python tf_doctest.py --file=<file_path>
    ```

    This will run it using your installed version of TensorFlow. To be sure
    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

        optimizer.
    *   **Learning rate schedule access.** When using a `LearningRateSchedule`,
        The new optimizer's `learning_rate` property returns the
        current learning rate value instead of a `LearningRateSchedule` object
        as before. If you need to access the `LearningRateSchedule` object,
        please use `optimizer._learning_rate`.

and [C++](https://www.tensorflow.org/api_docs/cc) APIs, as well as a
non-guaranteed backward compatible API for
[other languages](https://www.tensorflow.org/api_docs).

Keep up-to-date with release announcements and security updates by subscribing
to
[******@****.***](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce).
See all the [mailing lists](https://www.tensorflow.org/community/forums).