code: def mask_and_tensorize(self, batch):
      batch = list(batch)
      if not batch:
          return torch.Tensor()
      masked_sources = []
      masked_targets = []
  
      for tokens in batch:
          dec_source, dec_target = self.mask.gen_masked_source_target(
              tokens, vocab=self.vocab
          )
  
          masked_sources.append(dec_source)
          masked_targets.append(dec_target)
  
      return (
          cuda.tensor(
              pad(masked_sources, pad_token=self.vocab.get_pad_index()),
              dtype=torch.long,
          ),
          cuda.tensor(
              pad(masked_targets, pad_token=self.vocab.get_pad_index()),
              dtype=torch.long,
          ),
      )
  
file_path: pytext/pytext/data/masked_tensorizer.py
imports: import torch
  from pytext.config.component import ComponentType, create_component
  from pytext.data.masked_util import MaskedVocabBuilder, MaskingFunction, RandomizedMaskingFunction
  from pytext.data.tensorizers import TokenTensorizer
  from pytext.data.utils import pad, pad_and_tensorize
  from pytext.utils import cuda
label_desc: Use image node_id 3 for calling the MaskedTokenTensorizer obj's underlying member method code with example usage: obj.mask_and_tensorize(batch) and returns: cuda,  cuda, torch