class_docstr: Sequential RNNLM.
  
  See also:
      https://github.com/pytorch/examples/blob/4581968193699de14b56527296262dd76ab43557/word_language_model/model.py
docstr: Score new token.
  
  Args:
      y: 1D torch.int64 prefix tokens.
      state: Scorer state for prefix tokens
      x: 2D encoder feature that generates ys.
  
  Returns:
      Tuple of
          torch.float32 scores for next token (n_vocab)
          and next state for ys
code: def score(
      self,
      y: torch.Tensor,
      state: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
      x: torch.Tensor,
  ) -> Tuple[
      torch.Tensor,
      Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
  ]:
      """Score new token.
  
      Args:
          y: 1D torch.int64 prefix tokens.
          state: Scorer state for prefix tokens
          x: 2D encoder feature that generates ys.
  
      Returns:
          Tuple of
              torch.float32 scores for next token (n_vocab)
              and next state for ys
  
      """
      y, new_state = self(y[-1].view(1, 1), state)
      logp = y.log_softmax(dim=-1).view(-1)
      return logp, new_state
  
file_path: espnet/espnet2/lm/seq_rnn_lm.py
imports: from typing import Tuple, Union
  import torch
  import torch.nn
  from typeguard import check_argument_types
  from espnet2.lm.abs_model import AbsLM
label_desc: Use image node_id 4 for calling the SequentialRNNLM obj's underlying member method code with example usage: obj.score(y, state, x) and returns: logp,  new_state