**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
VERSION_DEFAULT_ARGS = {
        'project_id': 'test-project',
        'model_name': 'test-model',
        'task_id': 'test-version',
    }
    @patch('airflow.providers.google.cloud.operators.mlengine.MLEngineHook')
    def test_success_create_version(self, mock_hook):
        success_response = {'name': 'some-name', 'done': True}
        hook_instance = mock_hook.return_value
        hook_instance.create_version.return_value = success_response
        training_op = MLEngineManageVersionOperator(version=TEST_VERSION, **self.VERSION_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_version.assert_called_once_with(
            project_id='test-project', model_name='test-model', version_spec=TEST_VERSION
        )
class TestMLEngineCreateVersion(unittest.TestCase):