,7 +20611,7 @@ class UsageName(_serialization.Model):
         "localized_value": {"key": "localizedValue", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.value = None
@@ -19960,8 +20648,8 @@ def __init__(
         admin_user_name: str,
         admin_user_ssh_public_key: Optional[str] = None,
         admin_user_password: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword admin_user_name: Name of the administrator user account which can be used to SSH to
          nodes. Required.
@@ -19998,13 +20686,53 @@ class UserAssignedIdentity(_serialization.Model):
         "client_id": {"key": "clientId", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.principal_id = None
         self.client_id = None
 
 
+class UserCreatedAcrAccount(_serialization.Model):
+    """UserCreatedAcrAccount.
+
+    :ivar arm_resource_id: ARM ResourceId of a resource.
+    :vartype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+    """
+
+    _attribute_map = {
+        "arm_resource_id": {"key": "armResourceId", "type": "ArmResourceId"},
+    }
+
+    def __init__(self, *, arm_resource_id: Optional["_models.ArmResourceId"] = None, **kwargs: Any) -> None:
+        """
+        :keyword arm_resource_id: ARM ResourceId of a resource.
+        :paramtype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+        """
+        super().__init__(**kwargs)
+        self.arm_resource_id = arm_resource_id
+
+
+class UserCreatedStorageAccount(_serialization.Model):
+    """UserCreatedStorageAccount.
+
+    :ivar arm_resource_id: ARM ResourceId of a resource.
+    :vartype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+    """
+
+    _attribute_map = {
+        "arm_resource_id": {"key": "armResourceId", "type": "ArmResourceId"},
+    }
+
+    def __init__(self, *, arm_resource_id: Optional["_models.ArmResourceId"] = None, **kwargs: Any) -> None:
+        """
+        :keyword arm_resource_id: ARM ResourceId of a resource.
+        :paramtype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+        """
+        super().__init__(**kwargs)
+        self.arm_resource_id = arm_resource_id
+
+
 class UserIdentity(IdentityConfiguration):
     """User identity configuration.
 
@@ -20024,7 +20752,7 @@ class UserIdentity(IdentityConfiguration):
         "identity_type": {"key": "identityType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.identity_type: str = "UserIdentity"
@@ -20073,8 +20801,8 @@ def __init__(
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
         credentials: Optional["_models.WorkspaceConnectionUsernamePassword"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
          "ContainerRegistry", and "Git".
@@ -20105,7 +20833,7 @@ class VirtualMachineSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "VirtualMachineSchemaProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.VirtualMachineSchemaProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.VirtualMachineSchemaProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties:
         :paramtype properties:
@@ -20184,8 +20912,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties:
         :paramtype properties:
@@ -20238,7 +20966,7 @@ class VirtualMachineImage(_serialization.Model):
         "id": {"key": "id", "type": "str"},
     }
 
-    def __init__(self, *, id: str, **kwargs):  # pylint: disable=redefined-builtin
+    def __init__(self, *, id: str, **kwargs: Any) -> None:  # pylint: disable=redefined-builtin
         """
         :keyword id: Virtual Machine image path. Required.
         :paramtype id: str
@@ -20284,8 +21012,8 @@ def __init__(
         address: Optional[str] = None,
         administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None,
         is_notebook_instance_compute: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword virtual_machine_size: Virtual Machine size.
         :paramtype virtual_machine_size: str
@@ -20323,7 +21051,9 @@ class VirtualMachineSecretsSchema(_serialization.Model):
         "administrator_account": {"key": "administratorAccount", "type": "VirtualMachineSshCredentials"},
     }
 
-    def __init__(self, *, administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None, **kwargs):
+    def __init__(
+        self, *, administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword administrator_account: Admin credentials for virtual machine.
         :paramtype administrator_account:
@@ -20356,7 +21086,9 @@ class VirtualMachineSecrets(ComputeSecrets, VirtualMachineSecretsSchema):
         "compute_type": {"key": "computeType", "type": "str"},
     }
 
-    def __init__(self, *, administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None, **kwargs):
+    def __init__(
+        self, *, administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword administrator_account: Admin credentials for virtual machine.
         :paramtype administrator_account:
@@ -20429,8 +21161,8 @@ def __init__(
         *,
         estimated_vm_prices: Optional["_models.EstimatedVMPrices"] = None,
         supported_compute_types: Optional[List[str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword estimated_vm_prices: The estimated price information for using a VM.
         :paramtype estimated_vm_prices: ~azure.mgmt.machinelearningservices.models.EstimatedVMPrices
@@ -20463,7 +21195,7 @@ class VirtualMachineSizeListResult(_serialization.Model):
         "value": {"key": "value", "type": "[VirtualMachineSize]"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.VirtualMachineSize"]] = None, **kwargs):
+    def __init__(self, *, value: Optional[List["_models.VirtualMachineSize"]] = None, **kwargs: Any) -> None:
         """
         :keyword value: The list of virtual machine sizes supported by AmlCompute.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.VirtualMachineSize]
@@ -20499,8 +21231,8 @@ def __init__(
         password: Optional[str] = None,
         public_key_data: Optional[str] = None,
         private_key_data: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword username: Username of admin account.
         :paramtype username: str
@@ -20697,8 +21429,8 @@ def __init__(  # pylint: disable=too-many-locals
         service_managed_resources_settings: Optional["_models.ServiceManagedResourcesSettings"] = None,
         primary_user_assigned_identity: Optional[str] = None,
         v1_legacy_mode: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword identity: The identity of the resource.
         :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
@@ -20800,7 +21532,7 @@ class WorkspaceConnectionManagedIdentity(_serialization.Model):
         "client_id": {"key": "clientId", "type": "str"},
     }
 
-    def __init__(self, *, resource_id: Optional[str] = None, client_id: Optional[str] = None, **kwargs):
+    def __init__(self, *, resource_id: Optional[str] = None, client_id: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword resource_id:
         :paramtype resource_id: str
@@ -20823,7 +21555,7 @@ class WorkspaceConnectionPersonalAccessToken(_serialization.Model):
         "pat": {"key": "pat", "type": "str"},
     }
 
-    def __init__(self, *, pat: Optional[str] = None, **kwargs):
+    def __init__(self, *, pat: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword pat:
         :paramtype pat: str
@@ -20870,7 +21602,7 @@ class WorkspaceConnectionPropertiesV2BasicResource(Resource):
         "properties": {"key": "properties", "type": "WorkspaceConnectionPropertiesV2"},
     }
 
-    def __init__(self, *, properties: "_models.WorkspaceConnectionPropertiesV2", **kwargs):
+    def __init__(self, *, properties: "_models.WorkspaceConnectionPropertiesV2", **kwargs: Any) -> None:
         """
         :keyword properties: Required.
         :paramtype properties:
@@ -20902,8 +21634,8 @@ class WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult(_serializat
     }
 
     def __init__(
-        self, *, value: Optional[List["_models.WorkspaceConnectionPropertiesV2BasicResource"]] = None, **kwargs
-    ):
+        self, *, value: Optional[List["_models.WorkspaceConnectionPropertiesV2BasicResource"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword value:
         :paramtype value:
@@ -20925,7 +21657,7 @@ class WorkspaceConnectionSharedAccessSignature(_serialization.Model):
         "sas": {"key": "sas", "type": "str"},
     }
 
