code: def recog(args):
      """Decode with the given args.
  
      Args:
          args (namespace): The program arguments.
  
      """
      set_deterministic_pytorch(args)
      model, train_args = load_trained_model(args.model)
      assert isinstance(model, ASRInterface)
      model.recog_args = args
  
      # read rnnlm
      if args.rnnlm:
          rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
          if (
              getattr(rnnlm_args, "model_module", "default")
              != "default"
          ):
              raise ValueError(
                  "use '--api v2' option to decode with non-default language model"
              )
          rnnlm = lm_pytorch.ClassifierWithState(
              lm_pytorch.RNNLM(
                  len(train_args.char_list),
                  rnnlm_args.layer,
                  rnnlm_args.unit,
                  getattr(
                      rnnlm_args, "embed_unit", None
                  ),  # for backward compatibility
              )
          )
          torch_load(args.rnnlm, rnnlm)
          rnnlm.eval()
      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_pytorch.ClassifierWithState(
              lm_pytorch.RNNLM(
                  len(word_dict), rnnlm_args.layer, rnnlm_args.unit
              )
          )
          torch_load(args.word_rnnlm, word_rnnlm)
          word_rnnlm.eval()
  
          if rnnlm is not None:
              rnnlm = lm_pytorch.ClassifierWithState(
                  extlm_pytorch.MultiLevelLM(
                      word_rnnlm.predictor,
                      rnnlm.predictor,
                      word_dict,
                      char_dict,
                  )
              )
          else:
              rnnlm = lm_pytorch.ClassifierWithState(
                  extlm_pytorch.LookAheadWordLM(
                      word_rnnlm.predictor, word_dict, char_dict
                  )
              )
  
      # gpu
      if args.ngpu == 1:
          gpu_id = list(range(args.ngpu))
          logging.info("gpu id: " + str(gpu_id))
          model.cuda()
          if rnnlm:
              rnnlm.cuda()
  
      # read json data
      with open(args.recog_json, "rb") as f:
          js = json.load(f)["utts"]
      new_js = {}
  
      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},
      )
  
      if args.batchsize == 0:
          with torch.no_grad():
              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
                  )
  
      else:
  
          def grouper(n, iterable, fillvalue=None):
              kargs = [iter(iterable)] * n
              return zip_longest(*kargs, fillvalue=fillvalue)
  
          # sort data if batchsize > 1
          keys = list(js.keys())
          if args.batchsize > 1:
              feat_lens = [
                  js[key]["input"][0]["shape"][0] for key in keys
              ]
              sorted_index = sorted(
                  range(len(feat_lens)), key=lambda i: -feat_lens[i]
              )
              keys = [keys[i] for i in sorted_index]
  
          with torch.no_grad():
              for names in grouper(args.batchsize, keys, None):
                  names = [name for name in names if name]
                  batch = [(name, js[name]) for name in names]
                  feats = load_inputs_and_targets(batch)[0]
                  nbest_hyps = model.recognize_batch(
                      feats, args, train_args.char_list, rnnlm=rnnlm
                  )
  
                  for i, name in enumerate(names):
                      nbest_hyp = [hyp[i] for hyp in nbest_hyps]
                      new_js[name] = add_results_to_json(
                          js[name], nbest_hyp, 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
  from itertools import zip_longest
  import numpy
  import torch
  from chainer import training
  from chainer.training import extensions
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.asr_mix_utils import add_results_to_json
  from espnet.asr.asr_utils import CompareValueTrigger, adadelta_eps_decay, get_model_conf, restore_snapshot, snapshot_object, torch_load, torch_resume, torch_snapshot
  from espnet.asr.pytorch_backend.asr import CustomEvaluator, CustomUpdater, load_trained_model
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.pytorch_backend.e2e_asr_mix import pad_list
  from espnet.utils.dataset import ChainerDataLoader, TransformDataset
  from espnet.utils.deterministic_utils import set_deterministic_pytorch
  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.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 a global function with example usage: recog(args) and returns: zip_longest