docstr: Compute LM loss value from buffer sequences.
  
  Args:
      input (torch.Tensor): Input ids. (batch, len)
      hidden (torch.Tensor): Target ids. (batch, len)
docstr_tok: ["Compute","LM","loss","value","from","buffer","sequences",".","Args",":","input","(","torch.Tensor",")",":","Input","ids",".","(","batch",",","len",")","hidden","(","torch.Tensor",")",":","Target","ids",".","(","batch",",","len",")"]
code: def forward(
      self, input: torch.Tensor, hidden: None
  ) -> Tuple[torch.Tensor, None]:
      """Compute LM loss value from buffer sequences.
  
      Args:
          input (torch.Tensor): Input ids. (batch, len)
          hidden (torch.Tensor): Target ids. (batch, len)
  
      """
      x = self.embed(input)
      mask = self._target_mask(input)
      h, _ = self.encoder(x, mask)
      y = self.decoder(h)
      return y, None
  
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 3 for calling the TransformerLM obj's underlying member method code with example usage: obj.forward(input, hidden) and returns: y,  None