              var_x2 = Conv2D(self._filters, 3,
                              padding=self._padding,
                              name=f"{self._name}_conv2d",
                              **self._kwargs)(var_x)
              var_x2 = UpSampling2D(size=(self._scale_factor, self._scale_factor),
                                    interpolation=self._interpolation,
 
</s> remove     def __init__(self, filters, kernel_size=3, padding="same", activation="leakyrelu",
                 scale_factor=2, interpolation="bilinear"):
 </s> add     def __init__(self,
                 filters: int,
                 kernel_size: Union[int, Tuple[int]] = 3,
                 padding: str = "same",
                 activation: str = "leakyrelu",
                 scale_factor: int = 2,
                 interpolation: str = "bilinear") -> None: </s> remove     def __init__(self, filters, kernel_size=3, padding="same", activation="leakyrelu",
                 interpolation="bilinear", sr_ratio=0.5, scale_factor=2, fast=False, **kwargs):
 </s> add     def __init__(self,
                 filters: int,
                 kernel_size: Union[int, Tuple[int]] = 3,
                 padding: str = "same",
                 activation: Optional[str] = "leakyrelu",
                 interpolation: str = "bilinear",
                 sr_ratio: float = 0.5,
                 scale_factor: int = 2,
                 fast: bool = False, **kwargs) -> None: </s> remove     def __init__(self, filters, kernel_size=3, padding="same", **kwargs):
 </s> add     def __init__(self,
                 filters: int,
                 kernel_size: Union[int, Tuple[int]] = 3,
                 padding: str = "same",
                 **kwargs) -> None: </s> remove                  filters,
                 kernel_size=3,
                 padding="same",
                 scale_factor=2,
                 normalization=None,
                 activation="leakyrelu",
                 **kwargs):
 </s> add                  filters: int,
                 kernel_size: Union[int, Tuple[int]] = 3,
                 padding: str = "same",
                 scale_factor: int = 2,
                 normalization: Optional[str] = None,
                 activation: Optional[str] = "leakyrelu",
                 **kwargs) -> None:
                            is_upscale=True,
              var_x2 = Conv2D(self._filters, 3,
                              padding=self._padding,
                            is_upscale=True,
                              name=f"{self._name}_conv2d",
                              **self._kwargs)(var_x)
              var_x2 = UpSampling2D(size=(self._scale_factor, self._scale_factor),
                                    interpolation=self._interpolation,
keep add keep keep keep keep
 lib.model.nnblocks - typing + cleanup upscales 
lib/model/nn_blocks.py