code: def train_one_epoch(
      model,
      criterion,
      optimizer,
      scheduler,
      data_loader,
      decoder,
      language_model,
      device,
      epoch,
      clip_grad,
      disable_logger=False,
      reduce_lr_on_plateau=False,
  ):
      model.train()
  
      metric = MetricLogger("train", disable=disable_logger)
      metric["epoch"] = epoch
  
      for (
          inputs,
          targets,
          tensors_lengths,
          target_lengths,
      ) in data_loader:
          start = time()
          inputs = inputs.to(device, non_blocking=True)
          targets = targets.to(device, non_blocking=True)
  
          # keep batch first for data parallel
          outputs = model(inputs).transpose(-1, -2).transpose(0, 1)
  
          # CTC
          # outputs: input length, batch size, number of classes (including blank)
          # targets: batch size, max target length
          # input_lengths: batch size
          # target_lengths: batch size
  
          loss = criterion(
              outputs, targets, tensors_lengths, target_lengths
          )
  
          optimizer.zero_grad()
          loss.backward()
  
          if clip_grad > 0:
              metric["gradient"] = torch.nn.utils.clip_grad_norm_(
                  model.parameters(), clip_grad
              )
  
          optimizer.step()
  
          compute_error_rates(
              outputs, targets, decoder, language_model, metric
          )
  
          try:
              metric["lr"] = scheduler.get_last_lr()[0]
          except AttributeError:
              metric["lr"] = optimizer.param_groups[0]["lr"]
  
          metric["batch size"] = len(inputs)
          metric["n_channel"] = inputs.shape[1]
          metric["n_time"] = inputs.shape[-1]
          metric["dataset length"] += metric["batch size"]
          metric["iteration"] += 1
          metric["loss"] = loss.item()
          metric["cumulative loss"] += metric["loss"]
          metric["average loss"] = (
              metric["cumulative loss"] / metric["iteration"]
          )
          metric["iteration time"] = time() - start
          metric["epoch time"] += metric["iteration time"]
          metric()
  
      if reduce_lr_on_plateau and isinstance(
          scheduler, ReduceLROnPlateau
      ):
          scheduler.step(metric["average loss"])
      elif not isinstance(scheduler, ReduceLROnPlateau):
          scheduler.step()
  
imports: import argparse
  import logging
  import os
  import string
  from datetime import datetime
  from time import time
  import torch
  import torchaudio
  from ctc_decoders import GreedyDecoder
  from datasets import collate_factory, split_process_librispeech
  from languagemodels import LanguageModel
  from torch.optim import Adadelta, Adam, AdamW, SGD
  from torch.optim.lr_scheduler import ExponentialLR, ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from torchaudio.functional import edit_distance
  from torchaudio.models.wav2letter import Wav2Letter
  from transforms import Normalize, UnsqueezeFirst
  from utils import count_parameters, MetricLogger, save_checkpoint
label_desc: Use image node_id 5 for calling a global function with example usage: train_one_epoch(model, criterion, optimizer, scheduler, data_loader, decoder, language_model, device, epoch, clip_grad, disable_logger, reduce_lr_on_plateau) without return types