init files creation process and is subsequently stored in the wheel file after
the build. It also contains a few paths that cannot be directly imported. These
paths point to attributes or sub-modules within a module's namespace, but they
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

```

    ```bash
    export flags="--config=opt -k"
    ```

    If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and
    add the `cuda` option flag

    ```bash
    export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"
    export flags="--config=opt --config=cuda -k"
    ```

    For example, to run all tests under tensorflow/python, do:

updating requirements for multiple minor versions of Python.

It takes in a file with a set of dependencies, and produces a more detailed
requirements file for each version, with hashes specified for each
dependency required, as well as their sub-dependencies.

### How to update/add requirements

By default, the name of the base requirements file is `requirements.in`, but it

    `TFCI` env param pointing to a list of `env` files.
2.  `utilities/setup.sh`, initialized by all top-level scripts, reads and sets
    values from those `TFCI` paths.
    -   `set -a` / `set -o allexport` exports the variables from `env` files so
        all scripts can use them.
    -   `utilities/setup_docker.sh` creates a container called `tf` with all

    GRU(recurrent_activation='hard_sigmoid', reset_after=False) to fallback to
    1.x behavior.

*   `CUDNN_INSTALL_PATH`, `TENSORRT_INSTALL_PATH`, `NCCL_INSTALL_PATH`,
    `NCCL_HDR_PATH` are deprecated. Use `TF_CUDA_PATHS` instead which supports a
    comma-separated list of base paths that are searched to find CUDA libraries
    and headers.

Refer to our

dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.0.0.dev
tb-nightly ~= 2.17.0.a

potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.

}

# Do a bazel query specifically for the licenses checker. It searches for
# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \
  | grep -e "^//" -e "^@" \
  | grep -E -v "^//tensorflow" \

# Use Python 3.X as installed in container image
build:release_linux_base --action_env PYTHON_BIN_PATH="/usr/bin/python3"
build:release_linux_base --action_env PYTHON_LIB_PATH="/usr/lib/tf_python"
build:release_linux_base --python_path="/usr/bin/python3"
# Set Clang as compiler. Use the actual path to clang installed in container.
build:release_cpu_linux_base --repo_env=CC="/usr/lib/llvm-17/bin/clang"

    $old_path = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\"); \
    [Environment]::SetEnvironmentVariable(\"PATH\", $old_path + \";C:\VS\VC\Tools\MSVC\14.33.31629\bin\Hostx64\x64;C:\VS\Common7\Tools;C:\VS\MSBuild\Current\Bin\", \"Machine\");

# Add signtool.exe to the PATH. Note this path may need to be edited if updates
# are made to the Windows 10 SDK.