for (const Node* n : fn_body->graph.nodes()) {
    stack_traces[n->name()] = n->GetStackTrace();
  }

  TF_Function* tf_function = new TF_Function();
  tf_function->record = new tensorflow::FunctionRecord(
      std::move(fdef), std::move(stack_traces), false);

  return tf_function;
}

TF_Function* TF_GraphToFunction(const TF_Graph* fn_body, const char* fn_name,

  return ret;
}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {
  const auto& debug_str = DebugString(func->record->fdef());
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

// On success, returns a set of TF_Function instances from `text_proto` of

    status->status = tensorflow::errors::NotFound(
        "Unable to find Function with name: ", name);
    return nullptr;
  }

  TF_Function* result = new TF_Function();
  record->Ref();
  result->record = record.get();
  return result;
}

void TFE_ContextRemoveFunction(TFE_Context* ctx, const char* name,
                               TF_Status* status) {

TF_CAPI_EXPORT extern void TFE_ContextGetGraphDebugInfo(
    TFE_Context* ctx, const char* function_name, TF_Buffer* buf,
    TF_Status* status);

// Extracts a TF_Function from the context.
// Must call TF_DeleteFunction on the returned value.
TF_CAPI_EXPORT extern TF_Function* TFE_ContextGetFunction(TFE_Context* ctx,
                                                          const char* name,

        and `tf.keras.layers.LocallyConnected1D` layers using `tf.SparseTensor`
        to store weights, allowing a dramatic speedup for large sparse models.
    *   Enable the Keras compile API `experimental_run_tf_function` flag by
        default. This flag enables single training/eval/predict execution path.
        With this 1. All input types are converted to `Dataset`. 2. When