                                   name=f"{self._name}_resize")(var_x)
          var_x_sr = Conv2D(self._filters, self._kernel_size,
                            strides=1,
                            padding=self._padding,
                            name=f"{self._name}_conv")(var_x_sr)
          var_x_us = Conv2DTranspose(self._filters, 3,
                                     strides=2,
                                     padding=self._padding,
                                     name=f"{self._name}_convtrans")(var_x)
 
</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,
                                   name=f"{self._name}_resize")(var_x)
          var_x_sr = Conv2D(self._filters, self._kernel_size,
                            strides=1,
                            padding=self._padding,
                          is_upscale=True,
                            name=f"{self._name}_conv")(var_x_sr)
          var_x_us = Conv2DTranspose(self._filters, 3,
                                     strides=2,
                                     padding=self._padding,
                                     name=f"{self._name}_convtrans")(var_x)
keep keep keep add keep keep keep keep keep
 lib.model.nnblocks - typing + cleanup upscales 
lib/model/nn_blocks.py