code: def _max_by_axis(the_list):
      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 paddle
  import paddle.nn
  import paddle.nn.functional
  from scipy.optimize import linear_sum_assignment
  from paddlepanseg.cvlibs import manager, build_info_dict
  from paddlepanseg.core.runner import PanSegRunner
label_desc: Use image node_id 8 for calling a global function with example usage: _max_by_axis(the_list) and returns: maxes