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, training=False)
      assert isinstance(model, ASRInterface)
      model.recog_args = args
  
      if args.quantize_config is not None:
          q_config = set(
              [getattr(torch.nn, q) for q in args.quantize_config]
          )
      else:
          q_config = {torch.nn.Linear}
  
      if args.quantize_asr_model:
          logging.info("Use a quantized ASR model for decoding.")
  
          # It seems quantized LSTM only supports non-packed sequence before torch 1.4.0.
          # Reference issue: https://github.com/pytorch/pytorch/issues/27963
          if (
              V(torch.__version__) < V("1.4.0")
              and "lstm" in train_args.etype
              and torch.nn.LSTM in q_config
          ):
              raise ValueError(
                  "Quantized LSTM in ESPnet is only supported with torch 1.4+."
              )
  
          # Dunno why but weight_observer from dynamic quantized module must have
          # dtype=torch.qint8 with torch < 1.5 although dtype=torch.float16 is supported.
          if args.quantize_dtype == "float16" and V(
              torch.__version__
          ) < V("1.5.0"):
              raise ValueError(
                  "float16 dtype for dynamic quantization is not supported with torch "
                  "version < 1.5.0. Switching to qint8 dtype instead."
              )
  
          dtype = getattr(torch, args.quantize_dtype)
  
          model = torch.quantization.quantize_dynamic(
              model, q_config, dtype=dtype
          )
  
      if (
          args.streaming_mode
          and "transformer" in train_args.model_module
      ):
          raise NotImplementedError(
              "streaming mode for transformer is not implemented"
          )
      logging.info(
          " Total parameter of the model = "
          + str(sum(p.numel() for p in model.parameters()))
      )
  
      # 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)
          if args.quantize_lm_model:
              dtype = getattr(torch, args.quantize_dtype)
              rnnlm = torch.quantization.quantize_dynamic(
                  rnnlm, q_config, dtype=dtype
              )
          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,
                  getattr(
                      rnnlm_args, "embed_unit", None
                  ),  # for backward compatibility
              )
          )
          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},
      )
  
      # load transducer beam search
      if hasattr(model, "is_transducer"):
          if hasattr(model, "dec"):
              trans_decoder = model.dec
          else:
              trans_decoder = model.decoder
          joint_network = model.transducer_tasks.joint_network
  
          beam_search_transducer = BeamSearchTransducer(
              decoder=trans_decoder,
              joint_network=joint_network,
              beam_size=args.beam_size,
              lm=rnnlm,
              lm_weight=args.lm_weight,
              search_type=args.search_type,
              max_sym_exp=args.max_sym_exp,
              u_max=args.u_max,
              nstep=args.nstep,
              prefix_alpha=args.prefix_alpha,
              expansion_gamma=args.expansion_gamma,
              expansion_beta=args.expansion_beta,
              score_norm=args.score_norm,
              softmax_temperature=args.softmax_temperature,
              nbest=args.nbest,
              quantization=args.quantize_asr_model,
          )
  
