def ExperimentalMlirQuantizeModel(input_contents_txt_raw: object, disable_per_channel: bool = ..., fully_quantize: bool = ..., inference_type: int = ..., input_data_type: int = ..., output_data_type: int = ..., enable_numeric_verify: bool = ..., enable_whole_model_verify:...

    denylisted_nodes,
    enable_variable_quantization,
    disable_per_channel_for_dense_layers,
    debug_options_str,
):
  """Wraps experimental mlir quantize model."""
  return _pywrap_converter_api.ExperimentalMlirQuantizeModel(
      input_data_str,
      disable_per_channel,
      fully_quantize,
      inference_type,
      input_data_type,
      output_data_type,
      enable_numeric_verify,
      enable_whole_model_verify,

      `debug_info_str` contains the `GraphDebugInfo` proto. When
      `enable_mlir_converter` is True, tuse MLIR-based conversion instead of
      TOCO conversion.
    )pbdoc");
  m.def(
      "ExperimentalMlirQuantizeModel",
      [](py::object input_contents_txt_raw, bool disable_per_channel,
         bool fully_quantize, int inference_type, int input_data_type,
         int output_data_type, bool enable_numeric_verify,