text: # -*- coding: utf-8 -*-
  # Copyright 2020 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  from collections import OrderedDict
  import os
  import re
  from typing import Dict, Optional, Sequence, Tuple, Type, Union
  import pkg_resources
  
  from google.api_core import client_options as client_options_lib
  from google.api_core import exceptions as core_exceptions
  from google.api_core import gapic_v1
  from google.api_core import retry as retries
  from google.auth import credentials as ga_credentials  # type: ignore
  from google.auth.transport import mtls  # type: ignore
  from google.auth.transport.grpc import SslCredentials  # type: ignore
  from google.auth.exceptions import MutualTLSChannelError  # type: ignore
  from google.oauth2 import service_account  # type: ignore
  
  try:
      OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault]
  except AttributeError:  # pragma: NO COVER
      OptionalRetry = Union[retries.Retry, object]  # type: ignore
  
  from google.api_core import operation as gac_operation  # type: ignore
  from google.api_core import operation_async  # type: ignore
  from google.cloud.aiplatform_v1.services.model_service import pagers
  from google.cloud.aiplatform_v1.types import deployed_model_ref
  from google.cloud.aiplatform_v1.types import encryption_spec
  from google.cloud.aiplatform_v1.types import explanation
  from google.cloud.aiplatform_v1.types import model
  from google.cloud.aiplatform_v1.types import model as gca_model
  from google.cloud.aiplatform_v1.types import model_evaluation
  from google.cloud.aiplatform_v1.types import model_evaluation_slice
  from google.cloud.aiplatform_v1.types import model_service
  from google.cloud.aiplatform_v1.types import operation as gca_operation
  from google.protobuf import empty_pb2  # type: ignore
  from google.protobuf import field_mask_pb2  # type: ignore
  from google.protobuf import struct_pb2  # type: ignore
  from google.protobuf import timestamp_pb2  # type: ignore
  from .transports.base import ModelServiceTransport, DEFAULT_CLIENT_INFO
  from .transports.grpc import ModelServiceGrpcTransport
  from .transports.grpc_asyncio import ModelServiceGrpcAsyncIOTransport
  
  
  class ModelServiceClientMeta(type):
      """Metaclass for the ModelService client.
  
      This provides class-level methods for building and retrieving
      support objects (e.g. transport) without polluting the client instance
      objects.
      """
  
      _transport_registry = OrderedDict()  # type: Dict[str, Type[ModelServiceTransport]]
      _transport_registry["grpc"] = ModelServiceGrpcTransport
      _transport_registry["grpc_asyncio"] = ModelServiceGrpcAsyncIOTransport
  
      def get_transport_class(cls, label: str = None,) -> Type[ModelServiceTransport]:
          """Returns an appropriate transport class.
  
          Args:
              label: The name of the desired transport. If none is
                  provided, then the first transport in the registry is used.
  
          Returns:
              The transport class to use.
          """
          # If a specific transport is requested, return that one.
          if label:
              return cls._transport_registry[label]
  
          # No transport is requested; return the default (that is, the first one
          # in the dictionary).
          return next(iter(cls._transport_registry.values()))
  
  
  class ModelServiceClient(metaclass=ModelServiceClientMeta):
      """A service for managing Vertex AI's machine learning Models."""
  
      @staticmethod
      def _get_default_mtls_endpoint(api_endpoint):
          """Converts api endpoint to mTLS endpoint.
  
          Convert "*.sandbox.googleapis.com" and "*.googleapis.com" to
          "*.mtls.sandbox.googleapis.com" and "*.mtls.googleapis.com" respectively.
          Args:
              api_endpoint (Optional[str]): the api endpoint to convert.
          Returns:
              str: converted mTLS api endpoint.
          """
          if not api_endpoint:
              return api_endpoint
  
          mtls_endpoint_re = re.compile(
              r"(?P<name>[^.]+)(?P<mtls>\.mtls)?(?P<sandbox>\.sandbox)?(?P<googledomain>\.googleapis\.com)?"
          )
  
          m = mtls_endpoint_re.match(api_endpoint)
          name, mtls, sandbox, googledomain = m.groups()
          if mtls or not googledomain:
              return api_endpoint
  
          if sandbox:
              return api_endpoint.replace(
                  "sandbox.googleapis.com", "mtls.sandbox.googleapis.com"
              )
  
          return api_endpoint.replace(".googleapis.com", ".mtls.googleapis.com")
  
      DEFAULT_ENDPOINT = "aiplatform.googleapis.com"
      DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__(  # type: ignore
          DEFAULT_ENDPOINT
      )
  
      @classmethod
      def from_service_account_info(cls, info: dict, *args, **kwargs):
          """Creates an instance of this client using the provided credentials
              info.
  
