size_t proto_len, TF_Status* status) {
  // shape.ParseFromArray takes an int as length, this function takes size_t,
  // make sure there is no information loss.
  if (proto_len > std::numeric_limits<int>::max()) {
    status->status = InvalidArgument(
        "proto_len (", proto_len,
        " bytes) is too large to be parsed by the protocol buffer library");
    return;

}

const char* TF_GraphDebugString(TF_Graph* graph, size_t* len) {
  tensorflow::mutex_lock c(graph->mu);
  const auto& debug_str = graph->graph.ToGraphDefDebug().DebugString();
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {

    lib = g->graph.flib_def().ToProto();
  }
  const auto len = std::min(max_func, static_cast<int>(lib.function_size()));
  for (int i = 0; i < len; ++i) {
    TF_Function* func = new TF_Function();
    func->record = new tensorflow::FunctionRecord(lib.function(i), {}, false);
    funcs[i] = func;
  }
  status->status = absl::OkStatus();
  return len;
}

  // Copy Tensor data into an array.
  float result_data[4] = {0};
  memcpy(&result_data[0], TF_TensorData(result_tensor),
         TF_TensorByteSize(result_tensor));

  int data_len = 4;  // length of result_data
  for (int i = 0; i < data_len; i++) {
    EXPECT_EQ(result_data[i], 0);
  }

  TF_DeleteTensor(result_tensor);
  TF_DeleteAbstractTensor(result);
  TF_DeleteOutputList(o);
  TF_DeleteExecutionContext(ctx);

  const std::vector<std::string>& nodes =
      reinterpret_cast<const tensorflow::grappler::GrapplerItem*>(item)->fetch;

  const int len = std::min(num_values, static_cast<int>(nodes.size()));
  char* p = static_cast<char*>(storage);
  for (int index = 0; index < len; ++index) {
    const std::string& s = nodes[index];
    values[index] = p;
    lengths[index] = s.size();

  TF_TString tstr[1];
  TF_TString_Init(&tstr[0]);
  TF_TString_Copy(&tstr[0], test_string, sizeof(test_string) - 1);

  auto deallocator = [](void* data, size_t len, void* arg) {};
  unique_tensor_ptr t_in(TF_NewTensor(TF_STRING, nullptr, 0, &tstr[0],
                                      sizeof(tstr), deallocator, nullptr),
                         TF_DeleteTensor);

  }
}

// Wraps the deleter function of DLManagedTensor to match the function signature
// TFE_NewTensorHandleFromDeviceMemory.
void DeallocatorWrapperFunc(void* data, size_t len, void* dlmt_vptr) {
  TFE_CallDLManagedTensorDeleter(dlmt_vptr);
}

// Checks whether the stride array matches the layout of compact, row-majored
// data.

TFE_ContextOptions* TFE_NewContextOptions() { return new TFE_ContextOptions; }

void TFE_ContextOptionsSetConfig(TFE_ContextOptions* options, const void* proto,
                                 size_t proto_len, TF_Status* status) {
  TF_SetConfig(&options->session_options, proto, proto_len, status);
}

void TFE_ContextOptionsSetAsync(TFE_ContextOptions* options,
                                unsigned char enable) {
  options->async = enable;
}

}

void TFE_InitializeLocalOnlyContext(TFE_Context* ctx, int keep_alive_secs,
                                    const void* proto, size_t proto_len,
                                    TF_Status* status) {
  tensorflow::ServerDef server_def;
  if (!server_def.ParseFromArray(proto, proto_len)) {
    status->status = tensorflow::errors::InvalidArgument(
        "Invalid tensorflow.ServerDef protocol buffer");
    return;
  }