      arguments: :class:`argparse.Namespace`
          The arguments that were passed to the train or convert process as generated from
          Faceswap's command line arguments
      allow_growth: bool
          ``True`` if the Tensorflow allow_growth parameter should be set otherwise ``False``
      is_predict: bool, optional
          ``True`` if the model is being loaded for inference, ``False`` if the model is being loaded
          for training. Default: ``False``
 
</s> remove     def __init__(self, arguments, allow_growth, is_predict):
        logger.debug("Initializing %s: (arguments: %s, allow_growth: %s, is_predict: %s)",
                     self.__class__.__name__, arguments, allow_growth, is_predict)
 </s> add     def __init__(self, arguments, mixed_precision, allow_growth, is_predict):
        logger.debug("Initializing %s: (arguments: %s, mixed_precision: %s, allow_growth: %s, "
                     "is_predict: %s)", self.__class__.__name__, arguments, mixed_precision,
                     allow_growth, is_predict) </s> remove         if get_backend() != "nvidia" or not use_mixed_precision:
 </s> add         if not use_mixed_precision: </s> remove         argument_list.append(dict(
            opts=("-mp", "--mixed-precision"),
            action="store_true",
            dest="mixed_precision",
            default=False,
            backend="nvidia",
            group="training",
            help="R|NVIDIA GPUs can run operations in float16 faster than in float32. Mixed "
                 "precision allows you to use a mix of float16 with float32, to get the "
                 "performance benefits from float16 and the numeric stability benefits from "
                 "float32.\nWhile mixed precision will run on most Nvidia models, it will only "
                 "speed up training on more recent GPUs. Those with compute capability 7.0 or "
                 "higher will see the greatest performance benefit from mixed precision because "
                 "they have Tensor Cores. Older GPUs offer no math performance benefit for using "
                 "mixed precision, however memory and bandwidth savings can enable some speedups. "
                 "Generally RTX GPUs and later will offer the most benefit."))
 </s> add  </s> remove         use_mixed_precision = not is_predict and arguments.mixed_precision
 </s> add         use_mixed_precision = not is_predict and mixed_precision and get_backend() == "nvidia"
    mixed_precision: bool
        ``True`` if Mixed Precision training should be used otherwise ``False``
      arguments: :class:`argparse.Namespace`
          The arguments that were passed to the train or convert process as generated from
          Faceswap's command line arguments
    mixed_precision: bool
        ``True`` if Mixed Precision training should be used otherwise ``False``
      allow_growth: bool
          ``True`` if the Tensorflow allow_growth parameter should be set otherwise ``False``
      is_predict: bool, optional
          ``True`` if the model is being loaded for inference, ``False`` if the model is being loaded
          for training. Default: ``False``
keep keep add keep keep keep keep keep
 Training - Move Mixed Precision to model config 
plugins/train/model/_base.py