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  """Vertex custom job parameter model."""
  
  from typing import Any, Dict, Optional
  
  from pydantic import BaseModel
  
  
  class VertexCustomJobParameters(BaseModel):
      """Settings for the Vertex custom job parameters.
  
      Attributes:
          accelerator_type: Defines which accelerator (GPU, TPU) is used for the
              job. Check out out this table to see which accelerator
              type and count are compatible with your chosen machine type:
              https://cloud.google.com/vertex-ai/docs/training/configure-compute#gpu-compatibility-table.
          accelerator_count: Defines number of accelerators to be used for the
              job. Check out out this table to see which accelerator
              type and count are compatible with your chosen machine type:
              https://cloud.google.com/vertex-ai/docs/training/configure-compute#gpu-compatibility-table.
          machine_type: Machine type specified here
              https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types.
          boot_disk_size_gb: Size of the boot disk in GB. (Default: 100)
              https://cloud.google.com/vertex-ai/docs/training/configure-compute#boot_disk_options
          boot_disk_type: Type of the boot disk. (Default: pd-ssd)
              https://cloud.google.com/vertex-ai/docs/training/configure-compute#boot_disk_options
          persistent_resource_id: The ID of the persistent resource to use for the job.
              If empty (default), the job will not use a persistent resource.
              When using a persistent resource, you must also specify a service_account.
              Conversely, when explicitly setting this to an empty string, you
              should not specify a service_account (ZenML will handle this automatically).
              https://cloud.google.com/vertex-ai/docs/training/persistent-resource-overview
          service_account: Specifies the service account to be used.
              This is required when using a persistent_resource_id, and
              should not be set when persistent_resource_id="".
          additional_training_job_args: Additional arguments to pass to the create_custom_training_job_from_component
              function. This allows passing any additional parameters supported by the Google
              Cloud Pipeline Components library without requiring ZenML to update its API.
              Note: If you specify parameters in this dictionary that are also defined as explicit
              attributes (like machine_type or boot_disk_size_gb), the values in this dictionary
              will override the explicit values.
              See: https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/custom_job.html
      """
  
      accelerator_type: Optional[str] = None
      accelerator_count: int = 0
      machine_type: str = "n1-standard-4"
      boot_disk_size_gb: int = 100
      boot_disk_type: str = "pd-ssd"
      persistent_resource_id: Optional[str] = None
      service_account: Optional[str] = None
      additional_training_job_args: Dict[str, Any] = {}
  