class_docstr: A wrapper for a chainer RNNLM
  
  :param link.Chain predictor : The RNNLM
  :param function lossfun: The loss function to use
  :param int/str label_key:
class_docstr_tok: ["A","wrapper","for","a","chainer","RNNLM",":","param","link.Chain","predictor",":","The","RNNLM",":","param","function","lossfun",":","The","loss","function","to","use",":","param","int\/str","label_key",":"]
docstr: Predict log probabilities for given state and input x using the predictor
  
  :param state : the state
  :param x : the input
  :return a tuple (state, log prob vector)
  :rtype cupy/numpy array
docstr_tok: ["Predict","log","probabilities","for","given","state","and","input","x","using","the","predictor",":","param","state",":","the","state",":","param","x",":","the","input",":","return","a","tuple","(","state",",","log","prob","vector",")",":","rtype","cupy\/numpy","array"]
code: def predict(self, state, x):
      """Predict log probabilities for given state and input x using the predictor
  
      :param state : the state
      :param x : the input
      :return a tuple (state, log prob vector)
      :rtype cupy/numpy array
      """
      if (
          hasattr(self.predictor, "normalized")
          and self.predictor.normalized
      ):
          return self.predictor(state, x)
      else:
          state, z = self.predictor(state, x)
          return state, F.log_softmax(z).data
  
imports: import copy
  import json
  import logging
  import chainer
  import chainer.functions
  import chainer.links
  import numpy
  from chainer import link, reporter, training
  from chainer.dataset import convert
  from chainer.functions.loss import softmax_cross_entropy
  from chainer.training import extensions
  import espnet.nets.chainer_backend.deterministic_embed_id
  from espnet.lm.lm_utils import MakeSymlinkToBestModel, ParallelSentenceIterator, compute_perplexity, count_tokens, read_tokens
  from espnet.nets.lm_interface import LMInterface
  from espnet.optimizer.factory import dynamic_import_optimizer
  from espnet.scheduler.chainer import ChainerScheduler
  from espnet.scheduler.scheduler import dynamic_import_scheduler
  from espnet.utils.deterministic_utils import set_deterministic_chainer
  from espnet.utils.training.evaluator import BaseEvaluator
  from espnet.utils.training.iterators import ShufflingEnabler
  from espnet.utils.training.tensorboard_logger import TensorboardLogger
  from espnet.utils.training.train_utils import check_early_stop, set_early_stop
label_desc: Use image node_id 3 for calling the ClassifierWithState obj's underlying member method code with example usage: obj.predict(state, x) and returns: self, state,  F