class_docstr: A chainer RNNLM
  
  :param int n_vocab: The size of the vocabulary
  :param int n_layers: The number of layers to create
  :param int n_units: The number of units per layer
  :param str type: The RNN type
class_docstr_tok: ["A","chainer","RNNLM",":","param","int","n_vocab",":","The","size","of","the","vocabulary",":","param","int","n_layers",":","The","number","of","layers","to","create",":","param","int","n_units",":","The","number","of","units","per","layer",":","param","str","type",":","The","RNN","type"]
code: def __call__(self, state, x):
      if state is None:
          if self.typ == "lstm":
              state = {
                  "c": [None] * self.n_layers,
                  "h": [None] * self.n_layers,
              }
          else:
              state = {"h": [None] * self.n_layers}
  
      h = [None] * self.n_layers
      emb = self.embed(x)
      if self.typ == "lstm":
          c = [None] * self.n_layers
          c[0], h[0] = self.rnn[0](
              state["c"][0], state["h"][0], F.dropout(emb)
          )
          for n in range(1, self.n_layers):
              c[n], h[n] = self.rnn[n](
                  state["c"][n], state["h"][n], F.dropout(h[n - 1])
              )
          state = {"c": c, "h": h}
      else:
          if state["h"][0] is None:
              xp = self.xp
              with chainer.backends.cuda.get_device_from_id(
                  self._device_id
              ):
                  state["h"][0] = chainer.Variable(
                      xp.zeros(
                          (emb.shape[0], self.n_units), dtype=emb.dtype
                      )
                  )
          h[0] = self.rnn[0](state["h"][0], F.dropout(emb))
          for n in range(1, self.n_layers):
              if state["h"][n] is None:
                  xp = self.xp
                  with chainer.backends.cuda.get_device_from_id(
                      self._device_id
                  ):
                      state["h"][n] = chainer.Variable(
                          xp.zeros(
                              (h[n - 1].shape[0], self.n_units),
                              dtype=h[n - 1].dtype,
                          )
                      )
              h[n] = self.rnn[n](state["h"][n], F.dropout(h[n - 1]))
          state = {"h": h}
      y = self.lo(F.dropout(h[-1]))
      return state, y
  
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 2 for calling the RNNLM obj's underlying member method code with example usage: obj.__call__(state, x) and returns: state,  y