def METHOD_NAME(
    type,
    project,
    location,
    model_name,
    payload,
    job_configuration_query_override,
    gcp_resources,
    executor_input,
):
  """Create and poll bigquery evaluation model job till it reaches a final state.
  This follows the typical launching logic:
  1. Read if the bigquery job already exists in gcp_resources
     - If already exists, jump to step 3 and poll the job status. This happens
     if the launcher container experienced unexpected termination, such as
     preemption
  2. Deserialize the payload into the job spec and create the bigquery job
  3. Poll the bigquery job status every
  job_remote_runner._POLLING_INTERVAL_IN_SECONDS seconds
     - If the bigquery job is succeeded, return succeeded
     - If the bigquery job is pending/running, continue polling the status
  Also retry on ConnectionError up to
  job_remote_runner._CONNECTION_ERROR_RETRY_LIMIT times during the poll.
  Args:
      type: BigQuery model prediction job type.
      project: Project to launch the query job.
      location: location to launch the query job. For more details, see
        https://cloud.google.com/bigquery/docs/locations#specifying_your_location
      model_name: BigQuery ML model name for prediction. For more details, see
      https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-predict#predict_model_name
      payload: A json serialized Job proto. For more details, see
        https://cloud.google.com/bigquery/docs/reference/rest/v2/Job
      job_configuration_query_override: A json serialized JobConfigurationQuery
        proto. For more details, see
        https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#JobConfigurationQuery
      gcp_resources: File path for storing `gcp_resources` output parameter.
      executor_input: A json serialized pipeline executor input.
  """
  job_configuration_query_override_json = json.loads(
      job_configuration_query_override, strict=False
  )
  job_configuration_query_override_json['query'] = (
      'SELECT * FROM ML.TRAINING_INFO(MODEL %s)'
      % (bigquery_util.back_quoted_if_needed(model_name))
  )
  creds, _ = google.auth.default()
  job_uri = bigquery_util.check_if_job_exists(gcp_resources)
  if job_uri is None:
    job_uri = bigquery_util.create_query_job(
        project,
        location,
        payload,
        json.dumps(job_configuration_query_override_json),
        creds,
        gcp_resources,
    )
  # Poll bigquery job status until finished.
  job = bigquery_util.poll_job(job_uri, creds)
  logging.info('Getting query result for job %s', job['id'])
  _, job_id = job['id'].split('.')
  query_results = bigquery_util.get_query_results(
      project, job_id, location, creds
  )
  artifact_utils.update_output_artifact(
      executor_input,
      'ml_training_info',
      '',
      {
          bigquery_util.ARTIFACT_PROPERTY_KEY_SCHEMA: query_results[
              bigquery_util.ARTIFACT_PROPERTY_KEY_SCHEMA
          ],
          bigquery_util.ARTIFACT_PROPERTY_KEY_ROWS: query_results[
              bigquery_util.ARTIFACT_PROPERTY_KEY_ROWS
          ],
      },
  )