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

Physical GPUs or TPUs can also be the target of attacks. [Published
research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it
might be possible to use side channel attacks on the GPU to leak data from other

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by:

1. Creating a PR and observing the presubmit test results
2. Running the CI scripts locally, as explained below

        `.predict` is available for Cloud TPUs, Cloud TPU, for all types of
        Keras models (sequential, functional and subclassing models).
    *   Automatic outside compilation is now enabled for Cloud TPUs. This allows
        `tf.summary` to be used more conveniently with Cloud TPUs.
    *   Dynamic batch sizes with DistributionStrategy and Keras are supported on
        Cloud TPUs.