          Args:
              info (dict): The service account private key info.
              args: Additional arguments to pass to the constructor.
              kwargs: Additional arguments to pass to the constructor.
  
          Returns:
              ModelServiceClient: The constructed client.
          """
          credentials = service_account.Credentials.from_service_account_info(info)
          kwargs["credentials"] = credentials
          return cls(*args, **kwargs)
  
      @classmethod
      def from_service_account_file(cls, filename: str, *args, **kwargs):
          """Creates an instance of this client using the provided credentials
              file.
  
          Args:
              filename (str): The path to the service account private key json
                  file.
              args: Additional arguments to pass to the constructor.
              kwargs: Additional arguments to pass to the constructor.
  
          Returns:
              ModelServiceClient: The constructed client.
          """
          credentials = service_account.Credentials.from_service_account_file(filename)
          kwargs["credentials"] = credentials
          return cls(*args, **kwargs)
  
      from_service_account_json = from_service_account_file
  
      @property
      def transport(self) -> ModelServiceTransport:
          """Returns the transport used by the client instance.
  
          Returns:
              ModelServiceTransport: The transport used by the client
                  instance.
          """
          return self._transport
  
      @staticmethod
      def endpoint_path(project: str, location: str, endpoint: str,) -> str:
          """Returns a fully-qualified endpoint string."""
          return "projects/{project}/locations/{location}/endpoints/{endpoint}".format(
              project=project, location=location, endpoint=endpoint,
          )
  
      @staticmethod
      def parse_endpoint_path(path: str) -> Dict[str, str]:
          """Parses a endpoint path into its component segments."""
          m = re.match(
              r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/endpoints/(?P<endpoint>.+?)$",
              path,
          )
          return m.groupdict() if m else {}
  
      @staticmethod
      def model_path(project: str, location: str, model: str,) -> str:
          """Returns a fully-qualified model string."""
          return "projects/{project}/locations/{location}/models/{model}".format(
              project=project, location=location, model=model,
          )
  
      @staticmethod
      def parse_model_path(path: str) -> Dict[str, str]:
          """Parses a model path into its component segments."""
          m = re.match(
              r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/models/(?P<model>.+?)$",
              path,
          )
          return m.groupdict() if m else {}
  
      @staticmethod
      def model_evaluation_path(
          project: str, location: str, model: str, evaluation: str,
      ) -> str:
          """Returns a fully-qualified model_evaluation string."""
          return "projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}".format(
              project=project, location=location, model=model, evaluation=evaluation,
          )
  
      @staticmethod
      def parse_model_evaluation_path(path: str) -> Dict[str, str]:
          """Parses a model_evaluation path into its component segments."""
          m = re.match(
              r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/models/(?P<model>.+?)/evaluations/(?P<evaluation>.+?)$",
              path,
          )
          return m.groupdict() if m else {}
  
      @staticmethod
      def model_evaluation_slice_path(
          project: str, location: str, model: str, evaluation: str, slice: str,
      ) -> str:
          """Returns a fully-qualified model_evaluation_slice string."""
          return "projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}".format(
              project=project,
              location=location,
              model=model,
              evaluation=evaluation,
              slice=slice,
          )
  
      @staticmethod
      def parse_model_evaluation_slice_path(path: str) -> Dict[str, str]:
          """Parses a model_evaluation_slice path into its component segments."""
          m = re.match(
              r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/models/(?P<model>.+?)/evaluations/(?P<evaluation>.+?)/slices/(?P<slice>.+?)$",
              path,
          )
          return m.groupdict() if m else {}
  
      @staticmethod
      def training_pipeline_path(
          project: str, location: str, training_pipeline: str,
      ) -> str:
          """Returns a fully-qualified training_pipeline string."""
          return "projects/{project}/locations/{location}/trainingPipelines/{training_pipeline}".format(
              project=project, location=location, training_pipeline=training_pipeline,
          )
  
      @staticmethod
      def parse_training_pipeline_path(path: str) -> Dict[str, str]:
          """Parses a training_pipeline path into its component segments."""
          m = re.match(
              r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/trainingPipelines/(?P<training_pipeline>.+?)$",
              path,
          )
          return m.groupdict() if m else {}
  
