def METHOD_NAME(
    self,
    model: torch.nn.Module,
    calib_data_loader: torch.utils.data.DataLoader,
    method: str = "percentile",
    num_calib_batches: int = 2,
    percentile: float = 99.99,
):
    """
    Calibrates torch model with quantized modules.
    :param model:               torch.nn.Module, model to perfrom the calibration on.
    :param calib_data_loader:   torch.utils.data.DataLoader, data loader of the calibration dataset.
                                Assumes that the first element of the tuple is the input image.
    :param method:              str, One of [percentile, mse, entropy, max].
                                Statistics method for amax computation of the quantized modules
                                (Default=percentile).
    :param num_calib_batches:   int, number of batches to collect the statistics from.
    :param percentile:          float, percentile value to use when SgModel,quant_modules_calib_method='percentile'.
                                Discarded when other methods are used (Default=99.99).
    """
    logging_level = logging.getLogger("absl").getEffectiveLevel()
    if not self.verbose:  # suppress pytorch-quantization spam
        logging.getLogger("absl").setLevel("ERROR")
    acceptable_methods = ["percentile", "mse", "entropy", "max"]
    if method in acceptable_methods:
        with torch.no_grad():
            device = next(model.parameters()).device
            self._collect_stats(model, calib_data_loader, num_batches=num_calib_batches)
            # FOR PERCENTILE WE MUST PASS PERCENTILE VALUE THROUGH KWARGS,
            # SO IT WOULD BE PASSED TO module.load_calib_amax(**kwargs), AND IN OTHER METHODS WE MUST NOT PASS IT.
            if method == "precentile":
                self._compute_amax(model, method="percentile", percentile=percentile)
            else:
                self._compute_amax(model, method=method)
            model.to(device)
    else:
        raise ValueError(f"Unsupported quantization calibration method, " f"expected one of: {'.'.join(acceptable_methods)}, however, received: {method}")
    logging.getLogger("absl").setLevel(logging_level)