code: def __call__(self, state, x):
      # update state with input label x
      if (
          state is None
      ):  # make initial states and cumlative probability vector
          wlm_state, z_wlm = self.wordlm(None, self.xp_word_eos)
          cumsum_probs = self.xp.cumsum(F.softmax(z_wlm).data, axis=1)
          new_node = self.lexroot
          xi = self.space
      else:
          wlm_state, cumsum_probs, node = 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 cumlative probability vector
              wlm_state, z_wlm = self.wordlm(wlm_state, w)
              cumsum_probs = self.xp.cumsum(
                  F.softmax(z_wlm).data, axis=1
              )
              new_node = self.lexroot  # move to the tree root
          elif (
              node is not None and xi in node[0]
          ):  # intra-word transition
              new_node = node[0][xi]
          elif (
              self.open_vocab
          ):  # if no path in the tree, enter open-vocabulary mode
              new_node = None
          else:  # if open_vocab flag is disabled, return 0 probabilities
              log_y = self.xp.full(
                  (1, self.subword_dict_size), self.logzero, "f"
              )
              return (wlm_state, None, None), log_y
  
      if new_node is not None:
          succ, wid, wids = new_node
          # compute parent node probability
          sum_prob = (
              (cumsum_probs[:, wids[1]] - cumsum_probs[:, wids[0]])
              if wids is not None
              else 1.0
          )
          if sum_prob < self.zero:
              log_y = self.xp.full(
                  (1, self.subword_dict_size), self.logzero, "f"
              )
              return (wlm_state, cumsum_probs, new_node), log_y
          # set <unk> probability as a default value
          unk_prob = (
              cumsum_probs[:, self.word_unk]
              - cumsum_probs[:, self.word_unk - 1]
          )
          y = self.xp.full(
              (1, self.subword_dict_size),
              unk_prob * self.oov_penalty,
              "f",
          )
          # compute transition probabilities to child nodes
          for cid, nd in succ.items():
              y[:, cid] = (
                  cumsum_probs[:, nd[2][1]] - cumsum_probs[:, nd[2][0]]
              ) / sum_prob
          # apply word-level probabilies for <space> and <eos> labels
          if wid >= 0:
              wlm_prob = (
                  cumsum_probs[:, wid] - cumsum_probs[:, wid - 1]
              ) / sum_prob
              y[:, self.space] = wlm_prob
              y[:, self.eos] = wlm_prob
          elif xi == self.space:
              y[:, self.space] = self.zero
              y[:, self.eos] = self.zero
          log_y = self.xp.log(
              self.xp.clip(y, self.zero, None)
          )  # clip to avoid log(0)
      else:  # if no path in the tree, transition probability is one
          log_y = self.xp.zeros((1, self.subword_dict_size), "f")
      return (wlm_state, cumsum_probs, new_node), 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 LookAheadWordLM obj's underlying member method code with example usage: obj.__call__(state, x) and returns: wlm_state,  cumsum_probs,  new_node,  log_y, wlm_state,  cumsum_probs,  new_node,  log_y, wlm_state,  None,  None,  log_y