      @staticmethod
      def common_billing_account_path(billing_account: str,) -> str:
          """Returns a fully-qualified billing_account string."""
          return "billingAccounts/{billing_account}".format(
              billing_account=billing_account,
          )
  
      @staticmethod
      def parse_common_billing_account_path(path: str) -> Dict[str, str]:
          """Parse a billing_account path into its component segments."""
          m = re.match(r"^billingAccounts/(?P<billing_account>.+?)$", path)
          return m.groupdict() if m else {}
  
      @staticmethod
      def common_folder_path(folder: str,) -> str:
          """Returns a fully-qualified folder string."""
          return "folders/{folder}".format(folder=folder,)
  
      @staticmethod
      def parse_common_folder_path(path: str) -> Dict[str, str]:
          """Parse a folder path into its component segments."""
          m = re.match(r"^folders/(?P<folder>.+?)$", path)
          return m.groupdict() if m else {}
  
      @staticmethod
      def common_organization_path(organization: str,) -> str:
          """Returns a fully-qualified organization string."""
          return "organizations/{organization}".format(organization=organization,)
  
      @staticmethod
      def parse_common_organization_path(path: str) -> Dict[str, str]:
          """Parse a organization path into its component segments."""
          m = re.match(r"^organizations/(?P<organization>.+?)$", path)
          return m.groupdict() if m else {}
  
      @staticmethod
      def common_project_path(project: str,) -> str:
          """Returns a fully-qualified project string."""
          return "projects/{project}".format(project=project,)
  
      @staticmethod
      def parse_common_project_path(path: str) -> Dict[str, str]:
          """Parse a project path into its component segments."""
          m = re.match(r"^projects/(?P<project>.+?)$", path)
          return m.groupdict() if m else {}
  
      @staticmethod
      def common_location_path(project: str, location: str,) -> str:
          """Returns a fully-qualified location string."""
          return "projects/{project}/locations/{location}".format(
              project=project, location=location,
          )
  
      @staticmethod
      def parse_common_location_path(path: str) -> Dict[str, str]:
          """Parse a location path into its component segments."""
          m = re.match(r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)$", path)
          return m.groupdict() if m else {}
  
      @classmethod
      def get_mtls_endpoint_and_cert_source(
          cls, client_options: Optional[client_options_lib.ClientOptions] = None
      ):
          """Return the API endpoint and client cert source for mutual TLS.
  
          The client cert source is determined in the following order:
          (1) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is not "true", the
          client cert source is None.
          (2) if `client_options.client_cert_source` is provided, use the provided one; if the
          default client cert source exists, use the default one; otherwise the client cert
          source is None.
  
          The API endpoint is determined in the following order:
          (1) if `client_options.api_endpoint` if provided, use the provided one.
          (2) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is "always", use the
          default mTLS endpoint; if the environment variabel is "never", use the default API
          endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
          use the default API endpoint.
  
          More details can be found at https://google.aip.dev/auth/4114.
  
          Args:
              client_options (google.api_core.client_options.ClientOptions): Custom options for the
                  client. Only the `api_endpoint` and `client_cert_source` properties may be used
                  in this method.
  
          Returns:
              Tuple[str, Callable[[], Tuple[bytes, bytes]]]: returns the API endpoint and the
                  client cert source to use.
  
          Raises:
              google.auth.exceptions.MutualTLSChannelError: If any errors happen.
          """
          if client_options is None:
              client_options = client_options_lib.ClientOptions()
          use_client_cert = os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false")
          use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto")
          if use_client_cert not in ("true", "false"):
              raise ValueError(
                  "Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be either `true` or `false`"
              )
          if use_mtls_endpoint not in ("auto", "never", "always"):
              raise MutualTLSChannelError(
                  "Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`"
              )
  
          # Figure out the client cert source to use.
          client_cert_source = None
          if use_client_cert == "true":
              if client_options.client_cert_source:
                  client_cert_source = client_options.client_cert_source
              elif mtls.has_default_client_cert_source():
                  client_cert_source = mtls.default_client_cert_source()
  
          # Figure out which api endpoint to use.
          if client_options.api_endpoint is not None:
              api_endpoint = client_options.api_endpoint
          elif use_mtls_endpoint == "always" or (
              use_mtls_endpoint == "auto" and client_cert_source
          ):
              api_endpoint = cls.DEFAULT_MTLS_ENDPOINT
          else:
              api_endpoint = cls.DEFAULT_ENDPOINT
  
          return api_endpoint, client_cert_source
  
      def __init__(
          self,
          *,
          credentials: Optional[ga_credentials.Credentials] = None,
          transport: Union[str, ModelServiceTransport, None] = None,
          client_options: Optional[client_options_lib.ClientOptions] = None,
          client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
      ) -> None:
          """Instantiates the model service client.
  
