          self._no_flip = model.command_line_arguments.no_flip
          self._no_warp = model.command_line_arguments.no_warp
          self._warp_to_landmarks = (not self._no_warp
                                     and model.command_line_arguments.warp_to_landmarks)
          self._model_input_size = model.input_shape[1]
  
          if self._warp_to_landmarks:
              self._face_cache.pre_fill(images, side)
          self._processing = ImageAugmentation(batch_size,
                                               self._process_size,
 
</s> remove         self._process_size = max([model.input_shape[1]] + [img[1]
                                 for img in model.output_shapes[0]])
 </s> add         self._process_size = max(img[1]
                                 for img in model.model.input_shape + model.model.output_shape) </s> remove             print(f"{self._format_column(key, name_col)}{self._format_column(num, items_col)}"
                  f"{self._format_column(_min, time_col)}{self._format_column(avg, time_col)}"
                  f"{self._format_column(_max, time_col)}")
 </s> add             contents = f"{self._format_column(key, name_col)}{self._format_column(num, items_col)}"
            if self._display["min"]:
                _min = f"{np.min(val):.{decimal_places}f}"
                contents += f"{self._format_column(_min, time_col)}"
            if self._display["mean"]:
                avg = f"{np.mean(val):.{decimal_places}f}"
                contents += f"{self._format_column(avg, time_col)}"
            if self._display["max"]:
                _max = f"{np.max(val):.{decimal_places}f}"
                contents += f"{self._format_column(_max, time_col)}"
            print(contents) </s> remove     def __init__(self):
 </s> add     def __init__(self,
                 show_min: bool = True, show_mean: bool = True, show_max: bool = True) -> None: </s> remove             _min = f"{np.min(val):.{decimal_places}f}"
            avg = f"{np.mean(val):.{decimal_places}f}"
            _max = f"{np.max(val):.{decimal_places}f}"
 </s> add 
        self._model_input_size = max(img[1] for img in model.model.input_shape)
          self._no_flip = model.command_line_arguments.no_flip
          self._no_warp = model.command_line_arguments.no_warp
          self._warp_to_landmarks = (not self._no_warp
                                     and model.command_line_arguments.warp_to_landmarks)
        self._model_input_size = max(img[1] for img in model.model.input_shape)
  
          if self._warp_to_landmarks:
              self._face_cache.pre_fill(images, side)
          self._processing = ImageAugmentation(batch_size,
                                               self._process_size,
keep keep keep keep replace keep keep keep keep keep
 bugfixes:
  - lib.training: Correct  input + output size for pre-existing models
  - lib.align.detected_faces - fix trace logging
  - lib.util.debug_times 
lib/training/generator.py