non-backward-compatible API changes without a major release. Reviewers of
    your pull request will comment on any API compatibility issues
    [following API review practices](https://github.com/tensorflow/community/blob/master/governance/api-reviews.md).
*   When you contribute a new feature to TensorFlow, the maintenance burden is

[![PyPI](https://badge.fury.io/py/tensorflow.svg)](https://badge.fury.io/py/tensorflow)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4724125.svg)](https://doi.org/10.5281/zenodo.4724125)
[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/1486/badge)](https://bestpractices.coreinfrastructure.org/projects/1486)

serving. We also provide guidelines on what constitutes a vulnerability in
TensorFlow and how to report them.

This document applies to other repositories in the TensorFlow organization,
covering security practices for the entirety of the TensorFlow ecosystem.

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to

# ==============================================================================
"""
Top-level module of TensorFlow. By convention, we refer to this module as
`tf` instead of `tensorflow`, following the common practice of importing
TensorFlow via the command `import tensorflow as tf`.

The primary function of this module is to import all of the public TensorFlow
interfaces into a single place. The interfaces themselves are located in

                       const ComputeFunc& compute_func, TF_Status* status) {
    if (max_age_ == 0) {
      return compute_func(key, value, status);
    }

    // Note: we hold onto mu_ for the rest of this function. In practice, this
    // is okay, as stat requests are typically fast, and concurrent requests are
    // often for the same file. Future work can split this up into one lock per

*   Robust model deployment in production on any platform.
*   Powerful experimentation for research.
*   API simplification by reducing duplication and removing deprecated
    endpoints.

For details on best practices with 2.0, see
[the Effective 2.0 guide](https://www.tensorflow.org/beta/guide/effective_tf2)

For information on upgrading your existing TensorFlow 1.x models, please refer
to our