          Args:
              credentials (Optional[google.auth.credentials.Credentials]): The
                  authorization credentials to attach to requests. These
                  credentials identify the application to the service; if none
                  are specified, the client will attempt to ascertain the
                  credentials from the environment.
              transport (Union[str, ModelServiceTransport]): The
                  transport to use. If set to None, a transport is chosen
                  automatically.
              client_options (google.api_core.client_options.ClientOptions): Custom options for the
                  client. It won't take effect if a ``transport`` instance is provided.
                  (1) The ``api_endpoint`` property can be used to override the
                  default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT
                  environment variable can also be used to override the endpoint:
                  "always" (always use the default mTLS endpoint), "never" (always
                  use the default regular endpoint) and "auto" (auto switch to the
                  default mTLS endpoint if client certificate is present, this is
                  the default value). However, the ``api_endpoint`` property takes
                  precedence if provided.
                  (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
                  is "true", then the ``client_cert_source`` property can be used
                  to provide client certificate for mutual TLS transport. If
                  not provided, the default SSL client certificate will be used if
                  present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not
                  set, no client certificate will be used.
              client_info (google.api_core.gapic_v1.client_info.ClientInfo):
                  The client info used to send a user-agent string along with
                  API requests. If ``None``, then default info will be used.
                  Generally, you only need to set this if you're developing
                  your own client library.
  
          Raises:
              google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport
                  creation failed for any reason.
          """
          if isinstance(client_options, dict):
              client_options = client_options_lib.from_dict(client_options)
          if client_options is None:
              client_options = client_options_lib.ClientOptions()
  
          api_endpoint, client_cert_source_func = self.get_mtls_endpoint_and_cert_source(
              client_options
          )
  
          api_key_value = getattr(client_options, "api_key", None)
          if api_key_value and credentials:
              raise ValueError(
                  "client_options.api_key and credentials are mutually exclusive"
              )
  
          # Save or instantiate the transport.
          # Ordinarily, we provide the transport, but allowing a custom transport
          # instance provides an extensibility point for unusual situations.
          if isinstance(transport, ModelServiceTransport):
              # transport is a ModelServiceTransport instance.
              if credentials or client_options.credentials_file or api_key_value:
                  raise ValueError(
                      "When providing a transport instance, "
                      "provide its credentials directly."
                  )
              if client_options.scopes:
                  raise ValueError(
                      "When providing a transport instance, provide its scopes "
                      "directly."
                  )
              self._transport = transport
          else:
              import google.auth._default  # type: ignore
  
              if api_key_value and hasattr(
                  google.auth._default, "get_api_key_credentials"
              ):
                  credentials = google.auth._default.get_api_key_credentials(
                      api_key_value
                  )
  
              Transport = type(self).get_transport_class(transport)
              self._transport = Transport(
                  credentials=credentials,
                  credentials_file=client_options.credentials_file,
                  host=api_endpoint,
                  scopes=client_options.scopes,
                  client_cert_source_for_mtls=client_cert_source_func,
                  quota_project_id=client_options.quota_project_id,
                  client_info=client_info,
                  always_use_jwt_access=True,
              )
  
      def upload_model(
          self,
          request: Union[model_service.UploadModelRequest, dict] = None,
          *,
          parent: str = None,
          model: gca_model.Model = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> gac_operation.Operation:
          r"""Uploads a Model artifact into Vertex AI.
  
  
          .. code-block::
  
              from google.cloud import aiplatform_v1
  
              def sample_upload_model():
                  # Create a client
                  client = aiplatform_v1.ModelServiceClient()
  
                  # Initialize request argument(s)
                  model = aiplatform_v1.Model()
                  model.display_name = "display_name_value"
  
                  request = aiplatform_v1.UploadModelRequest(
                      parent="parent_value",
                      model=model,
                  )
  
                  # Make the request
                  operation = client.upload_model(request=request)
  
                  print("Waiting for operation to complete...")
  
                  response = operation.result()
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1.types.UploadModelRequest, dict]):
                  The request object. Request message for
                  [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel].
              parent (str):
                  Required. The resource name of the Location into which
                  to upload the Model. Format:
                  ``projects/{project}/locations/{location}``
  
                  This corresponds to the ``parent`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              model (google.cloud.aiplatform_v1.types.Model):
                  Required. The Model to create.
                  This corresponds to the ``model`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              google.api_core.operation.Operation:
                  An object representing a long-running operation.
  
