class_docstr: A wrapper for pytorch RNNLM.
class_docstr_tok: ["A","wrapper","for","pytorch","RNNLM","."]
docstr: Predict new tokens from buffered inputs.
docstr_tok: ["Predict","new","tokens","from","buffered","inputs","."]
code: def buff_predict(self, state, x, n):
      """Predict new tokens from buffered inputs."""
      if self.predictor.__class__.__name__ == "RNNLM":
          return self.predict(state, x)
  
      new_state = []
      new_log_y = []
      for i in range(n):
          state_i = None if state is None else state[i]
          state_i, log_y = self.predict(state_i, x[i].unsqueeze(0))
          new_state.append(state_i)
          new_log_y.append(log_y)
  
      return new_state, torch.cat(new_log_y)
  
imports: import logging
  from typing import Any, List, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet.nets.lm_interface import LMInterface
  from espnet.nets.pytorch_backend.e2e_asr import to_device
  from espnet.nets.scorer_interface import BatchScorerInterface
  from espnet.utils.cli_utils import strtobool
label_desc: Use image node_id 4 for calling the ClassifierWithState obj's underlying member method code with example usage: obj.buff_predict(state, x, n) and returns: new_state,  torch, self