namespace tensorflow {
namespace parallel_device {

TEST(PARALLEL_DEVICE, TestRemoteBasic) {
  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(

  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);  // Watch x.
  AbstractTensorHandle* exp_output;
  TF_RETURN_IF_ERROR(ops::Exp(ctx, inputs[0], &exp_output, "Exp"));
  std::unique_ptr<GradientFunction> gradient_function(
      new PassThroughGradientFunction);
  tape.RecordOperation(inputs, {exp_output}, gradient_function.release());
  TF_RETURN_IF_ERROR(tape.ComputeGradient(ctx,

  tensorflow::ServerDef server_def = GetServerDef(2);

  // This server def has the task index set to 0.
  string serialized = server_def.SerializeAsString();

  server_def.set_task_index(1);

  std::unique_ptr<tensorflow::GrpcServer> worker_server;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server)
                  .ok());

    stats->is_directory = cc_stats.is_directory;
  }
}

void TF_NewWritableFile(const char* filename, TF_WritableFileHandle** handle,
                        TF_Status* status) {
  std::unique_ptr<::tensorflow::WritableFile> f;
  TF_SetStatus(status, TF_OK, "");
  ::tensorflow::Status s =
      ::tensorflow::Env::Default()->NewWritableFile(filename, &f);
  ::tensorflow::Set_TF_Status_from_Status(status, s);

      if (!status.ok()) out_tensor->reset();
    }
  }
  if (!status.ok()) {
    tsl::Set_TF_Status_from_Status(out_status, status);
  }
}

std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,
          std::unique_ptr<TensorSliceReader::VarToDataTypeMap>>
CheckpointReader::BuildV2VarMaps() {
  CHECK(v2_reader_ != nullptr);
  CHECK(v2_reader_->status().ok());

int CopyFileContents(int dst_fd, int src_fd, off_t size) {
  // Use a copy buffer of 128KB but don't store it on the stack.
  constexpr static size_t kPosixCopyFileBufferSize = 128 * 1024;
  std::unique_ptr<char[]> buffer(new char[kPosixCopyFileBufferSize]);

  off_t offset = 0;
  int bytes_transferred = 0;
  int rc = 1;
  // When `sendfile` returns 0 we stop copying and let callers handle this.

#include "tensorflow/core/platform/cloud/now_seconds_env.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

TEST(ExpiringLRUCacheTest, MaxAge) {
  const string key = "a";
  std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);
  tf_gcs_filesystem::ExpiringLRUCache<int> cache(
      1, 0, [&env]() { return env->NowSeconds(); });
  env->SetNowSeconds(1);

};

Status RegisterGradients(GradientRegistry* registry) {
  TF_RETURN_IF_ERROR(RegisterNotDifferentiable(registry, "CheckNumerics"));
  return absl::OkStatus();
}

TEST_P(CppGradients, TestSetAttrString) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  AbstractContextPtr ctx;
  {
    AbstractContext* ctx_raw = nullptr;
    Status s =

void LoggedTensorDeallocator(void* data) {
  delete reinterpret_cast<LoggedTensor*>(data);
}

TFE_TensorHandle* MakeLoggedTensorHandle(
    TFE_Context* context, const tensorflow::string& logging_device_name,
    std::unique_ptr<LoggedTensor> t, TF_Status* status) {
  auto dtype = TFE_TensorHandleDataType(t->tensor);
  TFE_CustomDeviceTensorHandleMethods handle_methods;
  handle_methods.num_dims = &LoggedTensorNumDims;

#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {

AbstractContext* BuildFunction(const char* fn_name) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_ExecutionContext* graph_ctx = TF_CreateFunction(fn_name, status.get());
  return unwrap(graph_ctx);
}