class_docstr: Hook for Google Cloud Vertex AI Custom Job APIs.
docstr: Returns CustomContainerTrainingJob object.
docstr_tok: ["Returns","CustomContainerTrainingJob","object","."]
code: def get_custom_container_training_job(
      self,
      display_name: str,
      container_uri: str,
      command: Sequence[str] = [],
      model_serving_container_image_uri: str | None = None,
      model_serving_container_predict_route: str | None = None,
      model_serving_container_health_route: str | None = None,
      model_serving_container_command: Sequence[str] | None = None,
      model_serving_container_args: Sequence[str] | None = None,
      model_serving_container_environment_variables: dict[str, str]
      | None = None,
      model_serving_container_ports: Sequence[int] | None = None,
      model_description: str | None = None,
      model_instance_schema_uri: str | None = None,
      model_parameters_schema_uri: str | None = None,
      model_prediction_schema_uri: str | None = None,
      project: str | None = None,
      location: str | None = None,
      labels: dict[str, str] | None = None,
      training_encryption_spec_key_name: str | None = None,
      model_encryption_spec_key_name: str | None = None,
      staging_bucket: str | None = None,
  ) -> CustomContainerTrainingJob:
      """Returns CustomContainerTrainingJob object."""
      return CustomContainerTrainingJob(
          display_name=display_name,
          container_uri=container_uri,
          command=command,
          model_serving_container_image_uri=model_serving_container_image_uri,
          model_serving_container_predict_route=model_serving_container_predict_route,
          model_serving_container_health_route=model_serving_container_health_route,
          model_serving_container_command=model_serving_container_command,
          model_serving_container_args=model_serving_container_args,
          model_serving_container_environment_variables=model_serving_container_environment_variables,
          model_serving_container_ports=model_serving_container_ports,
          model_description=model_description,
          model_instance_schema_uri=model_instance_schema_uri,
          model_parameters_schema_uri=model_parameters_schema_uri,
          model_prediction_schema_uri=model_prediction_schema_uri,
          project=project,
          location=location,
          credentials=self.get_credentials(),
          labels=labels,
          training_encryption_spec_key_name=training_encryption_spec_key_name,
          model_encryption_spec_key_name=model_encryption_spec_key_name,
          staging_bucket=staging_bucket,
      )
  
imports: from __future__ import annotations
  import warnings
  from typing import TYPE_CHECKING, Sequence
  from google.api_core.client_options import ClientOptions
  from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
  from google.cloud.aiplatform import CustomContainerTrainingJob, CustomPythonPackageTrainingJob, CustomTrainingJob, datasets, models
  from google.cloud.aiplatform_v1 import JobServiceClient, PipelineServiceClient
  from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
  from airflow.providers.google.common.consts import CLIENT_INFO
  from airflow.providers.google.common.hooks.base_google import GoogleBaseHook
label_desc: Use image node_id 4 for calling the CustomJobHook obj's underlying member method code with example usage: obj.get_custom_container_training_job(display_name, container_uri, command, model_serving_container_image_uri, model_serving_container_predict_route, model_serving_container_health_route, model_serving_container_command, model_serving_container_args, model_serving_container_environment_variables, model_serving_container_ports, model_description, model_instance_schema_uri, model_parameters_schema_uri, model_prediction_schema_uri, project, location, labels, training_encryption_spec_key_name, model_encryption_spec_key_name, staging_bucket) and returns: CustomContainerTrainingJob