R"pbdoc(
      Registers the given custom opdefs to the TensorFlow global op registry.
    )pbdoc");
  m.def(
      "RetrieveCollectedErrors",
      []() {
        std::vector<std::string> collected_errors =
            tflite::RetrieveCollectedErrors();
        pybind11::list serialized_message_list(collected_errors.size());
        int i = 0;
        for (const auto& error_data : collected_errors) {

  return _pywrap_converter_api.RegisterCustomOpdefs(custom_opdefs_list)


def wrapped_retrieve_collected_errors():
  """Wraps RetrieveCollectedErrors with lazy loader."""
  return _pywrap_converter_api.RetrieveCollectedErrors()


def wrapped_flat_buffer_file_to_mlir(model, input_is_filepath):
  """Wraps FlatBufferFileToMlir with lazy loader."""

def ExperimentalMlirSparsifyModel(input_contents_txt_raw: object) -> object: ...
def FlatBufferToMlir(arg0: str, arg1: bool) -> str: ...
def RegisterCustomOpdefs(custom_opdefs_txt_raw: object) -> object: ...

// Registers the given custom opdefs to TensorFlow global op registry.
PyObject* RegisterCustomOpdefs(PyObject* list);

// Returns the collected TFLite conversion errors.
std::vector<std::string> RetrieveCollectedErrors();

// Returns MLIR string dump of the given Flatbuffer model.
std::string FlatBufferFileToMlir(const std::string& model,
                                 bool input_is_filepath);

                   "defs argument",
                   i);
      return nullptr;
    }
    TF_DeleteStatus(status);
  }

  Py_RETURN_TRUE;
}

std::vector<std::string> RetrieveCollectedErrors() {
  mlir::TFL::ErrorCollector* collector =
      mlir::TFL::ErrorCollector::GetErrorCollector();
  std::vector<std::string> collected_errors;
  for (const auto& error_data : collector->CollectedErrors()) {