code: def image_gen(normalizer, denorm, sz, tfms=None, max_zoom=None, pad=0, crop_type=None,
                tfm_y=None, sz_y=None, pad_mode=cv2.BORDER_REFLECT, scale=None):
      
      if tfm_y is None: tfm_y=TfmType.NO
      if tfms is None: tfms=[]
      elif not isinstance(tfms, collections.Iterable): tfms=[tfms]
      if sz_y is None: sz_y = sz
      if scale is None:
          scale = [RandomScale(sz, max_zoom, tfm_y=tfm_y, sz_y=sz_y) if max_zoom is not None
                   else Scale(sz, tfm_y, sz_y=sz_y)]
      elif not is_listy(scale): scale = [scale]
      if pad: scale.append(AddPadding(pad, mode=pad_mode))
      if crop_type!=CropType.GOOGLENET: tfms=scale+tfms
      return Transforms(sz, tfms, normalizer, denorm, crop_type,
                        tfm_y=tfm_y, sz_y=sz_y)
docs: Generate a standard set of transformations
  
      Arguments
      ---------
       normalizer :
           image normalizing function
       denorm :
           image denormalizing function
       sz :
           size, sz_y = sz if not specified.
       tfms :
           iterable collection of transformation functions
       max_zoom : float,
           maximum zoom
       pad : int,
           padding on top, left, right and bottom
       crop_type :
           crop type
       tfm_y :
           y axis specific transformations
       sz_y :
           y size, height
       pad_mode :
           cv2 padding style: repeat, reflect, etc.
  
      Returns
      -------
       type : ``Transforms``
           transformer for specified image operations.
  
      See Also
      --------
       Transforms: the transformer object returned by this function