code: def trans(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, MTInterface)
      model.trans_args = args
  
      # gpu
      if args.ngpu == 1:
          gpu_id = list(range(args.ngpu))
          logging.info("gpu id: " + str(gpu_id))
          model.cuda()
  
      # read json data
      with open(args.trans_json, "rb") as f:
          js = json.load(f)["utts"]
      new_js = {}
  
      # remove enmpy utterances
      if train_args.multilingual:
          js = {
              k: v
              for k, v in js.items()
              if v["output"][0]["shape"][0] > 1
              and v["output"][1]["shape"][0] > 1
          }
      else:
          js = {
              k: v
              for k, v in js.items()
              if v["output"][0]["shape"][0] > 0
              and v["output"][1]["shape"][0] > 0
          }
  
      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())
                  )
                  feat = [js[name]["output"][1]["tokenid"].split()]
                  nbest_hyps = model.translate(
                      feat, args, train_args.char_list
                  )
                  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 itertools.zip_longest(*kargs, fillvalue=fillvalue)
  
          # sort data
          keys = list(js.keys())
          feat_lens = [js[key]["output"][1]["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]
                  feats = [
                      np.fromiter(
                          map(
                              int,
                              js[name]["output"][1]["tokenid"].split(),
                          ),
                          dtype=np.int64,
                      )
                      for name in names
                  ]
                  nbest_hyps = model.translate_batch(
                      feats,
                      args,
                      train_args.char_list,
                  )
  
                  for i, nbest_hyp in enumerate(nbest_hyps):
                      name = names[i]
                      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 itertools
  import json
  import logging
  import os
  import numpy
  import torch
  from chainer import training
  from chainer.training import extensions
  from espnet.asr.asr_utils import CompareValueTrigger, adadelta_eps_decay, adam_lr_decay, add_results_to_json, 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.mt_interface import MTInterface
  from espnet.nets.pytorch_backend.e2e_asr 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: trans(args) and returns: itertools