code: def recog(args):
      """Decode with the given args.
  
      Args:
          args (namespace): The program arguments.
  
      """
      # display chainer version
      logging.info("chainer version = " + chainer.__version__)
  
      set_deterministic_chainer(args)
  
      # read training config
      idim, odim, train_args = get_model_conf(
          args.model, args.model_conf
      )
  
      for key in sorted(vars(args).keys()):
          logging.info("ARGS: " + key + ": " + str(vars(args)[key]))
  
      # specify model architecture
      logging.info("reading model parameters from " + args.model)
      # To be compatible with v.0.3.0 models
      if hasattr(train_args, "model_module"):
          model_module = train_args.model_module
      else:
          model_module = "espnet.nets.chainer_backend.e2e_asr:E2E"
      model_class = dynamic_import(model_module)
      model = model_class(idim, odim, train_args)
      assert isinstance(model, ASRInterface)
      chainer_load(args.model, model)
  
      # read rnnlm
      if args.rnnlm:
          rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
          rnnlm = lm_chainer.ClassifierWithState(
              lm_chainer.RNNLM(
                  len(train_args.char_list),
                  rnnlm_args.layer,
                  rnnlm_args.unit,
              )
          )
          chainer_load(args.rnnlm, rnnlm)
      else:
          rnnlm = None
  
      if args.word_rnnlm:
          rnnlm_args = get_model_conf(
              args.word_rnnlm, args.word_rnnlm_conf
          )
          word_dict = rnnlm_args.char_list_dict
          char_dict = {x: i for i, x in enumerate(train_args.char_list)}
          word_rnnlm = lm_chainer.ClassifierWithState(
              lm_chainer.RNNLM(
                  len(word_dict), rnnlm_args.layer, rnnlm_args.unit
              )
          )
          chainer_load(args.word_rnnlm, word_rnnlm)
  
          if rnnlm is not None:
              rnnlm = lm_chainer.ClassifierWithState(
                  extlm_chainer.MultiLevelLM(
                      word_rnnlm.predictor,
                      rnnlm.predictor,
                      word_dict,
                      char_dict,
                  )
              )
          else:
              rnnlm = lm_chainer.ClassifierWithState(
                  extlm_chainer.LookAheadWordLM(
                      word_rnnlm.predictor, word_dict, char_dict
                  )
              )
  
      # read json data
      with open(args.recog_json, "rb") as f:
          js = json.load(f)["utts"]
  
      load_inputs_and_targets = LoadInputsAndTargets(
          mode="asr",
          load_output=False,
          sort_in_input_length=False,
          preprocess_conf=train_args.preprocess_conf
          if args.preprocess_conf is None
          else args.preprocess_conf,
          preprocess_args={
              "train": False
          },  # Switch the mode of preprocessing
      )
  
      # decode each utterance
      new_js = {}
      with chainer.no_backprop_mode():
          for idx, name in enumerate(js.keys(), 1):
              logging.info(
                  "(%d/%d) decoding " + name, idx, len(js.keys())
              )
              batch = [(name, js[name])]
              feat = load_inputs_and_targets(batch)[0][0]
              nbest_hyps = model.recognize(
                  feat, args, train_args.char_list, rnnlm
              )
              new_js[name] = add_results_to_json(
                  js[name], nbest_hyps, train_args.char_list
              )
  
      with open(args.result_label, "wb") as f:
          f.write(
              json.dumps(
                  {"utts": new_js},
                  indent=4,
                  ensure_ascii=False,
                  sort_keys=True,
              ).encode("utf_8")
          )
  
imports: import json
  import logging
  import os
  import chainer
  from chainer import training
  from chainer.datasets import TransformDataset
  from chainer.training import extensions
  import espnet.lm.chainer_backend.extlm
  import espnet.lm.chainer_backend.lm
  from espnet.asr.asr_utils import CompareValueTrigger, adadelta_eps_decay, add_results_to_json, chainer_load, get_model_conf, restore_snapshot
  from espnet.nets.asr_interface import ASRInterface
  from espnet.utils.deterministic_utils import set_deterministic_chainer
  from espnet.utils.dynamic_import import dynamic_import
  from espnet.utils.io_utils import LoadInputsAndTargets
  from espnet.utils.training.batchfy import make_batchset
  from espnet.utils.training.evaluator import BaseEvaluator
  from espnet.utils.training.iterators import ShufflingEnabler, ToggleableShufflingMultiprocessIterator, ToggleableShufflingSerialIterator
  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 a global function with example usage: recog(args) without return types