.NewSubScope(child_scope_name);

    if (dx != nullptr) {
      std::vector<tensorflow::Output> dx_arg = OutputsFromTFOutputs(dx, ny);
      status->status =
          AddSymbolicGradients(scope, y_arg, x_arg, dx_arg, &dy_arg);
    } else {
      status->status = AddSymbolicGradients(scope, y_arg, x_arg, &dy_arg);
    }

    // Update g->name_map with the name_map from the scope, which will contain

static void BoolDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<bool*>(data);
}

static void Int32Deallocator(void* data, size_t, void* arg) {
  delete[] static_cast<int32_t*>(data);
}

static void DoubleDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<double*>(data);
}

static void FloatDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<float*>(data);
}

  if (!status->status.ok()) {
    return nullptr;
  }
  return tensor;
}

// Deleter for DLManagedTensor
void DLManagedTensorDeleter(DLManagedTensor* arg) {
  TfDlManagedTensorCtx* owner =
      static_cast<TfDlManagedTensorCtx*>(arg->manager_ctx);
  owner->reference.Unref();
  delete owner;
}

// Converts TF_DATAType to DLPack data type.
DLDataType GetDlDataType(TF_DataType data_type, TF_Status* status) {

  if (!status->status.ok()) return nullptr;

  if (input_index >= op_def->input_arg_size() || input_index < 0) {
    status->status = tensorflow::errors::InvalidArgument(
        input_index, " out of range for ", op_name);
    return nullptr;
  }

  const tensorflow::OpDef_ArgDef& input_arg = op_def->input_arg()[input_index];

  if (input_arg.number_attr().empty()) {
    status->status = tensorflow::errors::NotFound(

                                                  TF_Status* status) {
  auto* cc_ctx = reinterpret_cast<tensorflow::OpKernelContext*>(ctx);
  const tensorflow::Tensor& arg_tensor = cc_ctx->input(index);
  absl::Status cc_status;
  if (arg_tensor.dtype() != tensorflow::DT_RESOURCE) {
    cc_status = absl::InvalidArgumentError(
        absl::StrCat("Trying to obtain resource handle from Input[", index,

  CHECK(tensorflow::protobuf::TextFormat::ParseFromString(
      "    signature {"
      "      name: 'AddVariablesFunction'"
      "      input_arg {"
      "        name: 'var'"
      "        type: DT_RESOURCE"
      "      }"
      "      output_arg {"
      "        name: 'sum'"
      "        type: DT_FLOAT"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'read0'"

  GeneralAssignment("AssignVariableOp", context, value, status);
}

// Passed to `TF_NewTensor` to indicate how an array of floats should be
// deleted.
static void FloatDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<float*>(data);
}

// Creates a TFE_TensorHandle with value `v`.
TensorHandlePtr FloatTensorHandle(float v, TF_Status* status) {
  const int num_bytes = sizeof(float);

                   "      input_arg {"
                   "        name: 'a'"
                   "        type: DT_FLOAT"
                   "      }"
                   "      input_arg {"
                   "        name: 'b'"
                   "        type: DT_FLOAT"
                   "      }"
                   "      output_arg {"
                   "        name: 'm'"

  // Verify that FunctionDef ArgDef has attributes.
  ASSERT_EQ(func_->record->fdef().arg_attr_size(), 1);
  auto arg_attrs = func_->record->fdef().arg_attr().find(0);
  ASSERT_NE(arg_attrs, func_->record->fdef().arg_attr().end());
  auto iter = arg_attrs->second.attr().find("_test_attr");
  ASSERT_NE(iter, arg_attrs->second.attr().end());
  EXPECT_EQ(iter->second.s(), "value");
}

    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg, TF_Status* status) {
  tensorflow::Device* device = nullptr;
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  status->status = context->FindDeviceFromName(device_name, &device);
  if (!status->status.ok()) {
    deallocator(data, len, deallocator_arg);
    status->status =