class_docstr: Performs input / target transformation before feeding the data to a GeneralizedRCNN
  model.
  
  The transformations it perform are:
      - input normalization (mean subtraction and std division)
      - input / target resizing to match min_size / max_size
  
  It returns a ImageList for the inputs, and a List[Dict[Tensor]] for the targets
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code: def max_by_axis(self, the_list):
      # type: (List[List[int]]) -> List[int]
      maxes = the_list[0]
      for sublist in the_list[1:]:
          for index, item in enumerate(sublist):
              maxes[index] = max(maxes[index], item)
      return maxes
  
imports: import math
  from typing import List, Tuple, Dict, Optional
  import torch
  from torch import nn, Tensor
  import torch.nn.functional
  import torchvision
  from .image_list import ImageList
label_desc: Use image node_id 6 for calling the GeneralizedRCNNTransform obj's underlying member method code with example usage: obj.max_by_axis(the_list) and returns: maxes