Training with a custom service account needs a new field named service account
trainingInput:
  serviceAccount: CUSTOM_SERVICE_ACCOUNT

https://cloud.google.com/ai-platform/training/docs/custom-service-account


tests/providers/google/cloud/operators/test_mlengine.py
mock_hook.assert_called_once_with(
            'google_cloud_default',
            None,
            impersonation_chain=None,
        )
        hook_instance.create_job.assert_called_once_with(
            project_id='test-project',
            job={'jobId': 'test_prediction', 'predictionInput': input_with_uri},
            use_existing_job_fn=ANY,
        )
        self.assertEqual(success_message['predictionOutput'], prediction_output)
    def test_invalid_model_origin(self):
        
        task_args = self.BATCH_PREDICTION_DEFAULT_ARGS.copy()
        task_args['uri'] = 'gs://fake-uri/saved_model'
        task_args['model_name'] = 'fake_model'
        with self.assertRaises(AirflowException) as context:
            MLEngineStartBatchPredictionJobOperator(**task_args).execute(None)
        self.assertEqual(
            'Ambiguous model origin: Both uri and ' 'model/version name are provided.', str(context.exception)
        )
        
        task_args = self.BATCH_PREDICTION_DEFAULT_ARGS.copy()
        task_args['uri'] = 'gs://fake-uri/saved_model'
        task_args['model_name'] = 'fake_model'
        task_args['version_name'] = 'fake_version'
        with self.assertRaises(AirflowException) as context:
            MLEngineStartBatchPredictionJobOperator(**task_args).execute(None)
        self.assertEqual(
            'Ambiguous model origin: Both uri and ' 'model/version name are provided.', str(context.exception)