**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
super().setUp()
        self.dag = DAG(
            'test_dag',
            default_args={
                'owner': 'airflow',
                'start_date': DEFAULT_DATE,
                'end_date': DEFAULT_DATE,
            },
            schedule_interval='@daily',
        )
    @patch('airflow.providers.google.cloud.operators.mlengine.MLEngineHook')
    def test_success_with_model(self, mock_hook):
        input_with_model = self.INPUT_MISSING_ORIGIN.copy()
        input_with_model['modelName'] = 'projects/test-project/models/test_model'
        success_message = self.SUCCESS_MESSAGE_MISSING_INPUT.copy()
        success_message['predictionInput'] = input_with_model
        hook_instance = mock_hook.return_value
        hook_instance.get_job.side_effect = HttpError(
            resp=httplib2.Response({'status': 404}), content=b'some bytes'
        )
        hook_instance.create_job.return_value = success_message