-    def __init__(self, *, sas: Optional[str] = None, **kwargs):
+    def __init__(self, *, sas: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword sas:
         :paramtype sas: str
@@ -20948,7 +21680,7 @@ class WorkspaceConnectionUsernamePassword(_serialization.Model):
         "password": {"key": "password", "type": "str"},
     }
 
-    def __init__(self, *, username: Optional[str] = None, password: Optional[str] = None, **kwargs):
+    def __init__(self, *, username: Optional[str] = None, password: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword username:
         :paramtype username: str
@@ -20976,7 +21708,9 @@ class WorkspaceListResult(_serialization.Model):
         "next_link": {"key": "nextLink", "type": "str"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.Workspace"]] = None, next_link: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, value: Optional[List["_models.Workspace"]] = None, next_link: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword value: The list of machine learning workspaces. Since this list may be incomplete, the
          nextLink field should be used to request the next list of machine learning workspaces.
@@ -21052,8 +21786,8 @@ def __init__(
         public_network_access: Optional[Union[str, "_models.PublicNetworkAccess"]] = None,
         application_insights: Optional[str] = None,
         container_registry: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: The resource tags for the machine learning workspace.
         :paramtype tags: dict[str, str]
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/__init__.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/__init__.py
index 0065485916d9..3a8e74c05cba 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/__init__.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/__init__.py
@@ -32,6 +32,7 @@
 from ._online_endpoints_operations import OnlineEndpointsOperations
 from ._online_deployments_operations import OnlineDeploymentsOperations
 from ._schedules_operations import SchedulesOperations
+from ._registries_operations import RegistriesOperations
 from ._workspace_features_operations import WorkspaceFeaturesOperations
 
