code: def numericalize(self, arrs, device=None):
          
          numericalized = []
          self.nesting_field.include_lengths = False
          if self.include_lengths:
              arrs, sentence_lengths, word_lengths = arrs
  
          for arr in arrs:
              numericalized_ex = self.nesting_field.numericalize(
                  arr, device=device)
              numericalized.append(numericalized_ex)
          padded_batch = torch.stack(numericalized)
  
          self.nesting_field.include_lengths = True
          if self.include_lengths:
              sentence_lengths = \
                  torch.tensor(sentence_lengths, dtype=self.dtype, device=device)
              word_lengths = torch.tensor(word_lengths, dtype=self.dtype, device=device)
              return (padded_batch, sentence_lengths, word_lengths)
          return padded_batch
docs: Convert a padded minibatch into a variable tensor.
  
          Each item in the minibatch will be numericalized independently and the resulting
          tensors will be stacked at the first dimension.
  
          Arguments:
              arr (List[List[str]]): List of tokenized and padded examples.
              device (str or torch.device): A string or instance of `torch.device`
                  specifying which device the Variables are going to be created on.
                  If left as default, the tensors will be created on cpu. Default: None.