* It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
         * All you need is a good internet connection and you are all set.
      * Try to build TF from sources by changing CPU optimization flags.
   
   *Please let us know if this helps.*

        "//learning/pathways/...",
        "//learning/serving/contrib/tfrt/mlir/canonical_ops/...",
        "//learning/serving/experimental/remote_predict/...",
        "//perftools/accelerators/xprof/convert/...",
        "//perftools/accelerators/xprof/integration_tests/...",
        "//smartass/brain/...",
        "//tensorflow/...",
        "//tensorflow_decision_forests/...",
        "//tensorflow_federated/...",

implementation bugs that might allow attackers to leave malicious code running
and leak or tamper with applications from other users. Please report
vulnerabilities to the vendor of the affected hardware accelerator.

## Reporting vulnerabilities

### Vulnerabilities in TensorFlow

This document covers different use cases for TensorFlow together with comments

        `StatefulNnApiDelegate::Options::max_number_delegated_partitions` to 3.
    *   Add capability to disable `NNAPI` CPU and check `NNAPI` Errno.
    *   Fix crashes when using `NNAPI` with target accelerator specified with
        model containing Conv2d or FullyConnected or LSTM nodes with quantized
        weights.
    *   Fix `ANEURALNETWORKS_BAD_DATA` execution failures with