 from ._patch import __all__ as _patch_all
@@ -65,6 +66,7 @@
     "OnlineEndpointsOperations",
     "OnlineDeploymentsOperations",
     "SchedulesOperations",
+    "RegistriesOperations",
     "WorkspaceFeaturesOperations",
 ]
 __all__.extend([p for p in _patch_all if p not in __all__])
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_deployments_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_deployments_operations.py
index 944b8300d2f5..afc4724f9e78 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_deployments_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_deployments_operations.py
@@ -57,7 +57,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -102,7 +102,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -142,7 +142,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -182,7 +182,7 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -229,7 +229,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -321,7 +321,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchDeploymentTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -381,8 +381,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -413,7 +414,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -432,8 +433,9 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -490,7 +492,7 @@ def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -567,7 +569,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchDeployment] = kwargs.pop("cls", None)
@@ -586,8 +588,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -628,7 +631,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -659,8 +662,9 @@ def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -805,8 +809,8 @@ def begin_update(
         :type endpoint_name: str
         :param deployment_name: The identifier for the Batch inference deployment. Required.
         :type deployment_name: str
-        :param body: Batch inference deployment definition object. Is either a model type or a IO type.
-         Required.
+        :param body: Batch inference deployment definition object. Is either a
+         PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties type or a IO type. Required.
         :type body:
          ~azure.mgmt.machinelearningservices.models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties
          or IO
@@ -830,7 +834,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -899,7 +903,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -930,8 +934,9 @@ def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1077,8 +1082,8 @@ def begin_create_or_update(
         :type endpoint_name: str
         :param deployment_name: The identifier for the Batch inference deployment. Required.
         :type deployment_name: str
-        :param body: Batch inference deployment definition object. Is either a model type or a IO type.
-         Required.
+        :param body: Batch inference deployment definition object. Is either a BatchDeployment type or
+         a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.BatchDeployment or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1100,7 +1105,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_endpoints_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_endpoints_operations.py
index 5813ab488a03..74007ad8dade 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_endpoints_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_endpoints_operations.py
@@ -55,7 +55,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -92,7 +92,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -126,7 +126,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -160,7 +160,7 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -199,7 +199,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -238,7 +238,7 @@ def build_list_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -315,7 +315,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchEndpointTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -373,8 +373,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -405,7 +406,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -423,8 +424,9 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -479,7 +481,7 @@ def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -553,7 +555,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None)
@@ -571,8 +573,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -612,7 +615,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -642,8 +645,9 @@ def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -777,8 +781,8 @@ def begin_update(
         :type workspace_name: str
         :param endpoint_name: Name for the Batch inference endpoint. Required.
         :type endpoint_name: str
-        :param body: Mutable batch inference endpoint definition object. Is either a model type or a IO
-         type. Required.
+        :param body: Mutable batch inference endpoint definition object. Is either a
+         PartialMinimalTrackedResourceWithIdentity type or a IO type. Required.
         :type body:
          ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithIdentity or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -800,7 +804,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -867,7 +871,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -897,8 +901,9 @@ def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1033,8 +1038,8 @@ def begin_create_or_update(
         :type workspace_name: str
         :param endpoint_name: Name for the Batch inference endpoint. Required.
         :type endpoint_name: str
-        :param body: Batch inference endpoint definition object. Is either a model type or a IO type.
-         Required.
+        :param body: Batch inference endpoint definition object. Is either a BatchEndpoint type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.BatchEndpoint or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1055,7 +1060,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1134,7 +1139,7 @@ def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None)
@@ -1152,8 +1157,9 @@ def list_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_containers_operations.py
index 637d9072c910..6dfd8d3faee2 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_containers_operations.py
@@ -47,7 +47,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -82,7 +82,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -116,7 +116,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -150,7 +150,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -223,7 +223,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -280,8 +280,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -329,7 +330,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -347,8 +348,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -394,7 +396,7 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeContainer] = kwargs.pop("cls", None)
@@ -412,8 +414,9 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -520,7 +523,7 @@ def create_or_update(
         :type workspace_name: str
         :param name: Container name. This is case-sensitive. Required.
         :type name: str
-        :param body: Container entity to create or update. Is either a model type or a IO type.
+        :param body: Container entity to create or update. Is either a CodeContainer type or a IO type.
          Required.
         :type body: ~azure.mgmt.machinelearningservices.models.CodeContainer or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -542,7 +545,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -572,8 +575,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_versions_operations.py
index 561baa83676c..ac8245188034 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_versions_operations.py
@@ -55,7 +55,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -95,7 +95,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -130,7 +130,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -165,7 +165,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -252,7 +252,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -312,8 +312,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -363,7 +364,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -382,8 +383,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -433,7 +435,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeVersion] = kwargs.pop("cls", None)
@@ -452,8 +454,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -569,7 +572,8 @@ def create_or_update(
         :type name: str
         :param version: Version identifier. This is case-sensitive. Required.
         :type version: str
-        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :param body: Version entity to create or update. Is either a CodeVersion type or a IO type.
+         Required.
         :type body: ~azure.mgmt.machinelearningservices.models.CodeVersion or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -590,7 +594,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -621,8 +625,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_containers_operations.py
index 4b9361f4a37c..d029c64be2b1 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_containers_operations.py
@@ -53,7 +53,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -90,7 +90,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -124,7 +124,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -158,7 +158,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -240,7 +240,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -298,8 +298,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -347,7 +348,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -365,8 +366,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -414,7 +416,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentContainer] = kwargs.pop("cls", None)
@@ -432,8 +434,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -540,8 +543,8 @@ def create_or_update(
         :type workspace_name: str
         :param name: Container name. Required.
         :type name: str
-        :param body: Container entity to create or update. Is either a model type or a IO type.
-         Required.
+        :param body: Container entity to create or update. Is either a ComponentContainer type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.ComponentContainer or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -562,7 +565,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -592,8 +595,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_versions_operations.py
index 5e99740483db..118194c1e6ec 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_versions_operations.py
@@ -56,7 +56,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -98,7 +98,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -133,7 +133,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -168,7 +168,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -260,7 +260,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -321,8 +321,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -372,7 +373,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -391,8 +392,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -442,7 +444,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentVersion] = kwargs.pop("cls", None)
@@ -461,8 +463,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -578,7 +581,8 @@ def create_or_update(
         :type name: str
         :param version: Version identifier. Required.
         :type version: str
-        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :param body: Version entity to create or update. Is either a ComponentVersion type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.ComponentVersion or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -599,7 +603,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -630,8 +634,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_compute_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_compute_operations.py
index 51927e5687de..f091444fa8f7 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_compute_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_compute_operations.py
@@ -49,7 +49,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -84,7 +84,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -118,7 +118,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -155,7 +155,7 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -198,7 +198,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -235,7 +235,7 @@ def build_list_nodes_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -269,7 +269,7 @@ def build_list_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -303,7 +303,7 @@ def build_start_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -337,7 +337,7 @@ def build_stop_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -371,7 +371,7 @@ def build_restart_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -440,7 +440,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PaginatedComputeResourcesList] = kwargs.pop("cls", None)
@@ -497,8 +497,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -545,7 +546,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComputeResource] = kwargs.pop("cls", None)
@@ -563,8 +564,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -604,7 +606,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -634,8 +636,9 @@ def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -769,8 +772,8 @@ def begin_create_or_update(
         :type workspace_name: str
         :param compute_name: Name of the Azure Machine Learning compute. Required.
         :type compute_name: str
