code: def predict(
      self,
      image_list,
      aug_pred=False,
      scales=1.0,
      flip_horizontal=True,
      flip_vertical=False,
      is_slide=False,
      stride=None,
      crop_size=None,
  ):
      results = []
      nranks = paddle.distributed.get_world_size()
      local_rank = paddle.distributed.get_rank()
      if nranks > 1:
          img_lists = partition_list(image_list, nranks)
      else:
          img_lists = [image_list]
  
      logger.info("Start to predict...")
      progbar_pred = progbar.Progbar(
          target=len(img_lists[0]), verbose=1
      )
      with paddle.no_grad():
          for i, im_data in enumerate(img_lists[local_rank]):
              data = self.preprocess(im_data)
              if aug_pred:
                  pred, _ = infer.aug_inference(
                      self.model,
                      data["img"],
                      trans_info=data["trans_info"],
                      scales=scales,
                      flip_horizontal=flip_horizontal,
                      flip_vertical=flip_vertical,
                      is_slide=is_slide,
                      stride=stride,
                      crop_size=crop_size,
                  )
              else:
                  pred, _ = infer.inference(
                      self.model,
                      data["img"],
                      trans_info=data["trans_info"],
                      is_slide=is_slide,
                      stride=stride,
                      crop_size=crop_size,
                  )
              pred = paddle.squeeze(pred)
              pred = pred.numpy().astype("uint8")
  
              if isinstance(im_data, dict):
                  result = self.roi_postprocess(pred, im_data)
              else:
                  result = self.postprocess(pred, im_data)
              results.extend(result)
              progbar_pred.update(i + 1)
  
      return results
  
file_path: PaddleSeg/contrib/QualityInspector/qinspector/seg/engine/predictor.py
imports: import numpy
  import cv2
  import paddle
  from paddleseg import utils
  from paddleseg.core import infer
  from paddleseg.core.predict import partition_list
  from paddleseg.transforms import Compose
  from paddleseg.utils import progbar
  from qinspector.utils.logger import setup_logger
label_desc: Use image node_id 5 for calling the SegPredictor obj's underlying member method code with example usage: obj.predict(image_list, aug_pred, scales, flip_horizontal, flip_vertical, is_slide, stride, crop_size) and returns: results