* `tf.lite`
    * Add experimental supports conversion of models that may be larger than 2GB before buffer deduplication

### Bug Fixes and Other Changes

* `tf.py_function` and `tf.numpy_function` can now be used as function decorators for clearer code:
   ```
   @tf.py_function(Tout=tf.float32)
   def my_fun(x):
     print("This always executes eagerly.")
     return x+1
   ```

* `tf.lite`

  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,

  TF_Operation* func_op = Use({func_feed});
  Run({{func_feed, Int32Tensor(3)}}, func_op, -3);
}

TEST_F(CApiFunctionTest, SameGradForTwoFunctions) {
  // Define the functions
  TF_Function* func1;
  TF_Function* func2;
  TF_Function* grad_func;
  DefineFunction("FooFunc1", &func1);
  DefineFunction("FooFunc2", &func2);
  DefineFunction("MyGrad", &grad_func);

  // Make grad_func be a gradient of func1 and func2

  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

  if (ret) ret = node_def->ParseFromArray(buffer->data, buffer->length);
  TF_DeleteBuffer(buffer);
  TF_DeleteStatus(s);
  return ret;
}

bool GetFunctionDef(TF_Function* func, tensorflow::FunctionDef* func_def) {
  TF_Status* s = TF_NewStatus();
  TF_Buffer* buffer = TF_NewBuffer();
  TF_FunctionToFunctionDef(func, buffer, s);
  bool ret = TF_GetCode(s) == TF_OK;

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