-        :param parameters: Payload with Machine Learning compute definition. Is either a model type or
-         a IO type. Required.
+        :param parameters: Payload with Machine Learning compute definition. Is either a
+         ComputeResource type or a IO type. Required.
         :type parameters: ~azure.mgmt.machinelearningservices.models.ComputeResource or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -792,7 +795,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -859,7 +862,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -889,8 +892,9 @@ def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1012,8 +1016,8 @@ def begin_update(
         :type workspace_name: str
         :param compute_name: Name of the Azure Machine Learning compute. Required.
         :type compute_name: str
-        :param parameters: Additional parameters for cluster update. Is either a model type or a IO
-         type. Required.
+        :param parameters: Additional parameters for cluster update. Is either a
+         ClusterUpdateParameters type or a IO type. Required.
         :type parameters: ~azure.mgmt.machinelearningservices.models.ClusterUpdateParameters or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1035,7 +1039,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1102,7 +1106,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1121,8 +1125,9 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1184,7 +1189,7 @@ def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1251,7 +1256,7 @@ def list_nodes(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.AmlComputeNodesInformation] = kwargs.pop("cls", None)
@@ -1308,8 +1313,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -1355,7 +1361,7 @@ def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComputeSecrets] = kwargs.pop("cls", None)
@@ -1373,8 +1379,9 @@ def list_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1409,7 +1416,7 @@ def _start_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1427,8 +1434,9 @@ def _start_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1473,7 +1481,7 @@ def begin_start(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1530,7 +1538,7 @@ def _stop_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1548,8 +1556,9 @@ def _stop_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1594,7 +1603,7 @@ def begin_stop(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1651,7 +1660,7 @@ def _restart_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1669,8 +1678,9 @@ def _restart_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1715,7 +1725,7 @@ def begin_restart(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_containers_operations.py
index 3b5d3acc38c6..f2d538df0733 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_containers_operations.py
@@ -53,7 +53,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -90,7 +90,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -124,7 +124,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -158,7 +158,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -239,7 +239,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -297,8 +297,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -346,7 +347,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -364,8 +365,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -411,7 +413,7 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataContainer] = kwargs.pop("cls", None)
@@ -429,8 +431,9 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -537,7 +540,7 @@ def create_or_update(
         :type workspace_name: str
         :param name: Container name. Required.
         :type name: str
-        :param body: Container entity to create or update. Is either a model type or a IO type.
+        :param body: Container entity to create or update. Is either a DataContainer type or a IO type.
          Required.
         :type body: ~azure.mgmt.machinelearningservices.models.DataContainer or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -559,7 +562,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -589,8 +592,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_versions_operations.py
index 092363a5e2f3..822831c7c866 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_versions_operations.py
@@ -57,7 +57,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -101,7 +101,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -136,7 +136,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -171,7 +171,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -271,7 +271,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataVersionBaseResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -333,8 +333,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -384,7 +385,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -403,8 +404,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -454,7 +456,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataVersionBase] = kwargs.pop("cls", None)
@@ -473,8 +475,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -590,7 +593,8 @@ def create_or_update(
         :type name: str
         :param version: Version identifier. Required.
         :type version: str
-        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :param body: Version entity to create or update. Is either a DataVersionBase type or a IO type.
+         Required.
         :type body: ~azure.mgmt.machinelearningservices.models.DataVersionBase or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -611,7 +615,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -642,8 +646,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_datastores_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_datastores_operations.py
index bc01c32a0983..acdb28d35025 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_datastores_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_datastores_operations.py
@@ -58,7 +58,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -105,7 +105,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -139,7 +139,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -179,7 +179,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -218,7 +218,7 @@ def build_list_secrets_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -310,7 +310,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DatastoreResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -373,8 +373,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -422,7 +423,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -440,8 +441,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -487,7 +489,7 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Datastore] = kwargs.pop("cls", None)
@@ -505,8 +507,9 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -620,7 +623,7 @@ def create_or_update(
         :type workspace_name: str
         :param name: Datastore name. Required.
         :type name: str
-        :param body: Datastore entity to create or update. Is either a model type or a IO type.
+        :param body: Datastore entity to create or update. Is either a Datastore type or a IO type.
          Required.
         :type body: ~azure.mgmt.machinelearningservices.models.Datastore or IO
         :param skip_validation: Flag to skip validation. Default value is False.
@@ -644,7 +647,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -675,8 +678,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -732,7 +736,7 @@ def list_secrets(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DatastoreSecrets] = kwargs.pop("cls", None)
@@ -750,8 +754,9 @@ def list_secrets(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_containers_operations.py
index d80de1f2b3c7..38e3fb219121 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_containers_operations.py
@@ -53,7 +53,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -90,7 +90,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -124,7 +124,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -158,7 +158,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -241,7 +241,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -299,8 +299,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -348,7 +349,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -366,8 +367,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -415,7 +417,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentContainer] = kwargs.pop("cls", None)
@@ -433,8 +435,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -541,8 +544,8 @@ def create_or_update(
         :type workspace_name: str
         :param name: Container name. This is case-sensitive. Required.
         :type name: str
-        :param body: Container entity to create or update. Is either a model type or a IO type.
-         Required.
+        :param body: Container entity to create or update. Is either a EnvironmentContainer type or a
+         IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -563,7 +566,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -593,8 +596,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_versions_operations.py
index d898cb3c67dd..948e65acd3ec 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_versions_operations.py
@@ -56,7 +56,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -98,7 +98,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -133,7 +133,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -168,7 +168,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -260,7 +260,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -321,8 +321,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -372,7 +373,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -391,8 +392,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -442,7 +444,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentVersion] = kwargs.pop("cls", None)
@@ -461,8 +463,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -578,7 +581,8 @@ def create_or_update(
         :type name: str
         :param version: Version of EnvironmentVersion. Required.
         :type version: str
-        :param body: Definition of EnvironmentVersion. Is either a model type or a IO type. Required.
+        :param body: Definition of EnvironmentVersion. Is either a EnvironmentVersion type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -599,7 +603,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -630,8 +634,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_jobs_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_jobs_operations.py
index cd8bd0bd0c47..be5e5bdc69b4 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_jobs_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_jobs_operations.py
@@ -57,7 +57,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -98,7 +98,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -132,7 +132,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -166,7 +166,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -203,7 +203,7 @@ def build_cancel_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -287,7 +287,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.JobBaseResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -347,8 +347,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -379,7 +380,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -397,8 +398,9 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -451,7 +453,7 @@ def begin_delete(self, resource_group_name: str, workspace_name: str, id: str, *
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -523,7 +525,7 @@ def get(self, resource_group_name: str, workspace_name: str, id: str, **kwargs:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.JobBase] = kwargs.pop("cls", None)
@@ -541,8 +543,9 @@ def get(self, resource_group_name: str, workspace_name: str, id: str, **kwargs:
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -644,7 +647,7 @@ def create_or_update(
         :type workspace_name: str
         :param id: The name and identifier for the Job. This is case-sensitive. Required.
         :type id: str
-        :param body: Job definition object. Is either a model type or a IO type. Required.
+        :param body: Job definition object. Is either a JobBase type or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.JobBase or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -665,7 +668,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -695,8 +698,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -735,7 +739,7 @@ def _cancel_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -753,8 +757,9 @@ def _cancel_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -804,7 +809,7 @@ def begin_cancel(self, resource_group_name: str, workspace_name: str, id: str, *
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_containers_operations.py
index 2c416fba2772..507fea97e352 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_containers_operations.py
@@ -54,7 +54,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -93,7 +93,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -127,7 +127,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -161,7 +161,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -245,7 +245,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -304,8 +304,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -353,7 +354,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -371,8 +372,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -418,7 +420,7 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None)
@@ -436,8 +438,9 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -544,8 +547,8 @@ def create_or_update(
         :type workspace_name: str
