code: def evaluate_process(self, loader: paddle.io.DataLoader) -> dict:
      self.model.eval()
      avg_loss = num_samples = 0
      sum_metrics = defaultdict(int)
      avg_metrics = defaultdict(int)
  
      for batch_idx, batch in enumerate(loader):
          result = self.validation_step(batch, batch_idx)
          loss = result.get("loss", None)
          metrics = result.get("metrics", {})
          bs = batch[0].shape[0]
          num_samples += bs
  
          if loss:
              avg_loss += float(loss) * bs
  
          for metric, value in metrics.items():
              sum_metrics[metric] += float(value) * bs
  
      # print avg metrics and loss
      print_msg = "[Evaluation result]"
      if loss:
          avg_loss /= num_samples
          print_msg += " avg_loss={:.4f}".format(avg_loss)
  
      for metric, value in sum_metrics.items():
          avg_metrics[metric] = value / num_samples
          print_msg += " avg_{}={:.4f}".format(
              metric, avg_metrics[metric]
          )
  
      logger.eval(print_msg)
  
      if loss:
          return {"loss": avg_loss, "metrics": avg_metrics}
      return {"metrics": avg_metrics}
  
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 7 for calling the CustomTrainer obj's underlying member method code with example usage: obj.evaluate_process(loader) and returns: dict, dict