code: def enhance(args):
      """Dumping enhanced speech and mask.
  
      Args:
          args (namespace): The program arguments.
      """
      set_deterministic_pytorch(args)
      # read training config
      idim, odim, train_args = get_model_conf(
          args.model, args.model_conf
      )
  
      # TODO(ruizhili): implement enhance for multi-encoder model
      assert (
          args.num_encs == 1
      ), "number of encoder should be 1 ({} is given)".format(
          args.num_encs
      )
  
      # load trained model parameters
      logging.info("reading model parameters from " + args.model)
      model_class = dynamic_import(train_args.model_module)
      model = model_class(idim, odim, train_args)
      assert isinstance(model, ASRInterface)
      torch_load(args.model, model)
      model.recog_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.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=None,  # Apply pre_process in outer func
      )
      if args.batchsize == 0:
          args.batchsize = 1
  
      # Creates writers for outputs from the network
      if args.enh_wspecifier is not None:
          enh_writer = file_writer_helper(
              args.enh_wspecifier, filetype=args.enh_filetype
          )
      else:
          enh_writer = None
  
      # Creates a Transformation instance
      preprocess_conf = (
          train_args.preprocess_conf
          if args.preprocess_conf is None
          else args.preprocess_conf
      )
      if preprocess_conf is not None:
          logging.info(f"Use preprocessing: {preprocess_conf}")
          transform = Transformation(preprocess_conf)
      else:
          transform = None
  
      # Creates a IStft instance
      istft = None
      frame_shift = args.istft_n_shift  # Used for plot the spectrogram
      if args.apply_istft:
          if preprocess_conf is not None:
              # Read the conffile and find stft setting
              with open(preprocess_conf) as f:
                  # Json format: e.g.
                  #    {"process": [{"type": "stft",
                  #                  "win_length": 400,
                  #                  "n_fft": 512, "n_shift": 160,
                  #                  "window": "han"},
                  #                 {"type": "foo", ...}, ...]}
                  conf = json.load(f)
                  assert "process" in conf, conf
                  # Find stft setting
                  for p in conf["process"]:
                      if p["type"] == "stft":
                          istft = IStft(
                              win_length=p["win_length"],
                              n_shift=p["n_shift"],
                              window=p.get("window", "hann"),
                          )
                          logging.info(
                              "stft is found in {}. "
                              "Setting istft config from it\n{}".format(
                                  preprocess_conf, istft
                              )
                          )
                          frame_shift = p["n_shift"]
                          break
          if istft is None:
              # Set from command line arguments
              istft = IStft(
                  win_length=args.istft_win_length,
                  n_shift=args.istft_n_shift,
                  window=args.istft_window,
              )
              logging.info(
                  "Setting istft config from the command line args\n{}".format(
                      istft
                  )
              )
  
      # sort data
      keys = list(js.keys())
      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]
  
      def grouper(n, iterable, fillvalue=None):
          kargs = [iter(iterable)] * n
          return itertools.zip_longest(*kargs, fillvalue=fillvalue)
  
      num_images = 0
      if not os.path.exists(args.image_dir):
          os.makedirs(args.image_dir)
  
      for names in grouper(args.batchsize, keys, None):
          batch = [(name, js[name]) for name in names]
  
          # May be in time region: (Batch, [Time, Channel])
          org_feats = load_inputs_and_targets(batch)[0]
          if transform is not None:
              # May be in time-freq region: : (Batch, [Time, Channel, Freq])
              feats = transform(org_feats, train=False)
          else:
              feats = org_feats
  
          with torch.no_grad():
              enhanced, mask, ilens = model.enhance(feats)
  
          for idx, name in enumerate(names):
              # Assuming mask, feats : [Batch, Time, Channel. Freq]
              #          enhanced    : [Batch, Time, Freq]
              enh = enhanced[idx][: ilens[idx]]
              mas = mask[idx][: ilens[idx]]
              feat = feats[idx]
  
              # Plot spectrogram
              if (
                  args.image_dir is not None
                  and num_images < args.num_images
              ):
                  import matplotlib
  
                  matplotlib.use("Agg")
                  import matplotlib.pyplot as plt
  
                  num_images += 1
                  ref_ch = 0
  
                  plt.figure(figsize=(20, 10))
                  plt.subplot(4, 1, 1)
                  plt.title("Mask [ref={}ch]".format(ref_ch))
                  plot_spectrogram(
                      plt,
                      mas[:, ref_ch].T,
                      fs=args.fs,
                      mode="linear",
                      frame_shift=frame_shift,
                      bottom=False,
                      labelbottom=False,
                  )
  
                  plt.subplot(4, 1, 2)
                  plt.title("Noisy speech [ref={}ch]".format(ref_ch))
                  plot_spectrogram(
                      plt,
                      feat[:, ref_ch].T,
                      fs=args.fs,
                      mode="db",
                      frame_shift=frame_shift,
                      bottom=False,
                      labelbottom=False,
                  )
  
                  plt.subplot(4, 1, 3)
                  plt.title("Masked speech [ref={}ch]".format(ref_ch))
                  plot_spectrogram(
                      plt,
                      (feat[:, ref_ch] * mas[:, ref_ch]).T,
                      frame_shift=frame_shift,
                      fs=args.fs,
                      mode="db",
                      bottom=False,
                      labelbottom=False,
                  )
  
                  plt.subplot(4, 1, 4)
                  plt.title("Enhanced speech")
                  plot_spectrogram(
                      plt,
                      enh.T,
                      fs=args.fs,
                      mode="db",
                      frame_shift=frame_shift,
                  )
  
                  plt.savefig(
                      os.path.join(args.image_dir, name + ".png")
                  )
                  plt.clf()
  
              # Write enhanced wave files
              if enh_writer is not None:
                  if istft is not None:
                      enh = istft(enh)
                  else:
                      enh = enh
  
                  if args.keep_length:
                      if len(org_feats[idx]) < len(enh):
                          # Truncate the frames added by stft padding
                          enh = enh[: len(org_feats[idx])]
                      elif len(org_feats) > len(enh):
                          padwidth = [
                              (0, (len(org_feats[idx]) - len(enh)))
                          ] + [(0, 0)] * (enh.ndim - 1)
                          enh = np.pad(enh, padwidth, mode="constant")
  
                  if args.enh_filetype in ("sound", "sound.hdf5"):
                      enh_writer[name] = (args.fs, enh)
                  else:
                      # Hint: To dump stft_signal, mask or etc,
                      # enh_filetype='hdf5' might be convenient.
                      enh_writer[name] = enh
  
              if num_images >= args.num_images and enh_writer is None:
                  logging.info("Breaking the process.")
                  break
  
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 5 for calling a global function with example usage: enhance(args) and returns: itertools