environ_cp, 'TF_NEED_ROCM', 'ROCm', False, bazel_config_name='rocm')
  if (environ_cp.get('TF_NEED_ROCM') == '1' and
      'LD_LIBRARY_PATH' in environ_cp and
      environ_cp.get('LD_LIBRARY_PATH') != '1'):
    write_action_env_to_bazelrc('LD_LIBRARY_PATH',
                                environ_cp.get('LD_LIBRARY_PATH'))

  if (environ_cp.get('TF_NEED_ROCM') == '1' and environ_cp.get('ROCM_PATH')):

    ```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:

     * If you have above configuration and using _**Ubuntu/Linux**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.
       * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).
     * If error still persists then, apparently your CPU model does not support AVX instruction sets.

build:cuda_clang_official --action_env=GCC_HOST_COMPILER_PATH="/dt9/usr/bin/gcc"
build:cuda_clang_official --action_env=CLANG_CUDA_COMPILER_PATH="/usr/lib/llvm-17/bin/clang"
build:cuda_clang_official --action_env=LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
build:cuda_clang_official --crosstool_top="@sigbuild-r2.17-clang_config_cuda//crosstool:toolchain"

# Build with nvcc for CUDA and clang for host