code: def postprocess(info_list: List[dict]) -> List[dict]:
      for idx, info in enumerate(info_list):
          depth = info["depth"]
          if idx > 0:  # root module (0) is exclued
              if depth == 1:
                  prefix = "├─"
              else:
                  prefix = f"{'│    '*(depth-1)}└─"
              info["name"] = prefix + info["name"]
  
          if info["input_shape"]:
              info["input_shape"] = info["input_shape"].pop(0)
              info["output_shape"] = info["output_shape"].pop(0)
          else:
              info["input_shape"] = "--"
              info["output_shape"] = "--"
      return info_list
  
file_path: pytorch_geometric/torch_geometric/nn/summary.py
imports: from collections import defaultdict
  from typing import Any, List, Optional, Union
  import torch
  from torch.jit import ScriptModule
  from torch.nn import Module
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.dense.linear import is_uninitialized_parameter
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 3 for calling a global function with example usage: postprocess(info_list) and returns: info_list