                  The result type for the operation will be
                  :class:`google.cloud.aiplatform_v1.types.UploadModelResponse`
                  Response message of
                  [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel]
                  operation.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([parent, model])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a model_service.UploadModelRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, model_service.UploadModelRequest):
              request = model_service.UploadModelRequest(request)
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
              if parent is not None:
                  request.parent = parent
              if model is not None:
                  request.model = model
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[self._transport.upload_model]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # Wrap the response in an operation future.
          response = gac_operation.from_gapic(
              response,
              self._transport.operations_client,
              model_service.UploadModelResponse,
              metadata_type=model_service.UploadModelOperationMetadata,
          )
  
          # Done; return the response.
          return response
  
      def get_model(
          self,
          request: Union[model_service.GetModelRequest, dict] = None,
          *,
          name: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> model.Model:
          r"""Gets a Model.
  
  
          .. code-block::
  
              from google.cloud import aiplatform_v1
  
              def sample_get_model():
                  # Create a client
                  client = aiplatform_v1.ModelServiceClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1.GetModelRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  response = client.get_model(request=request)
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1.types.GetModelRequest, dict]):
                  The request object. Request message for
                  [ModelService.GetModel][google.cloud.aiplatform.v1.ModelService.GetModel].
              name (str):
                  Required. The name of the Model resource. Format:
                  ``projects/{project}/locations/{location}/models/{model}``
  
                  This corresponds to the ``name`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              google.cloud.aiplatform_v1.types.Model:
                  A trained machine learning Model.
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([name])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a model_service.GetModelRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, model_service.GetModelRequest):
              request = model_service.GetModelRequest(request)
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
              if name is not None:
                  request.name = name
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[self._transport.get_model]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # Done; return the response.
          return response
  
      def list_models(
          self,
          request: Union[model_service.ListModelsRequest, dict] = None,
          *,
          parent: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> pagers.ListModelsPager:
          r"""Lists Models in a Location.
  
  
          .. code-block::
  
              from google.cloud import aiplatform_v1
  
              def sample_list_models():
                  # Create a client
                  client = aiplatform_v1.ModelServiceClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1.ListModelsRequest(
                      parent="parent_value",
                  )
  
                  # Make the request
                  page_result = client.list_models(request=request)
  
                  # Handle the response
                  for response in page_result:
                      print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1.types.ListModelsRequest, dict]):
                  The request object. Request message for
                  [ModelService.ListModels][google.cloud.aiplatform.v1.ModelService.ListModels].
              parent (str):
                  Required. The resource name of the Location to list the
                  Models from. Format:
                  ``projects/{project}/locations/{location}``
  
                  This corresponds to the ``parent`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              google.cloud.aiplatform_v1.services.model_service.pagers.ListModelsPager:
                  Response message for
                  [ModelService.ListModels][google.cloud.aiplatform.v1.ModelService.ListModels]
  
                  Iterating over this object will yield results and
                  resolve additional pages automatically.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([parent])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a model_service.ListModelsRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, model_service.ListModelsRequest):
              request = model_service.ListModelsRequest(request)
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
              if parent is not None:
                  request.parent = parent
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[self._transport.list_models]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # This method is paged; wrap the response in a pager, which provides
          # an `__iter__` convenience method.
          response = pagers.ListModelsPager(
              method=rpc, request=request, response=response, metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      def update_model(
          self,
          request: Union[model_service.UpdateModelRequest, dict] = None,
          *,
          model: gca_model.Model = None,
          update_mask: field_mask_pb2.FieldMask = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> gca_model.Model:
          r"""Updates a Model.
  
  
          .. code-block::
  
              from google.cloud import aiplatform_v1
  
              def sample_update_model():
                  # Create a client
                  client = aiplatform_v1.ModelServiceClient()
  
                  # Initialize request argument(s)
                  model = aiplatform_v1.Model()
                  model.display_name = "display_name_value"
  
                  request = aiplatform_v1.UpdateModelRequest(
                      model=model,
                  )
  
                  # Make the request
                  response = client.update_model(request=request)
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1.types.UpdateModelRequest, dict]):
                  The request object. Request message for
                  [ModelService.UpdateModel][google.cloud.aiplatform.v1.ModelService.UpdateModel].
              model (google.cloud.aiplatform_v1.types.Model):
                  Required. The Model which replaces
                  the resource on the server.
  
