code: def train(args):
      """Train with the given args.
  
      Args:
          args (namespace): The program arguments.
  
      """
      # display chainer version
      logging.info("chainer version = " + chainer.__version__)
  
      set_deterministic_chainer(args)
  
      # check cuda and cudnn availability
      if not chainer.cuda.available:
          logging.warning("cuda is not available")
      if not chainer.cuda.cudnn_enabled:
          logging.warning("cudnn is not available")
  
      # get input and output dimension info
      with open(args.valid_json, "rb") as f:
          valid_json = json.load(f)["utts"]
      utts = list(valid_json.keys())
      idim = int(valid_json[utts[0]]["input"][0]["shape"][1])
      odim = int(valid_json[utts[0]]["output"][0]["shape"][1])
      logging.info("#input dims : " + str(idim))
      logging.info("#output dims: " + str(odim))
  
      # specify attention, CTC, hybrid mode
      if args.mtlalpha == 1.0:
          mtl_mode = "ctc"
          logging.info("Pure CTC mode")
      elif args.mtlalpha == 0.0:
          mtl_mode = "att"
          logging.info("Pure attention mode")
      else:
          mtl_mode = "mtl"
          logging.info("Multitask learning mode")
  
      # specify model architecture
      logging.info("import model module: " + args.model_module)
      model_class = dynamic_import(args.model_module)
      model = model_class(idim, odim, args, flag_return=False)
      assert isinstance(model, ASRInterface)
      total_subsampling_factor = model.get_total_subsampling_factor()
  
      # write model config
      if not os.path.exists(args.outdir):
          os.makedirs(args.outdir)
      model_conf = args.outdir + "/model.json"
      with open(model_conf, "wb") as f:
          logging.info("writing a model config file to " + model_conf)
          f.write(
              json.dumps(
                  (idim, odim, vars(args)),
                  indent=4,
                  ensure_ascii=False,
                  sort_keys=True,
              ).encode("utf_8")
          )
      for key in sorted(vars(args).keys()):
          logging.info("ARGS: " + key + ": " + str(vars(args)[key]))
  
      # Set gpu
      ngpu = args.ngpu
      if ngpu == 1:
          gpu_id = 0
          # Make a specified GPU current
          chainer.cuda.get_device_from_id(gpu_id).use()
          model.to_gpu()  # Copy the model to the GPU
          logging.info("single gpu calculation.")
      elif ngpu > 1:
          gpu_id = 0
          devices = {"main": gpu_id}
          for gid in range(1, ngpu):
              devices["sub_%d" % gid] = gid
          logging.info("multi gpu calculation (#gpus = %d)." % ngpu)
          logging.warning(
              "batch size is automatically increased (%d -> %d)"
              % (args.batch_size, args.batch_size * args.ngpu)
          )
      else:
          gpu_id = -1
          logging.info("cpu calculation")
  
      # Setup an optimizer
      if args.opt == "adadelta":
          optimizer = chainer.optimizers.AdaDelta(eps=args.eps)
      elif args.opt == "adam":
          optimizer = chainer.optimizers.Adam()
      elif args.opt == "noam":
          optimizer = chainer.optimizers.Adam(
              alpha=0, beta1=0.9, beta2=0.98, eps=1e-9
          )
      else:
          raise NotImplementedError("args.opt={}".format(args.opt))
  
      optimizer.setup(model)
      optimizer.add_hook(
          chainer.optimizer.GradientClipping(args.grad_clip)
      )
  
      # Setup a converter
      converter = model.custom_converter(
          subsampling_factor=model.subsample[0]
      )
  
      # read json data
      with open(args.train_json, "rb") as f:
          train_json = json.load(f)["utts"]
      with open(args.valid_json, "rb") as f:
          valid_json = json.load(f)["utts"]
  
      # set up training iterator and updater
      load_tr = LoadInputsAndTargets(
          mode="asr",
          load_output=True,
          preprocess_conf=args.preprocess_conf,
          preprocess_args={
              "train": True
          },  # Switch the mode of preprocessing
      )
      load_cv = LoadInputsAndTargets(
          mode="asr",
          load_output=True,
          preprocess_conf=args.preprocess_conf,
          preprocess_args={
              "train": False
          },  # Switch the mode of preprocessing
      )
  
      use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
      accum_grad = args.accum_grad
      if ngpu <= 1:
          # make minibatch list (variable length)
          train = make_batchset(
              train_json,
              args.batch_size,
              args.maxlen_in,
              args.maxlen_out,
              args.minibatches,
              min_batch_size=args.ngpu if args.ngpu > 1 else 1,
              shortest_first=use_sortagrad,
              count=args.batch_count,
              batch_bins=args.batch_bins,
              batch_frames_in=args.batch_frames_in,
              batch_frames_out=args.batch_frames_out,
              batch_frames_inout=args.batch_frames_inout,
              iaxis=0,
              oaxis=0,
          )
          # hack to make batchsize argument as 1
          # actual batchsize is included in a list
          if args.n_iter_processes > 0:
              train_iters = [
                  ToggleableShufflingMultiprocessIterator(
                      TransformDataset(train, load_tr),
                      batch_size=1,
                      n_processes=args.n_iter_processes,
                      n_prefetch=8,
                      maxtasksperchild=20,
                      shuffle=not use_sortagrad,
                  )
              ]
          else:
              train_iters = [
                  ToggleableShufflingSerialIterator(
                      TransformDataset(train, load_tr),
                      batch_size=1,
                      shuffle=not use_sortagrad,
                  )
              ]
  
