code: def forward(self, x):
      cloned = x.clone()
      length = cloned.size(1)
      n_mask = int((length + self.stride - 0.1) // self.stride)
      ts = torch.randint(0, self.window, size=(n_mask, 2))
      for t, t_end in ts:
          if length - t <= 0:
              continue
          t_start = random.randrange(0, length - t)
          if t_start == t_start + t:
              continue
          t_end += t_start
          cloned[:, t_start:t_end] = 0
      return cloned
  
imports: import random
  from typing import List
  import sentencepiece
  import torch
  import torchvision
  from data_module import LRS3DataModule
  from lightning import Batch
  from lightning_av import AVBatch
label_desc: Use image node_id 2 for calling the AdaptiveTimeMask obj's underlying member method code with example usage: obj.forward(x) and returns: cloned