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CONTRIBUTING.md
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
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README.md
[![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)
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SECURITY.md
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
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tensorflow/api_template.__init__.py
# ============================================================================== """ 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
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RELEASE.md
* 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
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