code: def validate(
      entry_point,
      requirements_txt,
      distribution_strategy,
      chief_config,
      worker_config,
      worker_count,
      entry_point_args,
      stream_logs,
      docker_image_build_bucket,
      called_from_notebook,
      job_labels=None,
      service_account=None,
      docker_parent_image=None,
  ):
      """Validates the inputs.
  
      Args:
          entry_point: Optional string. File path to the python file or iPython
              notebook that contains the TensorFlow code.
          requirements_txt: Optional string. File path to requirements.txt file
              containing aditionally pip dependencies, if any.
          distribution_strategy: 'auto' or None. Defaults to 'auto'.
              'auto' means we will take care of creating a Tensorflow
              distribution strategy instance based on the machine configurations
              provided using the `chief_config`, `worker_config` and
              `worker_count` params.
          chief_config: `MachineConfig` that represents the configuration
              for the chief worker in a distribution cluster.
          worker_config: `MachineConfig` that represents the configuration
              for the workers in a distribution cluster.
          worker_count: Optional integer that represents the number of workers in
              a distribution cluster. Defaults to 0. This count does not include
              the chief worker.
          entry_point_args: Optional list of strings. Defaults to None.
              Command line arguments to pass to the `entry_point` program.
          stream_logs: Boolean flag which when enabled streams logs back from
              the cloud job.
          docker_image_build_bucket: Optional string. Cloud storage bucket name.
          called_from_notebook: Boolean. True if the API is run in a
              notebook environment.
          job_labels: Dict of str: str. Labels to organize jobs. See
              [resource-labels](
              https://cloud.google.com/ai-platform/training/docs/resource-labels).
          service_account: The email address of a user-managed service account
              to be used for training instead of the service account that AI
              Platform Training uses by default. see [custom-service-account](
              https://cloud.google.com/ai-platform/training/docs/custom-service-account)
          docker_parent_image: Optional parent Docker image to use.
              Defaults to None.
  
      Raises:
          ValueError: if any of the inputs is invalid.
      """
      _validate_files(entry_point, requirements_txt)
      _validate_distribution_strategy(distribution_strategy)
      _validate_cluster_config(
          chief_config, worker_count, worker_config, docker_parent_image
      )
      _validate_job_labels(job_labels or {})
      _validate_other_args(
          entry_point_args,
          stream_logs,
          docker_image_build_bucket,
          called_from_notebook,
      )
      _validate_service_account(service_account)
  
file_path: cloud/src/python/tensorflow_cloud/core/validate.py
imports: import os
  from .None import gcp
  from .None import machine_config
label_desc: Use image node_id 1 for calling a global function with example usage: validate(entry_point, requirements_txt, distribution_strategy, chief_config, worker_config, worker_count, entry_point_args, stream_logs, docker_image_build_bucket, called_from_notebook, job_labels, service_account, docker_parent_image) without return types