code: def _eval(self, epoch):
      self.model.eval()
      raw_metrics = []
      total_frame = 0.0
      total_time = 0.0
      for i, batch in tqdm(
          enumerate(self.validate_loader),
          total=len(self.validate_loader),
          desc="test model",
      ):
          with paddle.no_grad():
              start = time.time()
              if self.amp:
                  with paddle.amp.auto_cast(
                      enable="gpu" in paddle.device.get_device(),
                      custom_white_list=self.amp.get(
                          "custom_white_list", []
                      ),
                      custom_black_list=self.amp.get(
                          "custom_black_list", []
                      ),
                      level=self.amp.get("level", "O2"),
                  ):
                      preds = self.model(batch["img"])
                  preds = preds.astype(paddle.float32)
              else:
                  preds = self.model(batch["img"])
              boxes, scores = self.post_process(
                  batch,
                  preds,
                  is_output_polygon=self.metric_cls.is_output_polygon,
              )
              total_frame += batch["img"].shape[0]
              total_time += time.time() - start
              raw_metric = self.metric_cls.validate_measure(
                  batch, (boxes, scores)
              )
              raw_metrics.append(raw_metric)
      metrics = self.metric_cls.gather_measure(raw_metrics)
      self.logger_info("FPS:{}".format(total_frame / total_time))
      return (
          metrics["recall"].avg,
          metrics["precision"].avg,
          metrics["fmeasure"].avg,
      )
  
file_path: PaddleOCR/benchmark/PaddleOCR_DBNet/trainer/trainer.py
imports: import time
  import paddle
  from tqdm import tqdm
  from base import BaseTrainer
  from utils import runningScore, cal_text_score, Polynomial, profiler
label_desc: Use image node_id 3 for calling the Trainer obj's underlying member method code with example usage: obj._eval(epoch) and returns: metrics,  metrics,  metrics