code: def __call__(self, state, x):
      # update state with input label x
      if state is None:  # make initial states and log-prob vectors
          wlm_state, z_wlm = self.wordlm(None, self.xp_word_eos)
          wlm_logprobs = F.log_softmax(z_wlm).data
          clm_state, z_clm = self.subwordlm(None, x)
          log_y = F.log_softmax(z_clm).data * self.subwordlm_weight
          new_node = self.lexroot
          clm_logprob = 0.0
          xi = self.space
      else:
          (
              clm_state,
              wlm_state,
              wlm_logprobs,
              node,
              log_y,
              clm_logprob,
          ) = state
          xi = int(x)
          if xi == self.space:  # inter-word transition
              if (
                  node is not None and node[1] >= 0
              ):  # check if the node is word end
                  w = self.xp.full(1, node[1], "i")
              else:  # this node is not a word end, which means <unk>
                  w = self.xp_word_unk
              # update wordlm state and log-prob vector
              wlm_state, z_wlm = self.wordlm(wlm_state, w)
              wlm_logprobs = F.log_softmax(z_wlm).data
              new_node = self.lexroot  # move to the tree root
              clm_logprob = 0.0
          elif (
              node is not None and xi in node[0]
          ):  # intra-word transition
              new_node = node[0][xi]
              clm_logprob += log_y[0, xi]
          elif (
              self.open_vocab
          ):  # if no path in the tree, enter open-vocabulary mode
              new_node = None
              clm_logprob += log_y[0, xi]
          else:  # if open_vocab flag is disabled, return 0 probabilities
              log_y = self.xp.full(
                  (1, self.subword_dict_size), self.logzero, "f"
              )
              return (clm_state, wlm_state, None, log_y, 0.0), log_y
  
          clm_state, z_clm = self.subwordlm(clm_state, x)
          log_y = F.log_softmax(z_clm).data * self.subwordlm_weight
  
      # apply word-level probabilies for <space> and <eos> labels
      if xi != self.space:
          if (
              new_node is not None and new_node[1] >= 0
          ):  # if new node is word end
              wlm_logprob = wlm_logprobs[:, new_node[1]] - clm_logprob
          else:
              wlm_logprob = (
                  wlm_logprobs[:, self.word_unk] + self.log_oov_penalty
              )
          log_y[:, self.space] = wlm_logprob
          log_y[:, self.eos] = wlm_logprob
      else:
          log_y[:, self.space] = self.logzero
          log_y[:, self.eos] = self.logzero
  
      return (
          clm_state,
          wlm_state,
          wlm_logprobs,
          new_node,
          log_y,
          clm_logprob,
      ), log_y
  
file_path: espnet/espnet/lm/chainer_backend/extlm.py
imports: import math
  import chainer
  import chainer.functions
  from espnet.lm.lm_utils import make_lexical_tree
label_desc: Use image node_id 2 for calling the MultiLevelLM obj's underlying member method code with example usage: obj.__call__(state, x) and returns: clm_state,  wlm_state,  wlm_logprobs,  new_node,  log_y,  clm_logprob,  log_y, clm_state,  wlm_state,  None,  log_y,  int,  log_y