         :param name: Container name. This is case-sensitive. Required.
         :type name: str
-        :param body: Container entity to create or update. Is either a model type or a IO type.
-         Required.
+        :param body: Container entity to create or update. Is either a ModelContainer type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.ModelContainer or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -566,7 +569,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -596,8 +599,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_versions_operations.py
index 775841fe3ae4..814430c362a9 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_versions_operations.py
@@ -62,7 +62,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -116,7 +116,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -151,7 +151,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -186,7 +186,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -297,7 +297,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -364,8 +364,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -415,7 +416,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -434,8 +435,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -485,7 +487,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelVersion] = kwargs.pop("cls", None)
@@ -504,8 +506,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -621,7 +624,8 @@ def create_or_update(
         :type name: str
         :param version: Version identifier. This is case-sensitive. Required.
         :type version: str
-        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :param body: Version entity to create or update. Is either a ModelVersion type or a IO type.
+         Required.
         :type body: ~azure.mgmt.machinelearningservices.models.ModelVersion or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -642,7 +646,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -673,8 +677,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_deployments_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_deployments_operations.py
index cf048b3e2993..0fe4ef4b7724 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_deployments_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_deployments_operations.py
@@ -57,7 +57,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -102,7 +102,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -142,7 +142,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -182,7 +182,7 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -229,7 +229,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -276,7 +276,7 @@ def build_get_logs_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -322,7 +322,7 @@ def build_list_skus_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -411,7 +411,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineDeploymentTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -471,8 +471,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -503,7 +504,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -522,8 +523,9 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -580,7 +582,7 @@ def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -657,7 +659,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineDeployment] = kwargs.pop("cls", None)
@@ -676,8 +678,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -718,7 +721,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -749,8 +752,9 @@ def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -894,8 +898,8 @@ def begin_update(
         :type endpoint_name: str
         :param deployment_name: Inference Endpoint Deployment name. Required.
         :type deployment_name: str
-        :param body: Online Endpoint entity to apply during operation. Is either a model type or a IO
-         type. Required.
+        :param body: Online Endpoint entity to apply during operation. Is either a
+         PartialMinimalTrackedResourceWithSku type or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithSku or
          IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -918,7 +922,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -987,7 +991,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1018,8 +1022,9 @@ def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1165,8 +1170,8 @@ def begin_create_or_update(
         :type endpoint_name: str
         :param deployment_name: Inference Endpoint Deployment name. Required.
         :type deployment_name: str
-        :param body: Inference Endpoint entity to apply during operation. Is either a model type or a
-         IO type. Required.
+        :param body: Inference Endpoint entity to apply during operation. Is either a OnlineDeployment
+         type or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.OnlineDeployment or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1188,7 +1193,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1332,8 +1337,8 @@ def get_logs(
         :type endpoint_name: str
         :param deployment_name: The name and identifier for the endpoint. Required.
         :type deployment_name: str
-        :param body: The request containing parameters for retrieving logs. Is either a model type or a
-         IO type. Required.
+        :param body: The request containing parameters for retrieving logs. Is either a
+         DeploymentLogsRequest type or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.DeploymentLogsRequest or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1354,7 +1359,7 @@ def get_logs(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1385,8 +1390,9 @@ def get_logs(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1443,7 +1449,7 @@ def list_skus(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.SkuResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -1503,8 +1509,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_endpoints_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_endpoints_operations.py
index 9d9051dce1f7..ed4ede148a8e 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_endpoints_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_endpoints_operations.py
@@ -60,7 +60,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -107,7 +107,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -141,7 +141,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -175,7 +175,7 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -212,7 +212,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -251,7 +251,7 @@ def build_list_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -285,7 +285,7 @@ def build_regenerate_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -322,7 +322,7 @@ def build_get_token_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -420,7 +420,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineEndpointTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -483,8 +483,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -515,7 +516,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -533,8 +534,9 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -589,7 +591,7 @@ def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -663,7 +665,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineEndpoint] = kwargs.pop("cls", None)
@@ -681,8 +683,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -722,7 +725,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -752,8 +755,9 @@ def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -889,8 +893,8 @@ def begin_update(
         :type workspace_name: str
         :param endpoint_name: Online Endpoint name. Required.
         :type endpoint_name: str
-        :param body: Online Endpoint entity to apply during operation. Is either a model type or a IO
-         type. Required.
+        :param body: Online Endpoint entity to apply during operation. Is either a
+         PartialMinimalTrackedResourceWithIdentity type or a IO type. Required.
         :type body:
          ~azure.mgmt.machinelearningservices.models.PartialMinimalTrackedResourceWithIdentity or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -913,7 +917,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -980,7 +984,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1010,8 +1014,9 @@ def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1148,8 +1153,8 @@ def begin_create_or_update(
         :type workspace_name: str
         :param endpoint_name: Online Endpoint name. Required.
         :type endpoint_name: str
-        :param body: Online Endpoint entity to apply during operation. Is either a model type or a IO
-         type. Required.
+        :param body: Online Endpoint entity to apply during operation. Is either a OnlineEndpoint type
+         or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.OnlineEndpoint or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1171,7 +1176,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1250,7 +1255,7 @@ def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None)
@@ -1268,8 +1273,9 @@ def list_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1309,7 +1315,7 @@ def _regenerate_keys_initial(  # pylint: disable=inconsistent-return-statements
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1339,8 +1345,9 @@ def _regenerate_keys_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1462,7 +1469,8 @@ def begin_regenerate_keys(
         :type workspace_name: str
         :param endpoint_name: Online Endpoint name. Required.
         :type endpoint_name: str
-        :param body: RegenerateKeys request . Is either a model type or a IO type. Required.
+        :param body: RegenerateKeys request . Is either a RegenerateEndpointKeysRequest type or a IO
+         type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.RegenerateEndpointKeysRequest or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1482,7 +1490,7 @@ def begin_regenerate_keys(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1561,7 +1569,7 @@ def get_token(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthToken] = kwargs.pop("cls", None)
@@ -1579,8 +1587,9 @@ def get_token(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_operations.py
index 369a4a8166c9..7dc0790a315d 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_operations.py
@@ -45,7 +45,7 @@ def build_list_request(**kwargs: Any) -> HttpRequest:
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -91,7 +91,7 @@ def list(self, **kwargs: Any) -> Iterable["_models.AmlOperation"]:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.AmlOperationListResult] = kwargs.pop("cls", None)
@@ -144,8 +144,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_endpoint_connections_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_endpoint_connections_operations.py
index 6be9a15c5c60..be4b0cda4df1 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_endpoint_connections_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_endpoint_connections_operations.py
@@ -47,7 +47,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -84,7 +84,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -124,7 +124,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -167,7 +167,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -237,7 +237,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateEndpointConnectionListResult] = kwargs.pop("cls", None)
@@ -293,8 +293,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -341,7 +342,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateEndpointConnection] = kwargs.pop("cls", None)
@@ -359,8 +360,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -464,8 +466,8 @@ def create_or_update(
         :param private_endpoint_connection_name: The name of the private endpoint connection associated
          with the workspace. Required.
         :type private_endpoint_connection_name: str
-        :param properties: The private endpoint connection properties. Is either a model type or a IO
-         type. Required.
+        :param properties: The private endpoint connection properties. Is either a
+         PrivateEndpointConnection type or a IO type. Required.
         :type properties: ~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -486,7 +488,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -516,8 +518,9 @@ def create_or_update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -568,7 +571,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -586,8 +589,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_link_resources_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_link_resources_operations.py
index 330afa1b8940..7a35653079f8 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_link_resources_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_link_resources_operations.py
@@ -45,7 +45,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -118,7 +118,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateLinkResourceListResult] = kwargs.pop("cls", None)
@@ -135,8 +135,9 @@ def list(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_quotas_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_quotas_operations.py
index 926e7f8d9baa..b7af732a2582 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_quotas_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_quotas_operations.py
@@ -45,7 +45,7 @@ def build_update_request(location: str, subscription_id: str, **kwargs: Any) ->
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -76,7 +76,7 @@ def build_list_request(location: str, subscription_id: str, **kwargs: Any) -> Ht
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -170,7 +170,8 @@ def update(
 
