void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());
    TF_SetTracingImplementation(std::get<0>(GetParam()), status.get());
    absl::Status s = StatusFromTF_Status(status.get());
    CHECK_EQ(errors::OK, s.code()) << s.message();
  }
};

TEST_P(UnifiedCAPI, TestBasicEager) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);

  int64_t actual = TFE_MonitoringReadCounter1(reader, label_name);

  CHECK_EQ(actual, 1);
}

TFE_MonitoringCounter0* CreateCounter0(const char* counter_name) {
  TF_Status* status = TF_NewStatus();
  auto* counter =
      TFE_MonitoringNewCounter0(counter_name, status, "description");
  TF_DeleteStatus(status);
  return counter;
}

void IncrementCounter0(TFE_MonitoringCounter0* counter, int64_t delta) {

#include "tensorflow/core/lib/gtl/cleanup.h"
#include "tensorflow/core/platform/test.h"

TEST(CUSTOM_DEVICE, RegisterSimpleDevice) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* context = TFE_NewContext(opts, status.get());
  TFE_DeleteContextOptions(opts);

using tracing::TracingOperation;

class CppGradients
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());
    TF_SetTracingImplementation(std::get<0>(GetParam()), status.get());
    absl::Status s = StatusFromTF_Status(status.get());
    CHECK_EQ(errors::OK, s.code()) << s.message();
  }
};

  TF_Buffer* op_list_buf = TF_GetAllOpList();
  TF_Status* status = TF_NewStatus();
  auto* api_def_map = TF_NewApiDefMap(op_list_buf, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);

  string op_name = "TestCApi";
  status = TF_NewStatus();
  auto* api_def_buf =
      TF_ApiDefMapGet(api_def_map, op_name.c_str(), op_name.size(), status);

  std::unique_ptr<TFE_ContextOptions, decltype(&TFE_DeleteContextOptions)> opts(
      TFE_NewContextOptions(), TFE_DeleteContextOptions);
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  std::unique_ptr<TFE_Context, decltype(&TFE_DeleteContext)> context(
      TFE_NewContext(opts.get(), status.get()), TFE_DeleteContext);
  tensorflow::ServerDef server_def = GetServerDef("worker", 3);

  return node_def.op() == "Neg" && node_def.name() == "neg" &&
         node_def.input_size() == 1 && node_def.input(0) == input;
}

bool GetGraphDef(TF_Graph* graph, tensorflow::GraphDef* graph_def) {
  TF_Status* s = TF_NewStatus();
  TF_Buffer* buffer = TF_NewBuffer();
  TF_GraphToGraphDef(graph, buffer, s);
  bool ret = TF_GetCode(s) == TF_OK;
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server2)
                  .ok());
  ASSERT_TRUE(worker_server2->Start().ok());

  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_ContextOptionsSetAsync(opts, static_cast<unsigned char>(/*enable=*/true));
  TFE_ContextOptionsSetDevicePlacementPolicy(opts, TFE_DEVICE_PLACEMENT_SILENT);

  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server)
                  .ok());
  ASSERT_TRUE(worker_server->Start().ok());

  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_ContextOptionsSetAsync(opts, static_cast<unsigned char>(async));
  TFE_ContextOptionsSetDevicePlacementPolicy(opts,

template <class T, TF_DataType datatype>
TFE_TensorHandle* TestTensorHandleWithDims(TFE_Context* ctx, const T* data,
                                           const int64_t* dims, int num_dims) {
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, datatype, dims, num_dims, status);
  memcpy(TF_TensorData(t), data, TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);