/*expected_max_outputs=*/1,
                               cancellation_manager);
  auto outputs = parallel_device.Join(
      /*expected_output_shapes=*/{PartialTensorShape({})}, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  const std::vector<std::unique_ptr<ParallelTensor>>& handles = *outputs;
  const std::vector<int64_t>* shape;

  // computation to continue without blocking.
  //
  // The return status and value is the same as `Execute`.
  absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> Join(
      const std::vector<PartialTensorShape>& expected_output_shapes,
      TF_Status* status) const;

  void AsyncWait(TFE_Context* context, TF_Status* status) const;

  // Device strings for component devices that only include a

}

void TF_SetAttrShape(TF_OperationDescription* desc, const char* attr_name,
                     const int64_t* dims, int num_dims) {
  PartialTensorShape shape;
  if (num_dims >= 0) {
    shape = PartialTensorShape(
        ArraySlice<int64_t>(reinterpret_cast<const int64_t*>(dims), num_dims));
  }
  desc->node_builder.Attr(attr_name, shape);
}

  string bytes1, bytes2;
  tensorflow::TensorShapeProto proto;

  const int64_t pts1[] = {2, 4, -1, 8};
  tensorflow::PartialTensorShape(pts1).AsProto(&proto);
  proto.SerializeToString(&bytes1);

  const int64_t pts2[] = {1, 3, 5, 7};
  tensorflow::PartialTensorShape(pts2).AsProto(&proto);
  proto.SerializeToString(&bytes2);

  std::unique_ptr<const void*[]> list_ptrs;

*   Adding TensorFlow-iOS CocoaPod for symmetry with tensorflow-android.
*   Introduces base implementations of ClusterResolvers.
*   Unify memory representations of TensorShape and PartialTensorShape. As a
    consequence, tensors now have a maximum of 254 dimensions, not 255.
*   Changed references to LIBXSMM to use version 1.8.1.
*   TensorFlow Debugger (tfdbg):