docstr: Train a model with specific config.
  
  Args:
      train_dataset(paddle.io.Dataset) : Dataset to train the model
      epochs(int) : Number of training loops, default is 1.
      batch_size(int) : Batch size of per step, default is 1.
      num_workers(int) : Number of subprocess to load data, default is 0.
      eval_dataset(paddle.io.Dataset) : The validation dataset, deafult is None. If set, the Trainer will
          execute evaluate function every `save_interval` epochs.
      log_interval(int) : Log the train infomation every `log_interval` steps.
      save_interval(int) : Save the checkpoint every `save_interval` epochs.
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code: def train(
      self,
      train_dataset: paddle.io.Dataset,
      epochs: int = 1,
      batch_size: int = 1,
      num_workers: int = 0,
      eval_dataset: paddle.io.Dataset = None,
      log_interval: int = 10,
      save_interval: int = 10,
  ):
      """
      Train a model with specific config.
  
      Args:
          train_dataset(paddle.io.Dataset) : Dataset to train the model
          epochs(int) : Number of training loops, default is 1.
          batch_size(int) : Batch size of per step, default is 1.
          num_workers(int) : Number of subprocess to load data, default is 0.
          eval_dataset(paddle.io.Dataset) : The validation dataset, deafult is None. If set, the Trainer will
              execute evaluate function every `save_interval` epochs.
          log_interval(int) : Log the train infomation every `log_interval` steps.
          save_interval(int) : Save the checkpoint every `save_interval` epochs.
      """
      batch_sampler, loader = self.init_train(
          train_dataset, batch_size, num_workers
      )
      steps_per_epoch = len(batch_sampler)
      timer = Timer(steps_per_epoch * epochs)
      timer.start()
  
      for i in range(epochs):
          loader.dataset.set_epoch(epochs)
          self.current_epoch += 1
          self.train_one_epoch(
              loader,
              timer,
              self.current_epoch,
              epochs,
              log_interval,
              steps_per_epoch,
          )
  
          # todo, why paddlehub put save, eval in batch?
          if (
              self.current_epoch % save_interval == 0
              and self.local_rank == 0
          ):
              if eval_dataset:
                  result = self.evaluate(
                      eval_dataset, batch_size, num_workers
                  )
                  eval_loss = result.get("loss", None)
                  eval_metrics = result.get("metrics", {})
                  if self.use_vdl:
                      if eval_loss:
                          self.log_writer.add_scalar(
                              tag="EVAL/loss",
                              step=timer.current_step,
                              value=eval_loss,
                          )
  
                      for metric, value in eval_metrics.items():
                          self.log_writer.add_scalar(
                              tag="EVAL/{}".format(metric),
                              step=timer.current_step,
                              value=value,
                          )
  
                  if not self.best_metrics or self.compare_metrics(
                      self.best_metrics, eval_metrics
                  ):
                      self.best_metrics = eval_metrics
                      best_model_path = os.path.join(
                          self.checkpoint_dir, "best_model"
                      )
                      self.save_model(best_model_path)
                      self._save_metrics()
  
                      metric_msg = [
                          "{}={:.4f}".format(metric, value)
                          for metric, value in self.best_metrics.items()
                      ]
                      metric_msg = " ".join(metric_msg)
                      logger.eval(
                          "Saving best model to {} [best {}]".format(
                              best_model_path, metric_msg
                          )
                      )
  
              self._save_checkpoint()
  
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 5 for calling the CustomTrainer obj's underlying member method code with example usage: obj.train(train_dataset, epochs, batch_size, num_workers, eval_dataset, log_interval, save_interval) without return types