                  This corresponds to the ``model`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              update_mask (google.protobuf.field_mask_pb2.FieldMask):
                  Required. The update mask applies to the resource. For
                  the ``FieldMask`` definition, see
                  [google.protobuf.FieldMask][google.protobuf.FieldMask].
  
                  This corresponds to the ``update_mask`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              google.cloud.aiplatform_v1.types.Model:
                  A trained machine learning Model.
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([model, update_mask])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a model_service.UpdateModelRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, model_service.UpdateModelRequest):
              request = model_service.UpdateModelRequest(request)
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
              if model is not None:
                  request.model = model
              if update_mask is not None:
                  request.update_mask = update_mask
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[self._transport.update_model]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata(
                  (("model.name", request.model.name),)
              ),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # Done; return the response.
          return response
  
      def delete_model(
          self,
          request: Union[model_service.DeleteModelRequest, dict] = None,
          *,
          name: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> gac_operation.Operation:
          r"""Deletes a Model.
  
          A model cannot be deleted if any
          [Endpoint][google.cloud.aiplatform.v1.Endpoint] resource has a
          [DeployedModel][google.cloud.aiplatform.v1.DeployedModel] based
          on the model in its
          [deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
          field.
  
  
  
          .. code-block::
  
              from google.cloud import aiplatform_v1
  
              def sample_delete_model():
                  # Create a client
                  client = aiplatform_v1.ModelServiceClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1.DeleteModelRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  operation = client.delete_model(request=request)
  
                  print("Waiting for operation to complete...")
  
                  response = operation.result()
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1.types.DeleteModelRequest, dict]):
                  The request object. Request message for
                  [ModelService.DeleteModel][google.cloud.aiplatform.v1.ModelService.DeleteModel].
              name (str):
                  Required. The name of the Model resource to be deleted.
                  Format:
                  ``projects/{project}/locations/{location}/models/{model}``
  
                  This corresponds to the ``name`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              google.api_core.operation.Operation:
                  An object representing a long-running operation.
  
                  The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
                     empty messages in your APIs. A typical example is to
                     use it as the request or the response type of an API
                     method. For instance:
  
                        service Foo {
                           rpc Bar(google.protobuf.Empty) returns
                           (google.protobuf.Empty);
  
                        }
  
                     The JSON representation for Empty is empty JSON
                     object {}.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([name])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a model_service.DeleteModelRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, model_service.DeleteModelRequest):
              request = model_service.DeleteModelRequest(request)
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
              if name is not None:
                  request.name = name
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[self._transport.delete_model]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # Wrap the response in an operation future.
          response = gac_operation.from_gapic(
              response,
              self._transport.operations_client,
              empty_pb2.Empty,
              metadata_type=gca_operation.DeleteOperationMetadata,
          )
  
          # Done; return the response.
          return response
  
      def export_model(
          self,
          request: Union[model_service.ExportModelRequest, dict] = None,
          *,
          name: str = None,
          output_config: model_service.ExportModelRequest.OutputConfig = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> gac_operation.Operation:
          r"""Exports a trained, exportable Model to a location specified by
          the user. A Model is considered to be exportable if it has at
          least one [supported export
          format][google.cloud.aiplatform.v1.Model.supported_export_formats].
  
  
  
          .. code-block::
  
              from google.cloud import aiplatform_v1
  
              def sample_export_model():
                  # Create a client
                  client = aiplatform_v1.ModelServiceClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1.ExportModelRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  operation = client.export_model(request=request)
  
                  print("Waiting for operation to complete...")
  
                  response = operation.result()
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1.types.ExportModelRequest, dict]):
                  The request object. Request message for
                  [ModelService.ExportModel][google.cloud.aiplatform.v1.ModelService.ExportModel].
              name (str):
                  Required. The resource name of the
                  Model to export.
  
                  This corresponds to the ``name`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              output_config (google.cloud.aiplatform_v1.types.ExportModelRequest.OutputConfig):
                  Required. The desired output location
                  and configuration.
  
                  This corresponds to the ``output_config`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              google.api_core.operation.Operation:
                  An object representing a long-running operation.
  