          # set up updater
          updater = model.custom_updater(
              train_iters[0],
              optimizer,
              converter=converter,
              device=gpu_id,
              accum_grad=accum_grad,
          )
      else:
          if (
              args.batch_count not in ("auto", "seq")
              and args.batch_size == 0
          ):
              raise NotImplementedError(
                  "--batch-count 'bin' and 'frame' are not implemented "
                  "in chainer multi gpu"
              )
          # set up minibatches
          train_subsets = []
          for gid in range(ngpu):
              # make subset
              train_json_subset = {
                  k: v
                  for i, (k, v) in enumerate(train_json.items())
                  if i % ngpu == gid
              }
              # make minibatch list (variable length)
              train_subsets += [
                  make_batchset(
                      train_json_subset,
                      args.batch_size,
                      args.maxlen_in,
                      args.maxlen_out,
                      args.minibatches,
                  )
              ]
  
          # each subset must have same length for MultiprocessParallelUpdater
          maxlen = max(
              [len(train_subset) for train_subset in train_subsets]
          )
          for train_subset in train_subsets:
              if maxlen != len(train_subset):
                  for i in range(maxlen - len(train_subset)):
                      train_subset += [train_subset[i]]
  
          # hack to make batchsize argument as 1
          # actual batchsize is included in a list
          if args.n_iter_processes > 0:
              train_iters = [
                  ToggleableShufflingMultiprocessIterator(
                      TransformDataset(train_subsets[gid], load_tr),
                      batch_size=1,
                      n_processes=args.n_iter_processes,
                      n_prefetch=8,
                      maxtasksperchild=20,
                      shuffle=not use_sortagrad,
                  )
                  for gid in range(ngpu)
              ]
          else:
              train_iters = [
                  ToggleableShufflingSerialIterator(
                      TransformDataset(train_subsets[gid], load_tr),
                      batch_size=1,
                      shuffle=not use_sortagrad,
                  )
                  for gid in range(ngpu)
              ]
  
          # set up updater
          updater = model.custom_parallel_updater(
              train_iters,
              optimizer,
              converter=converter,
              devices=devices,
          )
  
      # Set up a trainer
      trainer = training.Trainer(
          updater, (args.epochs, "epoch"), out=args.outdir
      )
  
      if use_sortagrad:
          trainer.extend(
              ShufflingEnabler(train_iters),
              trigger=(
                  args.sortagrad
                  if args.sortagrad != -1
                  else args.epochs,
                  "epoch",
              ),
          )
      if args.opt == "noam":
          from espnet.nets.chainer_backend.transformer.training import (
              VaswaniRule,
          )
  
          trainer.extend(
              VaswaniRule(
                  "alpha",
                  d=args.adim,
                  warmup_steps=args.transformer_warmup_steps,
                  scale=args.transformer_lr,
              ),
              trigger=(1, "iteration"),
          )
      # Resume from a snapshot
      if args.resume:
          chainer.serializers.load_npz(args.resume, trainer)
  
      # set up validation iterator
      valid = make_batchset(
          valid_json,
          args.batch_size,
          args.maxlen_in,
          args.maxlen_out,
          args.minibatches,
          min_batch_size=args.ngpu if args.ngpu > 1 else 1,
          count=args.batch_count,
          batch_bins=args.batch_bins,
          batch_frames_in=args.batch_frames_in,
          batch_frames_out=args.batch_frames_out,
          batch_frames_inout=args.batch_frames_inout,
          iaxis=0,
          oaxis=0,
      )
  
      if args.n_iter_processes > 0:
          valid_iter = chainer.iterators.MultiprocessIterator(
              TransformDataset(valid, load_cv),
              batch_size=1,
              repeat=False,
              shuffle=False,
              n_processes=args.n_iter_processes,
              n_prefetch=8,
              maxtasksperchild=20,
          )
      else:
          valid_iter = chainer.iterators.SerialIterator(
              TransformDataset(valid, load_cv),
              batch_size=1,
              repeat=False,
              shuffle=False,
          )
  
      # Evaluate the model with the test dataset for each epoch
      trainer.extend(
          BaseEvaluator(
              valid_iter, model, converter=converter, device=gpu_id
          )
      )
  
