if (status->status.ok()) {
    char* buf = new char[new_handle.size() + 1];
    memcpy(buf, new_handle.c_str(), new_handle.size() + 1);
    *handle = buf;
  }
}

void TF_PRun(TF_DeprecatedSession* s, const char* handle,
             // Input tensors
             const char** c_input_names, TF_Tensor** c_inputs, int ninputs,
             // Output tensors

  for (const auto& factory : GetFactories())
    factories_sorted.insert(factory.first);
  const char* comma = "";
  for (const string& factory : factories_sorted) {
    msg += comma + factory;
    comma = ", ";
  }
  msg += ")";

  return errors::InvalidArgument(msg.c_str());
}

static TracingContext* CreateTracingExecutionContext(const char* fn_name,
                                                     TF_Status* s) {

}

Status SetAttrString(AbstractOperation* op_, const char* attr_name,
                     const char* data, size_t length,
                     ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, StringPiece(data, length));
  return op_->SetAttrString(attr_name, data, length);
}
Status SetAttrInt(AbstractOperation* op_, const char* attr_name, int64_t value,
                  ForwardOperation* forward_op_) {

  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();
    if ((p + s.size()) > (static_cast<char*>(storage) + storage_size)) {
      tsl::Set_TF_Status_from_Status(
          status,

  if (value_string.length() > 100) {
    // The default NumPy-style output can be distractingly long in error
    // messages.
    value_string = absl::StrCat(value_string.substr(0, 100), " [...]");
  }
  Status s;
  const char* device_name = DeviceName(&s);
  if (!s.ok()) {
    device_name = "<error fetching device name>";
  }
  return absl::StrCat("TensorHandle(", value_string, ", shape=", shape_string,

  // for computing gradient so we could safely drop it.
  outputs[0] = temp_outputs[1];
  temp_outputs[0]->Unref();
  return absl::OkStatus();
}

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());
    status_ = StatusFromTF_Status(status.get());

      break;
  }
  return dtype;
}

// Gets DLPack's DLDevice from eager tensor handle.
DLDevice GetDlContext(TFE_TensorHandle* h, TF_Status* status) {
  DLDevice ctx;
  const char* device_name =
      tensorflow::unwrap(h)->BackingDeviceName(&status->status);
  DeviceNameUtils::ParsedName parsed_name;
  tensorflow::DeviceNameUtils::ParseFullName(device_name, &parsed_name);

}

// --------------------------  Resource  ---------------------------------------
void TF_CreatePluginResource(TF_OpKernelContext* ctx,
                             const char* container_name,
                             const char* plugin_resource_name,
                             void* plugin_resource, void (*delete_func)(void*),
                             TF_Status* status) {

TF_Function* TF_GraphToFunctionWithControlOutputs(
    const TF_Graph* fn_body, const char* fn_name,
    unsigned char append_hash_to_fn_name, int num_opers,
    const TF_Operation* const* opers, int ninputs, const TF_Output* inputs,
    int noutputs, const TF_Output* outputs, const char* const* output_names,
    int ncontrol_outputs, const TF_Operation* const* control_outputs,
    const char* const* control_output_names, const TF_FunctionOptions* opts,

  // the operation, its `StartCancel` method will be called if op execution
  // fails on any device in order to cancel the others.
  void StartExecute(TFE_Context* context, const char* operation_name,
                    std::vector<TFE_TensorHandle*> inputs,
                    const TFE_OpAttrs* attributes, int expected_max_outputs,
                    CancellationManager& cancellation_manager,