components.reserve(underlying_devices_.size());
    for (int j = 0; j < underlying_devices_.size(); ++j) {
      components.push_back(std::move(per_device_output_tensors[j][i]));
    }
    if (expected_output_shapes[i].IsFullyDefined()) {
      per_device_outputs.push_back(ParallelTensor::FromTensorHandles(
          *this, std::move(components),
          absl::Span<const int64_t>(expected_output_shapes[i].dim_sizes()),

  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  std::vector<TensorHandlePtr> vector_handles;
  vector_handles.reserve(2);
  vector_handles.push_back(std::move(two_vector));
  vector_handles.push_back(std::move(three_vector));
  std::unique_ptr<ParallelTensor> unknown_length_vector =
      ParallelTensor::FromTensorHandles(
          parallel_device, std::move(vector_handles), status.get());

    for (int i = 0; i < noutputs; ++i) {
      output_names_vec.push_back(string(output_names[i]));
    }
  }

  // Process control output names.
  std::vector<string> control_output_names_vec;
  if (control_output_names) {
    control_output_names_vec.reserve(ncontrol_outputs);
    for (int i = 0; i < ncontrol_outputs; ++i) {
      control_output_names_vec.push_back(string(control_output_names[i]));
    }
  }

    return shape;
  }
  shape.reserve(rank);
  for (int i = 0; i < rank; ++i) {
    int64_t dim;
    *status = handle.Dim(i, &dim);
    if (!status->ok()) {
      return shape;
    }
    shape.push_back(dim);
  }
  return shape;
}

}  // namespace

extern "C" {

TF_CAPI_EXPORT extern TFE_TensorDebugInfo* TFE_TensorHandleTensorDebugInfo(
    TFE_TensorHandle* h, TF_Status* status) {

          absl::get<TFE_TensorHandle*>(inputs[i]), status));
    }
    std::vector<MaybeParallelTensorOwned> result_content;
    result_content.reserve(1);
    result_content.push_back(ParallelTensor::FromTensorHandles(
        parallel_device, std::move(components), status));
    if (TF_GetCode(status) != TF_OK) return result;
    result.emplace(std::move(result_content));
    return result;

  forward_op_->inputs.push_back(input);
  return absl::OkStatus();
}
absl::Status AddInputList(AbstractOperation* op_,
                          absl::Span<AbstractTensorHandle* const> inputs,
                          ForwardOperation* forward_op_) {
  TF_RETURN_IF_ERROR(op_->AddInputList(inputs));
  for (auto input : inputs) {
    forward_op_->inputs.push_back(input);
  }
  return absl::OkStatus();
}

    TF_Function* func = TF_FunctionImportFunctionDef(
        binary_proto_buf.data(), binary_proto_buf.size(), status);
    if (!status->status.ok()) return {};
    ret.push_back(UniqueFuncPtr(func, TF_DeleteFunction));
  }
  return ret;
}

TF_Tensor* TF_DequeueNamedTensor(TF_Session* session, int tensor_id,
                                 TF_Status* status) {
  assert(session);

        lengths_vector.reserve(s_size);
        for (int i = 0; i < s_size; ++i) {
          const string& v = default_value.list().s(i);
          values_vector.push_back(v.data());
          lengths_vector.push_back(v.size());
        }
        TFE_OpSetAttrStringList(op, attr_name, values_vector.data(),
                                lengths_vector.data(), s_size);
      }

      // Int

    }
    auto* t = dyn_cast<GraphTensor>(outputs[0]);
    if (!t) {
      return tensorflow::errors::InvalidArgument(
          "Unable to cast input to GraphTensor");
    }
    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  absl::Status Finalize(OutputList* outputs, AbstractFunction** f) override {

    TFE_TensorHandle* input_eager = TestScalarTensorHandle(eager_ctx, 2.0f);
    func_args.push_back(TF_CreateAbstractTensorFromEagerTensor(input_eager, s));
    ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
    input_eager = TestScalarTensorHandle(eager_ctx, 3.0f);
    func_args.push_back(TF_CreateAbstractTensorFromEagerTensor(input_eager, s));
    ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  }