rare occasion we may reject it.

**3. Review**

-   For a valid PR, reviewer (person familiar with the code/functionality)
    checks if the PR looks good or needs additional changes.
-   If all looks good, the reviewer will approve the PR.
-   If a change is needed, the contributor is requested to make the suggested
    change.
-   You make the change and submit it for the review again.

Project Steward will determine whether the Code of Conduct was violated, and will issue an appropriate sanction, possibly including a written warning or expulsion from the project, project sponsored spaces, or project forums. We ask that you make a good-faith effort to resolve your conflict via the conflict resolution policy before submitting a report.

Violations of the Code of Conduct can occur in any setting, even those unrelated to the project. We will only consider complaints about conduct...

If the multitenant design allows sharing models, make sure that tenants and
users are aware of the security risks detailed here and that they are going to
be practically running code provided by other users. Currently there are no good
ways to detect malicious models/graphs/checkpoints, so the recommended way to
mitigate the risk in this scenario is to sandbox the model execution.

### Hardware attacks

   DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
   ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
   (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
   LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
   ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT

*   Add support for 3d convolutions and pooling.
*   Update gRPC release to 0.14.
*   Eigen version upgrade.
*   Switch to eigen thread pool
*   `tf.nn.moments()` now accepts a `shift` argument. Shifting by a good
    estimate of the mean improves numerical stability. Also changes the behavior
    of the `shift` argument to `tf.nn.sufficient_statistics()`.
*   Performance improvements
*   Many bugfixes

# Linux builds. Instructions are available in the official documentation:
# https://www.tensorflow.org/install/source#install_clang_recommended_linux_only
# Another good option is to use our Docker containers to build and test TF:
# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tf_sig_build_dockerfiles.
build:unsupported_cpu_linux --config=avx_linux