code: def pad(var_len_list, out_type="list", flatten=False):
      if flatten:
          lens = [len(x) for x in var_len_list]
          var_len_list = sum(var_len_list, [])
      max_len = max([len(x) for x in var_len_list])
      if out_type == "list":
          if flatten:
              return [
                  x + ["<PAD>"] * (max_len - len(x))
                  for x in var_len_list
              ], lens
          else:
              return [
                  x + ["<PAD>"] * (max_len - len(x))
                  for x in var_len_list
              ]
      if out_type == "tensor":
          if flatten:
              return (
                  torch.stack(
                      [
                          torch.cat(
                              [
                                  x,
                                  torch.zeros(
                                      [max_len - len(x)]
                                      + list(x.shape[1:])
                                  ).type_as(x),
                              ],
                              0,
                          )
                          for x in var_len_list
                      ],
                      0,
                  ),
                  lens,
              )
          else:
              return torch.stack(
                  [
                      torch.cat(
                          [
                              x,
                              torch.zeros(
                                  [max_len - len(x)] + list(x.shape[1:])
                              ).type_as(x),
                          ],
                          0,
                      )
                      for x in var_len_list
                  ],
                  0,
              )
  
imports: import json
  import pickle
  import random
  import dgl
  import numpy
  import torch
label_desc: Use image node_id 4 for calling a global function with example usage: pad(var_len_list, out_type, flatten) and returns: unknown,  lens, unknown, torch,  lens, torch