don't correspond to an actual file or directory on the filesystem. The list of
such paths is stored in the packages_for_skip variable and will be skipped
during the test.

##### How to Build

```
bazel build //:test_import_api_packages
```

##### How to Run

```
bazel test //:test_import_api_packages --test_output=all

```

To specify the file via a Bazel command argument, use the following:

```
--repo_env=REQUIREMENTS_FILE_NAME=my_requirements.in
```

### How to run the updater

```
bash updater.sh
```

## How to add a new Python version

Note: Updating the
[rules-python](https://github.com/bazelbuild/rules_python/releases) version may
be required before going through the steps below. This is due to the new Python

2)  Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).

```

-   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

// APIs specific to Eager modes
// -----------------------------------------------------------------------------

// Temporary APIs till we figure out how to create scalar valued Eager
// tensors and how to get value out of eager abstract tensors.
TF_AbstractTensor* TF_CreateAbstractTensorFromEagerTensor(TFE_TensorHandle* t,
                                                          TF_Status* s);

    how we can reproduce your findings. For memory corruption triggerable in
    TensorFlow models, please demonstrate an exploit against one of Alphabet's
    models in <https://tfhub.dev/>
  - An explanation of who can exploit this vulnerability, and what they gain
    when doing so. Write an attack scenario that demonstrates how your issue

  TFE_Executor* executor = TFE_ContextGetExecutorForThread(ctx);
  TFE_ExecutorWaitForAllPendingNodes(executor, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // TODO(b/136478427): Figure out how to correctly shut the server down.
  worker_server.release();

  // Update the server def with a new set of names (worker instead of
  // localhost).
  tensorflow::ServerDef updated_server_def = GetServerDef("worker", 2);

***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository.

### Contributing code

If you have improvements to TensorFlow, send us your pull requests! For those
just getting started, GitHub has a

# Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is
# relevant. All of the GPU test machines have CUDA installed via other means,
# so I am not sure how to verify that the dependencies themselves are valid for
# the moment.
if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then
  echo "Checking to make sure tensorflow[and-cuda] is installable..."

    string expected_msg =
        "No gradient defined for op: TestOpWithNoGradient. Please see "
        "https://www.tensorflow.org/code/"
        "tensorflow/cc/gradients/README.md"
        " for instructions on how to add C++ gradients.";
    EXPECT_EQ(expected_msg, TF_Message(s_));
  }

  // Run the graph and ensure that the gradient values are as expected.
  void RunGraphsAndCompareOutputs(TF_Output* grad_outputs,