**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.
airflow/providers/google/cloud/operators/mlengine.py
"""
    Start a Google Cloud ML Engine prediction job.
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:MLEngineStartBatchPredictionJobOperator`
    NOTE: For model origin, users should consider exactly one from the
    three options below:
    1. Populate ``uri`` field only, which should be a GCS location that
       points to a tensorflow savedModel directory.
    2. Populate ``model_name`` field only, which refers to an existing
       model, and the default version of the model will be used.
    3. Populate both ``model_name`` and ``version_name`` fields, which
       refers to a specific version of a specific model.