code: def train_one_epoch(
      self,
      loader: paddle.io.DataLoader,
      timer: Timer,
      current_epoch: int,
      epochs: int,
      log_interval: int,
      steps_per_epoch: int,
  ) -> None:
      avg_loss = 0
      avg_metrics = defaultdict(int)
      self.model.train()
  
      for batch_idx, batch in enumerate(loader):
          loss, metrics = self.training_step(batch, batch_idx)
          self.optimizer_step(
              current_epoch, batch_idx, self.optimizer, loss
          )
          self.optimizer_zero_grad(
              current_epoch, batch_idx, self.optimizer
          )
  
          # calculate metrics and loss
          avg_loss += float(loss)
          for metric, value in metrics.items():
              avg_metrics[metric] += float(value)
  
          timer.count()
  
          if (
              batch_idx + 1
          ) % log_interval == 0 and self.local_rank == 0:
              lr = self.optimizer.get_lr()
              avg_loss /= log_interval
              if self.use_vdl:
                  self.log_writer.add_scalar(
                      tag="TRAIN/loss",
                      step=timer.current_step,
                      value=avg_loss,
                  )
  
              print_msg = "Epoch={}/{}, Step={}/{}".format(
                  current_epoch, epochs, batch_idx + 1, steps_per_epoch
              )
              print_msg += " loss={:.4f}".format(avg_loss)
  
              for metric, value in avg_metrics.items():
                  value /= log_interval
                  if self.use_vdl:
                      self.log_writer.add_scalar(
                          tag="TRAIN/{}".format(metric),
                          step=timer.current_step,
                          value=value,
                      )
                  print_msg += " {}={:.4f}".format(metric, value)
  
              print_msg += " lr={:.6f} step/sec={:.2f} | ETA {}".format(
                  lr, timer.timing, timer.eta
              )
  
              logger.train(print_msg)
  
              avg_loss = 0
              avg_metrics = defaultdict(int)
  
file_path: PaddleHub/demo/autoaug/paddlehub_utils/trainer.py
imports: import os
  from collections import defaultdict
  import paddle
  from paddle.distributed import ParallelEnv
  from paddlehub.finetune.trainer import Trainer
  from paddlehub.utils.log import logger
  from paddlehub.utils.utils import Timer
label_desc: Use image node_id 4 for calling the CustomTrainer obj's underlying member method code with example usage: obj.train_one_epoch(loader, timer, current_epoch, epochs, log_interval, steps_per_epoch) without return types