                  The result type for the operation will be
                  :class:`google.cloud.aiplatform_v1.types.ExportModelResponse`
                  Response message of
                  [ModelService.ExportModel][google.cloud.aiplatform.v1.ModelService.ExportModel]
                  operation.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([name, output_config])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a model_service.ExportModelRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, model_service.ExportModelRequest):
              request = model_service.ExportModelRequest(request)
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
              if name is not None:
                  request.name = name
              if output_config is not None:
                  request.output_config = output_config
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[self._transport.export_model]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # Wrap the response in an operation future.
          response = gac_operation.from_gapic(
              response,
              self._transport.operations_client,
              model_service.ExportModelResponse,
              metadata_type=model_service.ExportModelOperationMetadata,
          )
  
          # Done; return the response.
          return response
  
      def get_model_evaluation(
          self,
          request: Union[model_service.GetModelEvaluationRequest, dict] = None,
          *,
          name: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> model_evaluation.ModelEvaluation:
          r"""Gets a ModelEvaluation.
  
  
          .. code-block::
  
              from google.cloud import aiplatform_v1
  
              def sample_get_model_evaluation():
                  # Create a client
                  client = aiplatform_v1.ModelServiceClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1.GetModelEvaluationRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  response = client.get_model_evaluation(request=request)
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1.types.GetModelEvaluationRequest, dict]):
                  The request object. Request message for
                  [ModelService.GetModelEvaluation][google.cloud.aiplatform.v1.ModelService.GetModelEvaluation].
              name (str):
                  Required. The name of the ModelEvaluation resource.
                  Format:
                  ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}``
  
                  This corresponds to the ``name`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              google.cloud.aiplatform_v1.types.ModelEvaluation:
                  A collection of metrics calculated by
                  comparing Model's predictions on all of
                  the test data against annotations from
                  the test data.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([name])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a model_service.GetModelEvaluationRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, model_service.GetModelEvaluationRequest):
              request = model_service.GetModelEvaluationRequest(request)
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
              if name is not None:
                  request.name = name
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[self._transport.get_model_evaluation]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # Done; return the response.
          return response
  
      def list_model_evaluations(
          self,
          request: Union[model_service.ListModelEvaluationsRequest, dict] = None,
          *,
          parent: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> pagers.ListModelEvaluationsPager:
          r"""Lists ModelEvaluations in a Model.
  
  
          .. code-block::
  
              from google.cloud import aiplatform_v1
  
              def sample_list_model_evaluations():
                  # Create a client
                  client = aiplatform_v1.ModelServiceClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1.ListModelEvaluationsRequest(
                      parent="parent_value",
                  )
  
                  # Make the request
                  page_result = client.list_model_evaluations(request=request)
  
                  # Handle the response
                  for response in page_result:
                      print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1.types.ListModelEvaluationsRequest, dict]):
                  The request object. Request message for
                  [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1.ModelService.ListModelEvaluations].
              parent (str):
                  Required. The resource name of the Model to list the
                  ModelEvaluations from. Format:
                  ``projects/{project}/locations/{location}/models/{model}``
  
                  This corresponds to the ``parent`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              google.cloud.aiplatform_v1.services.model_service.pagers.ListModelEvaluationsPager:
                  Response message for
                  [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1.ModelService.ListModelEvaluations].
  
                  Iterating over this object will yield results and
                  resolve additional pages automatically.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([parent])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a model_service.ListModelEvaluationsRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, model_service.ListModelEvaluationsRequest):
              request = model_service.ListModelEvaluationsRequest(request)
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
              if parent is not None:
                  request.parent = parent
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[self._transport.list_model_evaluations]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # This method is paged; wrap the response in a pager, which provides
          # an `__iter__` convenience method.
          response = pagers.ListModelEvaluationsPager(
              method=rpc, request=request, response=response, metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      def get_model_evaluation_slice(
          self,
          request: Union[model_service.GetModelEvaluationSliceRequest, dict] = None,
          *,
          name: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> model_evaluation_slice.ModelEvaluationSlice:
          r"""Gets a ModelEvaluationSlice.
  
  
          .. code-block::
  
              from google.cloud import aiplatform_v1
  
              def sample_get_model_evaluation_slice():
                  # Create a client
                  client = aiplatform_v1.ModelServiceClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1.GetModelEvaluationSliceRequest(
                      name="name_value",
                  )
  
                  # Make the request
                  response = client.get_model_evaluation_slice(request=request)
  
                  # Handle the response
                  print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1.types.GetModelEvaluationSliceRequest, dict]):
                  The request object. Request message for
                  [ModelService.GetModelEvaluationSlice][google.cloud.aiplatform.v1.ModelService.GetModelEvaluationSlice].
              name (str):
                  Required. The name of the ModelEvaluationSlice resource.
                  Format:
                  ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}``
  
