def METHOD_NAME(self, tensor: torch.Tensor):
    """
    Quantize-dequantize the tensor, using the saved encoding for this tensor
    :param tensor: Tensor passed to the module in the forward pass
    :return: Quantized output from the wrapped module
    """
    if self.enabled:
        if self.data_type == QuantizationDataType.float:
            quantized_tensor = tensor.half()
            quantized_tensor = quantized_tensor.float()
            return quantized_tensor
        if self._ch_axis is None:
            return torch.fake_quantize_per_tensor_affine(tensor, self.scale, self.zero_point,
                                                         self.q_min, self.q_max)
        return torch.fake_quantize_per_channel_affine(tensor, self.scale, self.zero_point,
                                                      self._ch_axis, self.q_min, self.q_max)
    return tensor