::tensorflow::io::JoinPath(tempdir, "somedir");
    TF_CreateDir(dirpath.c_str(), s);
    ASSERT_TF_OK(s) << "TF_CreateDir failed for " << dirpath << ": "
                    << TF_Message(s);

    ::tensorflow::string filepath =
        ::tensorflow::io::JoinPath(dirpath, "somefile.txt");
    TF_WritableFileHandle* handle;
    TF_NewWritableFile(filepath.c_str(), &handle, s);

  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) {
  const auto& debug_str = DebugString(func->record->fdef());
  *len = debug_str.size();

  ops_->copy_file(filesystem_.get(), translated_src.c_str(),
                  translated_target.c_str(), plugin_status.get());
  return StatusFromTF_Status(plugin_status.get());
}

std::string ModularFileSystem::TranslateName(const std::string& name) const {
  if (ops_->translate_name == nullptr) return FileSystem::TranslateName(name);

  char* p = ops_->translate_name(filesystem_.get(), name.c_str());

  auto handle = dyn_cast<ImmediateExecutionTensorHandle>(unwrap(at));
  if (!handle) {
    string msg =
        StrCat("Not an eager tensor handle.", reinterpret_cast<uintptr_t>(at));
    TF_SetStatus(s, TF_INVALID_ARGUMENT, msg.c_str());
    return nullptr;
  }
  return wrap(handle);
}

TFE_Context* TF_ExecutionContextGetTFEContext(TF_ExecutionContext* ctx,
                                              TF_Status* s) {

    std::string name = "Conj_Exp_Grad";
    TF_RETURN_IF_ERROR(SafeConj(ctx, exp_.get(), &conj_output, name.c_str()));
    AbstractTensorHandlePtr conj_output_releaser(conj_output);

    name = "Mul_Exp_Grad";
    TF_RETURN_IF_ERROR(
        Mul(ctx, conj_output, grad_outputs[0], &grad_inputs[0], name.c_str()));
    return absl::OkStatus();
  }
  ~ExpGradientFunction() override {}

 private:

    if (!metadata) {
      TF_SetStatusFromGCSStatus(metadata.status(), status);
      return;
    }
    TF_VLog(3, "AppendObject: gs://%s/%s to gs://%s/%s", bucket.c_str(),
            temporary_object.c_str(), bucket.c_str(), object.c_str());
    const std::vector<gcs::ComposeSourceObject> source_objects = {
        {object, {}, {}}, {temporary_object, {}, {}}};

    grad_inputs[0]->Ref();

    // Grad for bias
    std::string name = "bias_add_grad";
    TF_RETURN_IF_ERROR(BiasAddGrad(ctx, upstream_grad, &grad_inputs[1],
                                   data_format.c_str(), name.c_str()));

    return absl::OkStatus();
  }
  ~BiasAddGradientFunction() override {}

 private:
  AttrBuilder forward_attrs_;
};

}  // namespace

  for (const auto& op : op_names) {
    op_names_oss << op << ", ";
  }
  const std::string& op_names_str = op_names_oss.str();
  void* data = tensorflow::port::Malloc(op_names_str.length());
  op_names_str.copy(static_cast<char*>(data), op_names_str.length(), 0);
  buf->data = data;
  buf->length = op_names_str.length();
  buf->data_deallocator = [](void* data, size_t length) {
    tensorflow::port::Free(data);
  };

  if len(version_segments) == 2:
    version_segments.append('0')
  for seg in version_segments:
    if not seg.isdigit():
      return None

  version_str = ''.join(['%03d' % int(seg) for seg in version_segments])
  return int(version_str)


def retrieve_bazel_version():
  """Retrieve installed bazel version (or bazelisk).

  Returns:
    The bazel version detected.
  """

      reinterpret_cast<const tensorflow::grappler::GrapplerItem*>(item)
          ->NodesToPreserve();
  *num_values = nodes.size();
  *storage_size = 0;
  for (const std::string& str : nodes) {
    *storage_size += str.size();
  }
}

void TF_GetNodesToPreserveList(const TF_GrapplerItem* item, char** values,
                               size_t* lengths, int num_values, void* storage,