code: def _target_mask(self, ys_in_pad):
      ys_mask = ys_in_pad != 0
      m = subsequent_mask(
          ys_mask.size(-1), device=ys_mask.device
      ).unsqueeze(0)
      return ys_mask.unsqueeze(-2) & m
  
file_path: espnet/espnet2/lm/transformer_lm.py
imports: from typing import Any, List, Tuple
  import torch
  import torch.nn
  from espnet2.lm.abs_model import AbsLM
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
label_desc: Use image node_id 2 for calling the TransformerLM obj's underlying member method code with example usage: obj._target_mask(ys_in_pad) and returns: unknown