converted_model = quantize_model.quantize(
        self._input_saved_model_path,
        representative_dataset=self._simple_model_data_gen(),
    )

    self.assertIsNotNone(converted_model)
    self.assertCountEqual(
        converted_model.signatures._signatures.keys(), {'serving_default'}
    )

    # Indirectly prove that it is performing a static-range quantization

    root = load.load(self._output_saved_model_path)
    self.assertCountEqual(root.signatures.keys(), {'serving_default'})
    new_outputs_1 = root.signatures['serving_default'](
        x=ops.convert_to_tensor(input_data)
    )

    root = load.load(output_saved_model_path_2)
    self.assertCountEqual(root.signatures.keys(), {'serving_default'})
    new_outputs_2 = root.signatures['serving_default'](

    dataset_file_map = saver.save(repr_ds_map)
    self.assertCountEqual(dataset_file_map.keys(), ['serving_default'])

    dataset_map = repr_dataset.TfRecordRepresentativeDatasetLoader(
        dataset_file_map
    ).load()
    self.assertCountEqual(dataset_map.keys(), ['serving_default'])
    samples = dataset_map['serving_default']
    for sample in samples:
      self.assertCountEqual(sample.keys(), {'x'})