namespace tf_random_access_file {

// TODO(b/139060984): Implement later

}  // namespace tf_random_access_file

// SECTION 2. Implementation for `TF_WritableFile`
// ----------------------------------------------------------------------------
namespace tf_writable_file {

// TODO(b/139060984): Implement later

}  // namespace tf_writable_file

// SECTION 3. Implementation for `TF_ReadOnlyMemoryRegion`

      if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;
    }
  }

  // Create an inference context with dummy values, which will be updated later.
  InferenceContext c(TF_GRAPH_DEF_VERSION, node_def, op_reg_data->op_def,
                     std::vector<ShapeHandle>(num_inputs), input_tensors_vector,
                     {},

  if (*requested_placement == '\0') {
    TF_SetStatus(
        status, TF_INTERNAL,
        "Ops must be placed on the parallel device explicitly, or their inputs "
        "first un-packed. Got an un-placed op with an input placed on the "
        "parallel device.");
    return;
  }
  TFE_Context* context = TFE_OpGetContext(original_op, status);

  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Pack 3 variable handles into one TFE_TensorHandle.
  // When remote is false, function device is placed on task0. Handle types are
  // REMOTE, REMOTE, LOCAL on task0. When remote is true, function device is
  // placed on task1, Handle types are LOCAL, REMOTE, LOCAL on task1.
  int num_replicas = 3;
  std::vector<TFE_TensorHandle*> handles = {h0, h1, h2};

    // Returning null tensors from a tf.function is not supported, so we keep
    // track of indices in the model's outputs are nullptr in this set.
    // The FunctionDef only outputs the non-null tensors. We later pad the
    // function op outputs to have nullptrs at the `null_indices`.
    absl::flat_hash_set<int> null_indices;
    {
      AbstractContextPtr func_ctx(BuildFunction(fn_name));

//     but plugins didn't provide will be set to `nullptr` values and core
//     TensorFlow will know to not call these on behalf of users;
//   * increased security as plugins will not be able to alter function table
//     after loading up. Thus, malicious plugins can't alter functionality to
//     probe for gadgets inside core TensorFlow. We can even protect the area
//     of memory where the copies reside to not allow any more writes to it

          bucket.c_str(), object.c_str(), stat->base.length,
          stat->generation_number, stat->base.mtime_nsec);
  return TF_SetStatus(status, TF_OK, "");
}

// TODO(vnvo2409): Implement later
void NewRandomAccessFile(const TF_Filesystem* filesystem, const char* path,
                         TF_RandomAccessFile* file, TF_Status* status) {
  std::string bucket, object;