return absl::OkStatus();
  }
  absl::Status SetAttrShape(const char* attr_name, const int64_t* dims,
                            const int num_dims) override {
    PartialTensorShape shape;
    if (num_dims >= 0) {
      shape = PartialTensorShape(ArraySlice<int64_t>(
          reinterpret_cast<const int64_t*>(dims), num_dims));
    }
    op_->node_builder.Attr(attr_name, shape);
    return absl::OkStatus();
  }

absl::Status ParallelTensor::Shape(const std::vector<int64_t>** shape) const {
  if (!shape_.has_value()) {
    TF_Status status;
    PartialTensorShape combined_shape;
    TF_RETURN_IF_ERROR(unwrap(tensors_[0].get())->Shape(&combined_shape));

    for (const TensorHandlePtr& component : tensors_) {
      PartialTensorShape component_shape;
      TF_RETURN_IF_ERROR(unwrap(component.get())->Shape(&component_shape));
      if (combined_shape.dims() < 0 ||

                               /*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