code: def tfms_from_model(f_model, sz, aug_tfms=None, max_zoom=None, pad=0, crop_type=CropType.RANDOM,
                      tfm_y=None, sz_y=None, pad_mode=cv2.BORDER_REFLECT, norm_y=True, scale=None):
      
      stats = inception_stats if f_model in inception_models else imagenet_stats
      return tfms_from_stats(stats, sz, aug_tfms, max_zoom=max_zoom, pad=pad, crop_type=crop_type,
                             tfm_y=tfm_y, sz_y=sz_y, pad_mode=pad_mode, norm_y=norm_y, scale=scale)
docs: Returns separate transformers of images for training and validation.
      Transformers are constructed according to the image statistics given by the model. (See tfms_from_stats)
  
      Arguments:
          f_model: model, pretrained or not pretrained