      # Save attention weight each epoch
      if args.num_save_attention > 0 and args.mtlalpha != 1.0:
          data = sorted(
              list(valid_json.items())[: args.num_save_attention],
              key=lambda x: int(x[1]["input"][0]["shape"][1]),
              reverse=True,
          )
          if hasattr(model, "module"):
              att_vis_fn = model.module.calculate_all_attentions
              plot_class = model.module.attention_plot_class
          else:
              att_vis_fn = model.calculate_all_attentions
              plot_class = model.attention_plot_class
          logging.info("Using custom PlotAttentionReport")
          att_reporter = plot_class(
              att_vis_fn,
              data,
              args.outdir + "/att_ws",
              converter=converter,
              transform=load_cv,
              device=gpu_id,
              subsampling_factor=total_subsampling_factor,
          )
          trainer.extend(att_reporter, trigger=(1, "epoch"))
      else:
          att_reporter = None
  
      # Take a snapshot for each specified epoch
      trainer.extend(
          extensions.snapshot(filename="snapshot.ep.{.updater.epoch}"),
          trigger=(1, "epoch"),
      )
  
      # Make a plot for training and validation values
      trainer.extend(
          extensions.PlotReport(
              [
                  "main/loss",
                  "validation/main/loss",
                  "main/loss_ctc",
                  "validation/main/loss_ctc",
                  "main/loss_att",
                  "validation/main/loss_att",
              ],
              "epoch",
              file_name="loss.png",
          )
      )
      trainer.extend(
          extensions.PlotReport(
              ["main/acc", "validation/main/acc"],
              "epoch",
              file_name="acc.png",
          )
      )
  
      # Save best models
      trainer.extend(
          extensions.snapshot_object(model, "model.loss.best"),
          trigger=training.triggers.MinValueTrigger(
              "validation/main/loss"
          ),
      )
      if mtl_mode != "ctc":
          trainer.extend(
              extensions.snapshot_object(model, "model.acc.best"),
              trigger=training.triggers.MaxValueTrigger(
                  "validation/main/acc"
              ),
          )
  
      # epsilon decay in the optimizer
      if args.opt == "adadelta":
          if args.criterion == "acc" and mtl_mode != "ctc":
              trainer.extend(
                  restore_snapshot(
                      model, args.outdir + "/model.acc.best"
                  ),
                  trigger=CompareValueTrigger(
                      "validation/main/acc",
                      lambda best_value, current_value: best_value
                      > current_value,
                  ),
              )
              trainer.extend(
                  adadelta_eps_decay(args.eps_decay),
                  trigger=CompareValueTrigger(
                      "validation/main/acc",
                      lambda best_value, current_value: best_value
                      > current_value,
                  ),
              )
          elif args.criterion == "loss":
              trainer.extend(
                  restore_snapshot(
                      model, args.outdir + "/model.loss.best"
                  ),
                  trigger=CompareValueTrigger(
                      "validation/main/loss",
                      lambda best_value, current_value: best_value
                      < current_value,
                  ),
              )
              trainer.extend(
                  adadelta_eps_decay(args.eps_decay),
                  trigger=CompareValueTrigger(
                      "validation/main/loss",
                      lambda best_value, current_value: best_value
                      < current_value,
                  ),
              )
  
      # Write a log of evaluation statistics for each epoch
      trainer.extend(
          extensions.LogReport(
              trigger=(args.report_interval_iters, "iteration")
          )
      )
      report_keys = [
          "epoch",
          "iteration",
          "main/loss",
          "main/loss_ctc",
          "main/loss_att",
          "validation/main/loss",
          "validation/main/loss_ctc",
          "validation/main/loss_att",
          "main/acc",
          "validation/main/acc",
          "elapsed_time",
      ]
      if args.opt == "adadelta":
          trainer.extend(
              extensions.observe_value(
                  "eps",
                  lambda trainer: trainer.updater.get_optimizer(
                      "main"
                  ).eps,
              ),
              trigger=(args.report_interval_iters, "iteration"),
          )
          report_keys.append("eps")
      trainer.extend(
          extensions.PrintReport(report_keys),
          trigger=(args.report_interval_iters, "iteration"),
      )
  
      trainer.extend(
          extensions.ProgressBar(
              update_interval=args.report_interval_iters
          )
      )
  
      set_early_stop(trainer, args)
      if (
          args.tensorboard_dir is not None
          and args.tensorboard_dir != ""
      ):
          try:
              from tensorboardX import SummaryWriter
          except Exception:
              logging.error("Please install tensorboardx")
              raise
          writer = SummaryWriter(args.tensorboard_dir)
          trainer.extend(
              TensorboardLogger(writer, att_reporter),
              trigger=(args.report_interval_iters, "iteration"),
          )
  
      # Run the training
      trainer.run()
      check_early_stop(trainer, args.epochs)
  
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 1 for calling a global function with example usage: train(args) without return types