          if self._config["learn_mask"]:
              var_y = inputs
              var_y = self._reshape_for_output(var_y)
  
          # De-convolve
          upscales = int(np.log2(self._config["output_size"] / K.int_shape(var_x)[1]))
          filters = _get_curve(self._config["dec_max_filters"],
                               self._config["dec_min_filters"],
                               upscales,
                               self._config["dec_filter_slope"],
 
</s> remove             var_x = Conv2DBlock(filters, activation="leakyrelu", name=f"fs_enc_convblk_{i}")(var_x)
 </s> add             name = f"fs_{'dny_' if self._is_alt else ''}enc"
            var_x = Conv2DBlock(filters,
                                kernel_size=self._kernel_size,
                                strides=self._strides,
                                relu_alpha=self._relu_alpha,
                                name=f"{name}_convblk_{i}")(var_x) </s> remove             var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add             if not self._is_dny:
                var_x = LeakyReLU(alpha=0.1)(var_x)
        if self._is_dny:
            var_x = self._dny_entry(var_x)
        if self._is_dny and self._config["learn_mask"]:
            var_y = self._dny_entry(var_y)

          if self._config["learn_mask"]:
              var_y = inputs
              var_y = self._reshape_for_output(var_y)
  
        if self._is_dny:
            var_x = self._dny_entry(var_x)
        if self._is_dny and self._config["learn_mask"]:
            var_y = self._dny_entry(var_y)

          # De-convolve
          upscales = int(np.log2(self._config["output_size"] / K.int_shape(var_x)[1]))
          filters = _get_curve(self._config["dec_max_filters"],
                               self._config["dec_min_filters"],
                               upscales,
                               self._config["dec_filter_slope"],
keep keep keep add keep keep keep keep keep keep
 Phaze-A - Updates for future implementations 
plugins/train/model/phaze_a.py