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))

    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.

  ops->writable_file_ops->tell = tf_writable_file::Tell;
  ops->writable_file_ops->flush = tf_writable_file::Flush;
  ops->writable_file_ops->sync = tf_writable_file::Sync;
  ops->writable_file_ops->close = tf_writable_file::Close;

  ops->read_only_memory_region_ops = static_cast<TF_ReadOnlyMemoryRegionOps*>(
      plugin_memory_allocate(TF_READ_ONLY_MEMORY_REGION_OPS_SIZE));
  ops->read_only_memory_region_ops->cleanup =

// gradient function uses only the first of those values and ignores the rest.
// The entry, "FusedBatchNorm": [1, 2, 3, 4], indicates that only the gradient
// corresponding to index 0 is used, and the gradient values at indices 1-4 are
// ignored (and hence can be None). The backprop algorithm can then leverage
// this by not constructing zeros to pass for those indices.
std::unordered_map<string, std::unordered_set<int>>*