docstr: 1 for padding tokens , 0 for non-padding tokens
docstr_tok: ["1","for","padding","tokens",",","0","for","non-padding","tokens"]
code: def attn_mask(self, batch_size, seq_len, dtype=None):
      """
      1 for padding tokens , 0 for non-padding tokens
      """
      if dtype is None:
          dtype = self.dtype
      mask = torch.zeros((batch_size, seq_len))
      for b in range(batch_size):
          valid_seq_len = random.randint(1, seq_len)
          mask[b, valid_seq_len:] = 1
      return mask.to(self.device, dtype=dtype)
  
imports: import random
  import time
  from collections import OrderedDict
  import numpy
  import torch
label_desc: Use image node_id 21 for calling the TestDecorator obj's underlying member method code with example usage: obj.attn_mask(batch_size, seq_len, dtype) and returns: mask