tensor_spec.TensorSpec(
                shape=input_shape, dtype=dtypes.float32, name='input_tensor'
            )
        ),
    )
    return model

  def _any_log_contains(
      self, substring: str, log_record_list: List['logging.LogRecord']
  ) -> bool:
    """Returns True if any of the log contains a given substring.

    Args:

      quantization.quantize_saved_model(
          self._input_saved_model_path,
          self._output_saved_model_path,
          config,
      )
      self.assertTrue(
          self._any_log_contains(
              'Calibration step is executed in graph mode.',
              info_logs.records,
          )
      )
      module_str = self._extract_first_xla_call_module_op(

    """
    total = 0
    for root, _, files in os.walk(path):
      for filename in files:
        total += os.path.getsize(os.path.join(root, filename))
    return total

  def _any_log_contains(
      self, substring: str, log_record_list: List['logging.LogRecord']
  ) -> bool:
    """Returns True if any of the log contains a given substring.

    Args:

        )
      finally:
        # Restore the logger verbosity.
        logging.set_verbosity(prev_log_level)

      self.assertNotEmpty(info_logs.records)
      self.assertTrue(
          self._any_log_contains(
              'Calibration step is executed in graph mode.',
              info_logs.records,
          )
      )


class TensorNamePreservationTest(quantize_model_test_base.QuantizedModelTest):