class_docstr: Sequential RNNLM.
  
  See also:
      https://github.com/pytorch/examples/blob/4581968193699de14b56527296262dd76ab43557/word_language_model/model.py
class_docstr_tok: ["Sequential","RNNLM",".","See","also",":","https",":","\/\/github.com\/pytorch\/examples\/blob\/4581968193699de14b56527296262dd76ab43557\/word_language_model\/model.py"]
docstr: Score new token.
  
  Args:
      y (torch.Tensor): 1D torch.int64 prefix tokens.
      state: Scorer state for prefix tokens
      x (torch.Tensor): 2D encoder feature that generates ys.
  
  Returns:
      tuple[torch.Tensor, Any]: Tuple of
          torch.float32 scores for next token (n_vocab)
          and next state for ys
docstr_tok: ["Score","new","token",".","Args",":","y","(","torch.Tensor",")",":","1D","torch.int64","prefix","tokens",".","state",":","Scorer","state","for","prefix","tokens","x","(","torch.Tensor",")",":","2D","encoder","feature","that","generates","ys",".","Returns",":","tuple","[","torch.Tensor",",","Any","]",":","Tuple","of","torch.float32","scores","for","next","token","(","n_vocab",")","and","next","state","for","ys"]
code: def score(self, y, state, x):
      """Score new token.
  
      Args:
          y (torch.Tensor): 1D torch.int64 prefix tokens.
          state: Scorer state for prefix tokens
          x (torch.Tensor): 2D encoder feature that generates ys.
  
      Returns:
          tuple[torch.Tensor, Any]: Tuple of
              torch.float32 scores for next token (n_vocab)
              and next state for ys
  
      """
      y, new_state = self._before_loss(y[-1].view(1, 1), state)
      logp = y.log_softmax(dim=-1).view(-1)
      return logp, new_state
  
file_path: espnet/espnet/nets/pytorch_backend/lm/seq_rnn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from espnet.nets.lm_interface import LMInterface
label_desc: Use image node_id 8 for calling the SequentialRNNLM obj's underlying member method code with example usage: obj.score(y, state, x) and returns: logp,  new_state