                  This corresponds to the ``name`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              google.cloud.aiplatform_v1.types.ModelEvaluationSlice:
                  A collection of metrics calculated by
                  comparing Model's predictions on a slice
                  of the test data against ground truth
                  annotations.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([name])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a model_service.GetModelEvaluationSliceRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, model_service.GetModelEvaluationSliceRequest):
              request = model_service.GetModelEvaluationSliceRequest(request)
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
              if name is not None:
                  request.name = name
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[
              self._transport.get_model_evaluation_slice
          ]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # Done; return the response.
          return response
  
      def list_model_evaluation_slices(
          self,
          request: Union[model_service.ListModelEvaluationSlicesRequest, dict] = None,
          *,
          parent: str = None,
          retry: OptionalRetry = gapic_v1.method.DEFAULT,
          timeout: float = None,
          metadata: Sequence[Tuple[str, str]] = (),
      ) -> pagers.ListModelEvaluationSlicesPager:
          r"""Lists ModelEvaluationSlices in a ModelEvaluation.
  
  
          .. code-block::
  
              from google.cloud import aiplatform_v1
  
              def sample_list_model_evaluation_slices():
                  # Create a client
                  client = aiplatform_v1.ModelServiceClient()
  
                  # Initialize request argument(s)
                  request = aiplatform_v1.ListModelEvaluationSlicesRequest(
                      parent="parent_value",
                  )
  
                  # Make the request
                  page_result = client.list_model_evaluation_slices(request=request)
  
                  # Handle the response
                  for response in page_result:
                      print(response)
  
          Args:
              request (Union[google.cloud.aiplatform_v1.types.ListModelEvaluationSlicesRequest, dict]):
                  The request object. Request message for
                  [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.ListModelEvaluationSlices].
              parent (str):
                  Required. The resource name of the ModelEvaluation to
                  list the ModelEvaluationSlices from. Format:
                  ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}``
  
                  This corresponds to the ``parent`` field
                  on the ``request`` instance; if ``request`` is provided, this
                  should not be set.
              retry (google.api_core.retry.Retry): Designation of what errors, if any,
                  should be retried.
              timeout (float): The timeout for this request.
              metadata (Sequence[Tuple[str, str]]): Strings which should be
                  sent along with the request as metadata.
  
          Returns:
              google.cloud.aiplatform_v1.services.model_service.pagers.ListModelEvaluationSlicesPager:
                  Response message for
                  [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.ListModelEvaluationSlices].
  
                  Iterating over this object will yield results and
                  resolve additional pages automatically.
  
          """
          # Create or coerce a protobuf request object.
          # Quick check: If we got a request object, we should *not* have
          # gotten any keyword arguments that map to the request.
          has_flattened_params = any([parent])
          if request is not None and has_flattened_params:
              raise ValueError(
                  "If the `request` argument is set, then none of "
                  "the individual field arguments should be set."
              )
  
          # Minor optimization to avoid making a copy if the user passes
          # in a model_service.ListModelEvaluationSlicesRequest.
          # There's no risk of modifying the input as we've already verified
          # there are no flattened fields.
          if not isinstance(request, model_service.ListModelEvaluationSlicesRequest):
              request = model_service.ListModelEvaluationSlicesRequest(request)
              # If we have keyword arguments corresponding to fields on the
              # request, apply these.
              if parent is not None:
                  request.parent = parent
  
          # Wrap the RPC method; this adds retry and timeout information,
          # and friendly error handling.
          rpc = self._transport._wrapped_methods[
              self._transport.list_model_evaluation_slices
          ]
  
          # Certain fields should be provided within the metadata header;
          # add these here.
          metadata = tuple(metadata) + (
              gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
          )
  
          # Send the request.
          response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  
          # This method is paged; wrap the response in a pager, which provides
          # an `__iter__` convenience method.
          response = pagers.ListModelEvaluationSlicesPager(
              method=rpc, request=request, response=response, metadata=metadata,
          )
  
          # Done; return the response.
          return response
  
      def __enter__(self):
          return self
  
      def __exit__(self, type, value, traceback):
          """Releases underlying transport's resources.
  
          .. warning::
              ONLY use as a context manager if the transport is NOT shared
              with other clients! Exiting the with block will CLOSE the transport
              and may cause errors in other clients!
          """
          self.transport.close()
  
  
  try:
      DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
          gapic_version=pkg_resources.get_distribution(
              "google-cloud-aiplatform",
          ).version,
      )
  except pkg_resources.DistributionNotFound:
      DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo()
  
  
  __all__ = ("ModelServiceClient",)
  