**Description**

The operator is arguably unnecessarily limited to AI Platform’s standard images. The only change that is required to lift this constraint is making `package_uris` and `training_python_module` optional with default values `[]` and `None`, respectively. Then, using `master_config`, one can supply `imageUri` and run any image of choice.

**Use case / motivation**

This will open up for running arbitrary images on AI Platform.

**Are you willing to submit a PR?**

If the above sounds reasonable, I can open pull requests.
tests/providers/google/cloud/operators/test_mlengine.py
http_error_code = 403
        hook_instance = mock_hook.return_value
        hook_instance.create_job.side_effect = HttpError(
            resp=httplib2.Response({'status': http_error_code}), content=b'Forbidden'
        )
        with self.assertRaises(HttpError) as context:
            training_op = MLEngineStartTrainingJobOperator(**self.TRAINING_DEFAULT_ARGS)
            training_op.execute(None)
        mock_hook.assert_called_once_with(
            gcp_conn_id='google_cloud_default',
            delegate_to=None,
            impersonation_chain=None,
        )
        
        self.assertEqual(len(hook_instance.mock_calls), 1)
        hook_instance.create_job.assert_called_once_with(
            project_id='test-project', job=self.TRAINING_INPUT, use_existing_job_fn=ANY
        )
        self.assertEqual(http_error_code, context.exception.resp.status)
    @patch('airflow.providers.google.cloud.operators.mlengine.MLEngineHook')
    def test_failed_job_error(self, mock_hook):