#   "rbe", if you have set up your system to use RBE (see further below).
#
# Here is how to choose your TFCI value:
# 1. A Python version must come first, because other scripts reference it.
#      Ex. py39  -- Python 3.9
#      Ex. py310 -- Python 3.10
#      Ex. py311 -- Python 3.11
#      Ex. py312 -- Python 3.12
# 2. Choose the platform, which corresponds to the version of TensorFlow to

  // There are two ways to represent compact row-major data
  // 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.
  // Here we choose option 2, since some frameworks didn't handle the strides
  // argument properly.
  dlm_tensor->dl_tensor.strides = stride_arr->data();

  dlm_tensor->dl_tensor.byte_offset =

    return status.status;
  }

  absl::StatusOr<bool> ShallPinToThisDevice(
      const ImmediateExecutionOperation* op) override {
    TF_Status status;
    // Let this custom device choose the device to pin this op on if it
    // implements the pinning function.
    if (device_.shall_pin_to_this_device != nullptr) {
      return device_.shall_pin_to_this_device(tensorflow::wrap(op), &status);
    }

        as arg with default value `None` to choose the I/O device for loading
        models and weights.
    *   Update `tf.saved_model.SaveOptions` with
        [`experimental_io_device`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/saved_model/SaveOptions?hl=en)
        as arg with default value `None` to choose the I/O device for saving
        models and weights.

      environ_cp['TF_NEED_CLANG'] = str(choose_compiler(environ_cp))
      if environ_cp.get('TF_NEED_CLANG') == '1':
        clang_compiler_path = set_clang_compiler_path(environ_cp)
        clang_version = retrieve_clang_version(clang_compiler_path)
        disable_clang_offsetof_extension(clang_version)
    if is_windows():
      environ_cp['TF_NEED_CLANG'] = str(choose_compiler_Win(environ_cp))