if (gradient_tensors.size() == 1) {
    return gradient_tensors[0];
  }

  AbstractOperationPtr op(ctx_->CreateOperation());
  absl::Status s = op->Reset("AddN", /*raw_device_name=*/nullptr);
  if (!s.ok()) {
    return nullptr;
  }
  s = op->AddInputList(gradient_tensors);
  if (!s.ok()) {
    return nullptr;
  }

  int num_outputs = 1;

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Test TF_Operation*() query functions.
  EXPECT_EQ(std::string("add"), std::string(TF_OperationName(add)));
  EXPECT_EQ(std::string("AddN"), std::string(TF_OperationOpType(add)));
  EXPECT_EQ(std::string(""), std::string(TF_OperationDevice(add)));
  EXPECT_EQ(1, TF_OperationNumOutputs(add));
  EXPECT_EQ(TF_INT32, TF_OperationOutputType(TF_Output{add, 0}));

  TF_Operation* add = Add(feed1, feed2, func_graph_, s_);
  DefineT(-1, {}, {{feed1, 0}, {feed2, 0}}, {{add, 3}}, {}, true);
  EXPECT_EQ(TF_OUT_OF_RANGE, TF_GetCode(s_));
  EXPECT_EQ(std::string("Node 'add' (type: 'AddN', num of outputs: 1) does "
                        "not have output 3\n\tEncountered while processing "
                        "output 0 from function 'MyFunc'"),
            std::string(TF_Message(s_)));
}