      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)
                  feat = (
                      feat[0][0]
                      if args.num_encs == 1
                      else [
                          feat[idx][0] for idx in range(model.num_encs)
                      ]
                  )
                  if (
                      args.streaming_mode == "window"
                      and args.num_encs == 1
                  ):
                      logging.info(
                          "Using streaming recognizer with window size %d frames",
                          args.streaming_window,
                      )
                      se2e = WindowStreamingE2E(
                          e2e=model, recog_args=args, rnnlm=rnnlm
                      )
                      for i in range(
                          0, feat.shape[0], args.streaming_window
                      ):
                          logging.info(
                              "Feeding frames %d - %d",
                              i,
                              i + args.streaming_window,
                          )
                          se2e.accept_input(
                              feat[i : i + args.streaming_window]
                          )
                      logging.info("Running offline attention decoder")
                      se2e.decode_with_attention_offline()
                      logging.info("Offline attention decoder finished")
                      nbest_hyps = se2e.retrieve_recognition()
                  elif (
                      args.streaming_mode == "segment"
                      and args.num_encs == 1
                  ):
                      logging.info(
                          "Using streaming recognizer with threshold value %d",
                          args.streaming_min_blank_dur,
                      )
                      nbest_hyps = []
                      for n in range(args.nbest):
                          nbest_hyps.append({"yseq": [], "score": 0.0})
                      se2e = SegmentStreamingE2E(
                          e2e=model, recog_args=args, rnnlm=rnnlm
                      )
                      r = np.prod(model.subsample)
                      for i in range(0, feat.shape[0], r):
                          hyps = se2e.accept_input(feat[i : i + r])
                          if hyps is not None:
                              text = "".join(
                                  [
                                      train_args.char_list[int(x)]
                                      for x in hyps[0]["yseq"][1:-1]
                                      if int(x) != -1
                                  ]
                              )
                              text = text.replace(
                                  "\u2581", " "
                              ).strip()  # for SentencePiece
                              text = text.replace(model.space, " ")
                              text = text.replace(model.blank, "")
                              logging.info(text)
                              for n in range(args.nbest):
                                  nbest_hyps[n]["yseq"].extend(
                                      hyps[n]["yseq"]
                                  )
                                  nbest_hyps[n]["score"] += hyps[n][
                                      "score"
                                  ]
                  elif hasattr(model, "is_transducer"):
                      nbest_hyps = model.recognize(
                          feat, beam_search_transducer
                      )
                  else:
                      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 itertools.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]
                      if args.num_encs == 1
                      else load_inputs_and_targets(batch)
                  )
                  if (
                      args.streaming_mode == "window"
                      and args.num_encs == 1
                  ):
                      raise NotImplementedError
                  elif (
                      args.streaming_mode == "segment"
                      and args.num_encs == 1
                  ):
                      if args.batchsize > 1:
                          raise NotImplementedError
                      feat = feats[0]
                      nbest_hyps = []
                      for n in range(args.nbest):
                          nbest_hyps.append({"yseq": [], "score": 0.0})
                      se2e = SegmentStreamingE2E(
                          e2e=model, recog_args=args, rnnlm=rnnlm
                      )
                      r = np.prod(model.subsample)
                      for i in range(0, feat.shape[0], r):
                          hyps = se2e.accept_input(feat[i : i + r])
                          if hyps is not None:
                              text = "".join(
                                  [
                                      train_args.char_list[int(x)]
                                      for x in hyps[0]["yseq"][1:-1]
                                      if int(x) != -1
                                  ]
                              )
                              text = text.replace(
                                  "\u2581", " "
                              ).strip()  # for SentencePiece
                              text = text.replace(model.space, " ")
                              text = text.replace(model.blank, "")
                              logging.info(text)
                              for n in range(args.nbest):
                                  nbest_hyps[n]["yseq"].extend(
                                      hyps[n]["yseq"]
                                  )
                                  nbest_hyps[n]["score"] += hyps[n][
                                      "score"
                                  ]
                      nbest_hyps = [nbest_hyps]
                  else:
                      nbest_hyps = model.recognize_batch(
                          feats, args, train_args.char_list, rnnlm=rnnlm
                      )
  
                  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 copy
  import itertools
  import json
  import logging
  import math
  import os
  import numpy
  import torch
  import torch.distributed
  from chainer import reporter
  from chainer import training
  from chainer.training import extensions
  from chainer.training.updater import StandardUpdater
  from packaging.version import parse
  from torch.nn.parallel import DistributedDataParallel
  from torch.nn.parallel import data_parallel
  from torch.utils.data.distributed import DistributedSampler
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.asr_utils import CompareValueTrigger, adadelta_eps_decay, add_results_to_json, format_mulenc_args, get_model_conf, plot_spectrogram, restore_snapshot, snapshot_object, torch_load, torch_resume, torch_snapshot
  from espnet.asr.pytorch_backend.asr_init import freeze_modules, load_trained_model, load_trained_modules
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.e2e_asr import pad_list
  from espnet.nets.pytorch_backend.streaming.segment import SegmentStreamingE2E
  from espnet.nets.pytorch_backend.streaming.window import WindowStreamingE2E
  from espnet.transform.spectrogram import IStft
  from espnet.transform.transformation import Transformation
  from espnet.utils.cli_writers import file_writer_helper
  from espnet.utils.dataset import ChainerDataLoader, Transform, 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.evaluator import BaseEvaluator
  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 4 for calling a global function with example usage: recog(args) and returns: itertools