         :param location: The location for update quota is queried. Required.
         :type location: str
-        :param parameters: Quota update parameters. Is either a model type or a IO type. Required.
+        :param parameters: Quota update parameters. Is either a QuotaUpdateParameters type or a IO
+         type. Required.
         :type parameters: ~azure.mgmt.machinelearningservices.models.QuotaUpdateParameters or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -191,7 +192,7 @@ def update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -219,8 +220,9 @@ def update(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -255,7 +257,7 @@ def list(self, location: str, **kwargs: Any) -> Iterable["_models.ResourceQuota"
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListWorkspaceQuotas] = kwargs.pop("cls", None)
@@ -310,8 +312,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registries_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registries_operations.py
new file mode 100644
index 000000000000..f064305bf939
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registries_operations.py
@@ -0,0 +1,1116 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_by_subscription_request(
+    subscription_id: str, *, skip: Optional[str] = None, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url", "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/registries"
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_list_request(
+    resource_group_name: str, subscription_id: str, *, skip: Optional[str] = None, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str, registry_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url(
+            "registry_name", registry_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"
+        ),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(resource_group_name: str, registry_name: str, subscription_id: str, **kwargs: Any) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_update_request(
+    resource_group_name: str, registry_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url(
+            "registry_name", registry_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"
+        ),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str, registry_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url(
+            "registry_name", registry_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"
+        ),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class RegistriesOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`registries` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list_by_subscription(self, skip: Optional[str] = None, **kwargs: Any) -> Iterable["_models.Registry"]:
+        """List registries by subscription.
+
+        List registries by subscription.
+
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either Registry or the result of cls(response)
+        :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.RegistryTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_by_subscription_request(
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list_by_subscription.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                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("RegistryTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            _stream = False
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=_stream, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list_by_subscription.metadata = {
+        "url": "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/registries"
+    }
+
+    @distributed_trace
+    def list(self, resource_group_name: str, skip: Optional[str] = None, **kwargs: Any) -> Iterable["_models.Registry"]:
+        """List registries.
+
+        List registries.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either Registry or the result of cls(response)
+        :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.RegistryTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                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("RegistryTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            _stream = False
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=_stream, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries"
+    }
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        _stream = False
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=_stream, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @distributed_trace
+    def begin_delete(self, resource_group_name: str, registry_name: str, **kwargs: Any) -> LROPoller[None]:
+        """Delete registry.
+
+        Delete registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @distributed_trace
+    def get(self, resource_group_name: str, registry_name: str, **kwargs: Any) -> _models.Registry:
+        """Get registry.
+
+        Get registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Registry or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Registry
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        _stream = False
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=_stream, **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("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    def _update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: Union[_models.PartialRegistryPartialTrackedResource, IO],
+        **kwargs: Any
+    ) -> _models.Registry:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "PartialRegistryPartialTrackedResource")
+
+        request = build_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        _stream = False
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=_stream, **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)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if response.status_code == 202:
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @overload
+    def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: _models.PartialRegistryPartialTrackedResource,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.PartialRegistryPartialTrackedResource
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: 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 Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: 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 Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: Union[_models.PartialRegistryPartialTrackedResource, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Is either a
+         PartialRegistryPartialTrackedResource type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.PartialRegistryPartialTrackedResource or
+         IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: 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 Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("Registry", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    def _create_or_update_initial(
+        self, resource_group_name: str, registry_name: str, body: Union[_models.Registry, IO], **kwargs: Any
+    ) -> Optional[_models.Registry]:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[Optional[_models.Registry]] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "Registry")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        _stream = False
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=_stream, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201, 202]:
+            map_error(status_code=response.status_code, 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 = None
+        if response.status_code == 200:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: _models.Registry,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.Registry
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: 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 Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: 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 Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self, resource_group_name: str, registry_name: str, body: Union[_models.Registry, IO], **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Is either a Registry type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.Registry or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: 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 Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2023-04-01"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("Registry", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "azure-async-operation"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_schedules_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_schedules_operations.py
index ccf22fb3aed1..926a94c40b2a 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_schedules_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_schedules_operations.py
@@ -55,7 +55,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -92,7 +92,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -126,7 +126,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -160,7 +160,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -241,7 +241,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ScheduleResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -299,8 +299,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -331,7 +332,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -349,8 +350,9 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -403,7 +405,7 @@ def begin_delete(self, resource_group_name: str, workspace_name: str, name: str,
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -475,7 +477,7 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Schedule] = kwargs.pop("cls", None)
@@ -493,8 +495,9 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -529,7 +532,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -559,8 +562,9 @@ def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -688,7 +692,7 @@ def begin_create_or_update(
         :type workspace_name: str
         :param name: Schedule name. Required.
         :type name: str
-        :param body: Schedule definition. Is either a model type or a IO type. Required.
+        :param body: Schedule definition. Is either a Schedule type or a IO type. Required.
         :type body: ~azure.mgmt.machinelearningservices.models.Schedule or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -708,7 +712,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_usages_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_usages_operations.py
index 87b60742ab08..bde1d0f96e80 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_usages_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_usages_operations.py
@@ -45,7 +45,7 @@ def build_list_request(location: str, subscription_id: str, **kwargs: Any) -> Ht
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -103,7 +103,7 @@ def list(self, location: str, **kwargs: Any) -> Iterable["_models.Usage"]:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListUsagesResult] = kwargs.pop("cls", None)
@@ -158,8 +158,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_virtual_machine_sizes_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_virtual_machine_sizes_operations.py
index 4928f81dcedf..422824e3b4e4 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_virtual_machine_sizes_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_virtual_machine_sizes_operations.py
@@ -43,7 +43,7 @@ def build_list_request(location: str, subscription_id: str, **kwargs: Any) -> Ht
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -108,7 +108,7 @@ def list(self, location: str, **kwargs: Any) -> _models.VirtualMachineSizeListRe
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.VirtualMachineSizeListResult] = kwargs.pop("cls", None)
@@ -124,8 +124,9 @@ def list(self, location: str, **kwargs: Any) -> _models.VirtualMachineSizeListRe
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_connections_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_connections_operations.py
index c85caf24b6e8..173a3d1214e4 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_connections_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_connections_operations.py
@@ -47,7 +47,7 @@ def build_create_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -84,7 +84,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -118,7 +118,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -158,7 +158,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -290,7 +290,7 @@ def create(
         :param connection_name: Friendly name of the workspace connection. Required.
         :type connection_name: str
         :param parameters: The object for creating or updating a new workspace connection. Is either a
-         model type or a IO type. Required.
+         WorkspaceConnectionPropertiesV2BasicResource type or a IO type. Required.
         :type parameters:
          ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionPropertiesV2BasicResource or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
@@ -312,7 +312,7 @@ def create(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -342,8 +342,9 @@ def create(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -393,7 +394,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceConnectionPropertiesV2BasicResource] = kwargs.pop("cls", None)
@@ -411,8 +412,9 @@ def get(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -462,7 +464,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -480,8 +482,9 @@ def delete(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -528,7 +531,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -588,8 +591,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_features_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_features_operations.py
index 00c72c0a8b82..5cfdb08d7e2e 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_features_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_features_operations.py
@@ -47,7 +47,7 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -110,7 +110,7 @@ def list(self, resource_group_name: str, workspace_name: str, **kwargs: Any) ->
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListAmlUserFeatureResult] = kwargs.pop("cls", None)
@@ -166,8 +166,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspaces_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspaces_operations.py
index 3d8220b5fefa..fbb7c3003bec 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspaces_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspaces_operations.py
@@ -49,7 +49,7 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -82,7 +82,7 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -118,7 +118,7 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -151,7 +151,7 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -187,7 +187,7 @@ def build_list_by_resource_group_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -221,7 +221,7 @@ def build_diagnose_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -257,7 +257,7 @@ def build_list_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -290,7 +290,7 @@ def build_resync_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -323,7 +323,7 @@ def build_list_by_subscription_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -353,7 +353,7 @@ def build_list_notebook_access_token_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -386,7 +386,7 @@ def build_prepare_notebook_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -419,7 +419,7 @@ def build_list_storage_account_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -452,7 +452,7 @@ def build_list_notebook_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -485,7 +485,7 @@ def build_list_outbound_network_dependencies_endpoints_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2023-04-01"] = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -556,7 +556,7 @@ def get(self, resource_group_name: str, workspace_name: str, **kwargs: Any) -> _
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Workspace] = kwargs.pop("cls", None)
@@ -573,8 +573,9 @@ def get(self, resource_group_name: str, workspace_name: str, **kwargs: Any) -> _
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -609,7 +610,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -638,8 +639,9 @@ def _create_or_update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -746,7 +748,7 @@ def begin_create_or_update(
         :param workspace_name: Name of Azure Machine Learning workspace. Required.
         :type workspace_name: str
         :param parameters: The parameters for creating or updating a machine learning workspace. Is
-         either a model type or a IO type. Required.
+         either a Workspace type or a IO type. Required.
         :type parameters: ~azure.mgmt.machinelearningservices.models.Workspace or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -766,7 +768,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -827,7 +829,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -844,8 +846,9 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -886,7 +889,7 @@ def begin_delete(self, resource_group_name: str, workspace_name: str, **kwargs:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -946,7 +949,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -975,8 +978,9 @@ def _update_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1084,8 +1088,8 @@ def begin_update(
         :type resource_group_name: str
         :param workspace_name: Name of Azure Machine Learning workspace. Required.
         :type workspace_name: str
-        :param parameters: The parameters for updating a machine learning workspace. Is either a model
-         type or a IO type. Required.
+        :param parameters: The parameters for updating a machine learning workspace. Is either a
+         WorkspaceUpdateParameters type or a IO type. Required.
         :type parameters: ~azure.mgmt.machinelearningservices.models.WorkspaceUpdateParameters or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1105,7 +1109,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1171,7 +1175,7 @@ def list_by_resource_group(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceListResult] = kwargs.pop("cls", None)
@@ -1227,8 +1231,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -1263,7 +1268,7 @@ def _diagnose_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1295,8 +1300,9 @@ def _diagnose_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1419,8 +1425,8 @@ def begin_diagnose(
         :type resource_group_name: str
         :param workspace_name: Name of Azure Machine Learning workspace. Required.
         :type workspace_name: str
-        :param parameters: The parameter of diagnosing workspace health. Is either a model type or a IO
-         type. Default value is None.
+        :param parameters: The parameter of diagnosing workspace health. Is either a
+         DiagnoseWorkspaceParameters type or a IO type. Default value is None.
         :type parameters: ~azure.mgmt.machinelearningservices.models.DiagnoseWorkspaceParameters or IO
         :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
          Default value is None.
@@ -1442,7 +1448,7 @@ def begin_diagnose(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1519,7 +1525,7 @@ def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListWorkspaceKeysResult] = kwargs.pop("cls", None)
@@ -1536,8 +1542,9 @@ def list_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1572,7 +1579,7 @@ def _resync_keys_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1589,8 +1596,9 @@ def _resync_keys_initial(  # pylint: disable=inconsistent-return-statements
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1632,7 +1640,7 @@ def begin_resync_keys(self, resource_group_name: str, workspace_name: str, **kwa
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1688,7 +1696,7 @@ def list_by_subscription(self, skip: Optional[str] = None, **kwargs: Any) -> Ite
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceListResult] = kwargs.pop("cls", None)
@@ -1743,8 +1751,9 @@ def extract_data(pipeline_response):
         def get_next(next_link=None):
             request = prepare_request(next_link)
 
