Enable running tfc.run() on notebook running from within a AI Platform hosted notebook.

Using AI Platform hosted notebooks, we created an Jupyter notebook with the model that we are were planning to train and saved it. We created a separate notebook in which we had our runner wrapping script similar to

import tensorflow_cloud as tfc

tfc.run(
    docker_config=tfc.DockerConfig(
        image_build_bucket="somebucket",
        parent_image="gcr.io/xyz"), 
    entry_point="model.ipynb",
    distribution_strategy="auto",
    worker_count=5,
    requirements_txt='requirements.txt',
    chief_config=tfc.COMMON_MACHINE_CONFIGS["CPU"],
    worker_config=tfc.COMMON_MACHINE_CONFIGS["CPU"],
    job_labels={
        "job": "kaggle_competition",
        "team": "base_line",
    },
    stream_logs=False
)

The run fails with error

/opt/conda/lib/python3.7/site-packages/tensorflow_cloud/core/preprocess.py in _get_colab_notebook_content()
    207 def _get_colab_notebook_content():
    208     """Returns the colab notebook python code contents."""
--> 209     response = _message.blocking_request("get_ipynb",
    210                                          request="",
    211                                          timeout_sec=200)

AttributeError: 'NoneType' object has no attribute 'blocking_request'

Would be nice to add support for this case were all requirements and a proper base image are directly provided for the remote run.

Start in tensorflow_cloud/core/preprocess.py at _get_colab_notebook_content() and trace how tfc.run() obtains notebook content. Reproduce the failure from an AI Platform hosted Jupyter notebook using the shown DockerConfig and entry_point settings. Done means the hosted-notebook case can run with the supplied requirements file and base image without relying on the unavailable Colab message interface.