+            _stream = False
             pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-                request, stream=False, **kwargs
+                request, stream=_stream, **kwargs
             )
             response = pipeline_response.http_response
 
@@ -1788,7 +1797,7 @@ def list_notebook_access_token(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.NotebookAccessTokenResult] = kwargs.pop("cls", None)
@@ -1805,8 +1814,9 @@ def list_notebook_access_token(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1841,7 +1851,7 @@ def _prepare_notebook_initial(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[Optional[_models.NotebookResourceInfo]] = kwargs.pop("cls", None)
@@ -1858,8 +1868,9 @@ def _prepare_notebook_initial(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -1910,7 +1921,7 @@ def begin_prepare_notebook(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.NotebookResourceInfo] = kwargs.pop("cls", None)
@@ -1983,7 +1994,7 @@ def list_storage_account_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListStorageAccountKeysResult] = kwargs.pop("cls", None)
@@ -2000,8 +2011,9 @@ def list_storage_account_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -2049,7 +2061,7 @@ def list_notebook_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListNotebookKeysResult] = kwargs.pop("cls", None)
@@ -2066,8 +2078,9 @@ def list_notebook_keys(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
@@ -2119,7 +2132,7 @@ def list_outbound_network_dependencies_endpoints(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2023-04-01"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ExternalFQDNResponse] = kwargs.pop("cls", None)
@@ -2136,8 +2149,9 @@ def list_outbound_network_dependencies_endpoints(
         request = _convert_request(request)
         request.url = self._client.format_url(request.url)
 
+        _stream = False
         pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
-            request, stream=False, **kwargs
+            request, stream=_stream, **kwargs
         )
 
         response = pipeline_response.http_response
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aks_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aks_compute.py
index 4154ad04114e..4692f63ff9d7 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aks_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aks_compute.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/get/AKSCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/get/AKSCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aml_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aml_compute.py
index 9b60010544b5..59ea7d05e296 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aml_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aml_compute.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/get/AmlCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/get/AmlCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aks_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aks_compute.py
index 7bf0144d06dc..c4f5100e5166 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aks_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aks_compute.py
@@ -38,6 +38,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/createOrUpdate/BasicAKSCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/createOrUpdate/BasicAKSCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aml_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aml_compute.py
index d6cfcd909034..4e8789e3ef24 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aml_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aml_compute.py
@@ -55,6 +55,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/createOrUpdate/BasicAmlCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/createOrUpdate/BasicAmlCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_data_factory_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_data_factory_compute.py
index c7256aaa7266..322dec99f0ea 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_data_factory_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_data_factory_compute.py
@@ -38,6 +38,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/createOrUpdate/BasicDataFactoryCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/createOrUpdate/BasicDataFactoryCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/cancel.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/cancel.py
index 14832bc92a6c..be40485be034 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/cancel.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/cancel.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Job/cancel.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Job/cancel.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance.py
index 8be35ceb91e4..fe66677f7193 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/get/ComputeInstance.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/get/ComputeInstance.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_minimal.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_minimal.py
index f67029c8ca3c..575439d32563 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_minimal.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_minimal.py
@@ -41,6 +41,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/createOrUpdate/ComputeInstanceMinimal.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/createOrUpdate/ComputeInstanceMinimal.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_with_schedules.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_with_schedules.py
index c5ef7c03d6b9..4134df53117f 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_with_schedules.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_with_schedules.py
@@ -69,6 +69,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/createOrUpdate/ComputeInstanceWithSchedules.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/createOrUpdate/ComputeInstanceWithSchedules.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create.py
index 0d7e657dba30..cd04a676e0e1 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create.py
@@ -76,6 +76,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/create.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/create.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update.py
index d69a53bd684c..4f95d6a5bdef 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update.py
@@ -40,6 +40,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/PrivateEndpointConnection/createOrUpdate.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/PrivateEndpointConnection/createOrUpdate.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_system_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_system_created.py
new file mode 100644
index 000000000000..9fc7c5cc4965
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_system_created.py
@@ -0,0 +1,76 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python create_or_update_system_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.begin_create_or_update(
+        resource_group_name="test-rg",
+        registry_name="string",
+        body={
+            "identity": {"type": "None", "userAssignedIdentities": {"string": {}}},
+            "kind": "string",
+            "location": "string",
+            "properties": {
+                "description": "string",
+                "properties": {"string": "string"},
+                "regionDetails": [
+                    {
+                        "acrDetails": [
+                            {
+                                "systemCreatedAcrAccount": {
+                                    "acrAccountSku": "string",
+                                    "armResourceId": {"resourceId": "string"},
+                                }
+                            }
+                        ],
+                        "location": "string",
+                        "storageAccountDetails": [
+                            {
+                                "systemCreatedStorageAccount": {
+                                    "allowBlobPublicAccess": False,
+                                    "armResourceId": {"resourceId": "string"},
+                                    "storageAccountHnsEnabled": False,
+                                    "storageAccountType": "string",
+                                }
+                            }
+                        ],
+                    }
+                ],
+                "tags": {"string": "string"},
+            },
+            "sku": {"capacity": 1, "family": "string", "name": "string", "size": "string", "tier": "Basic"},
+            "tags": {},
+        },
+    ).result()
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Registries/createOrUpdate-SystemCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_user_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_user_created.py
new file mode 100644
index 000000000000..64e82af8fad5
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_user_created.py
@@ -0,0 +1,62 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python create_or_update_user_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.begin_create_or_update(
+        resource_group_name="test-rg",
+        registry_name="string",
+        body={
+            "identity": {"type": "None", "userAssignedIdentities": {"string": {}}},
+            "kind": "string",
+            "location": "string",
+            "properties": {
+                "description": "string",
+                "properties": {"string": "string"},
+                "regionDetails": [
+                    {
+                        "acrDetails": [{"userCreatedAcrAccount": {"armResourceId": {"resourceId": "string"}}}],
+                        "location": "string",
+                        "storageAccountDetails": [
+                            {"userCreatedStorageAccount": {"armResourceId": {"resourceId": "string"}}}
+                        ],
+                    }
+                ],
+                "tags": {"string": "string"},
+            },
+            "sku": {"capacity": 1, "family": "string", "name": "string", "size": "string", "tier": "Basic"},
+            "tags": {},
+        },
+    ).result()
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Registries/createOrUpdate-UserCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/delete.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/delete.py
index 1ab7cceee3e0..6332bd1c2462 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/delete.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/delete.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/delete.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/delete.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/diagnose.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/diagnose.py
index e7d53be6b011..3077375cd454 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/diagnose.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/diagnose.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/diagnose.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/diagnose.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get.py
index ae2325915702..4c2f399080e3 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/get.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/get.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_logs.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_logs.py
index 08ca1c985e42..2bae655720a4 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_logs.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_logs.py
@@ -39,6 +39,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/OnlineDeployment/getLogs.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/OnlineDeployment/getLogs.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_system_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_system_created.py
new file mode 100644
index 000000000000..fd5b114248b7
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_system_created.py
@@ -0,0 +1,41 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python get_system_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.get(
+        resource_group_name="test-rg",
+        registry_name="string",
+    )
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Registries/get-SystemCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_token.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_token.py
index ce3165223bbc..bdd0e74314a5 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_token.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_token.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/OnlineEndpoint/getToken.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/OnlineEndpoint/getToken.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_user_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_user_created.py
new file mode 100644
index 000000000000..bd86983df7e3
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_user_created.py
@@ -0,0 +1,41 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python get_user_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.get(
+        resource_group_name="test-rg",
+        registry_name="string",
+    )
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Registries/get-UserCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/kubernetes_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/kubernetes_compute.py
index 1c4abcb9c998..b09cac4f3f62 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/kubernetes_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/kubernetes_compute.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/get/KubernetesCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/get/KubernetesCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list.py
index 803939df104d..ccff07e2fa36 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list.py
@@ -36,6 +36,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Usage/list.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Usage/list.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_resource_group.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_resource_group.py
index ab77ccf41bcb..48ad1779487f 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_resource_group.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_resource_group.py
@@ -36,6 +36,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/listByResourceGroup.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/listByResourceGroup.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_subscription.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_subscription.py
index 021a94dc65b3..921bfb2b59f6 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_subscription.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_subscription.py
@@ -34,6 +34,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/listBySubscription.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/listBySubscription.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_keys.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_keys.py
index 4329788bf08c..9e0e183e60ef 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_keys.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_keys.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/listKeys.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/listKeys.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_nodes.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_nodes.py
index 08a92686eed7..66ee6a0f4bee 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_nodes.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_nodes.py
@@ -38,6 +38,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/listNodes.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/listNodes.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_notebook_access_token.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_notebook_access_token.py
index 2231f08361b1..d180f3ee30c2 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_notebook_access_token.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_notebook_access_token.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/listNotebookAccessToken.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/listNotebookAccessToken.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_secrets.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_secrets.py
index 6c9f9145642c..74fe51d50b35 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_secrets.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_secrets.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Datastore/listSecrets.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Datastore/listSecrets.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_skus.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_skus.py
index a1cd89f08c22..532af7db70b6 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_skus.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_skus.py
@@ -39,6 +39,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/OnlineDeployment/KubernetesOnlineDeployment/listSkus.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/OnlineDeployment/KubernetesOnlineDeployment/listSkus.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_storage_account_keys.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_storage_account_keys.py
index 84b7f19567c0..dbc963513d84 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_storage_account_keys.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_storage_account_keys.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/listStorageAccountKeys.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/listStorageAccountKeys.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_system_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_system_created.py
new file mode 100644
index 000000000000..d9ef5482f599
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_system_created.py
@@ -0,0 +1,41 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python list_system_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.list(
+        resource_group_name="test-rg",
+    )
+    for item in response:
+        print(item)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Registries/list-SystemCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_user_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_user_created.py
new file mode 100644
index 000000000000..193089df5170
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_user_created.py
@@ -0,0 +1,41 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python list_user_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.list(
+        resource_group_name="test-rg",
+    )
+    for item in response:
+        print(item)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Registries/list-UserCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/operations_list.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/operations_list.py
index 1ed3dd9d1da4..91510c53d5b5 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/operations_list.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/operations_list.py
@@ -34,6 +34,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/operationsList.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/operationsList.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/patch.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/patch.py
index 88d3d61b0099..344934f7929b 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/patch.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/patch.py
@@ -44,6 +44,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/patch.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/patch.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/prepare.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/prepare.py
index c6038615acb6..009f750c227c 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/prepare.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/prepare.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Notebook/prepare.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Notebook/prepare.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/regenerate_keys.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/regenerate_keys.py
index 7e4eb60a2634..376397f12e36 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/regenerate_keys.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/regenerate_keys.py
@@ -38,6 +38,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/OnlineEndpoint/regenerateKeys.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/OnlineEndpoint/regenerateKeys.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/restart.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/restart.py
index df42109f655d..3a911b9a6697 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/restart.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/restart.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/restart.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/restart.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resync_keys.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resync_keys.py
index 02b711c19636..2e671e8eab13 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resync_keys.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resync_keys.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/resyncKeys.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/resyncKeys.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/start.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/start.py
index b2754e281b74..ba8fe5e94d0c 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/start.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/start.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/start.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/start.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/stop.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/stop.py
index 4337f64aa158..0015e7d4156d 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/stop.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/stop.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/stop.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Compute/stop.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update.py
index 482c71b07c8c..f8b3fae5c4cc 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update.py
@@ -43,6 +43,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/update.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Workspace/update.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_system_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_system_created.py
new file mode 100644
index 000000000000..6658ff696c21
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_system_created.py
@@ -0,0 +1,48 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python update_system_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.begin_update(
+        resource_group_name="test-rg",
+        registry_name="string",
+        body={
+            "identity": {"type": "UserAssigned", "userAssignedIdentities": {"string": {}}},
+            "kind": "string",
+            "properties": {},
+            "sku": {"capacity": 1, "family": "string", "name": "string", "size": "string", "tier": "Premium"},
+            "tags": {},
+        },
+    ).result()
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Registries/update-SystemCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_user_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_user_created.py
new file mode 100644
index 000000000000..73dae5da8e4e
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_user_created.py
@@ -0,0 +1,48 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python update_user_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.begin_update(
+        resource_group_name="test-rg",
+        registry_name="string",
+        body={
+            "identity": {"type": "UserAssigned", "userAssignedIdentities": {"string": {}}},
+            "kind": "string",
+            "properties": {},
+            "sku": {"capacity": 1, "family": "string", "name": "string", "size": "string", "tier": "Premium"},
+            "tags": {},
+        },
+    ).result()
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Registries/update-UserCreated.json
+if __name__ == "__main__":
+    main()