          ----------
          bounding_boxes_scales: list
              The output predictions from the S3FD model
          """
          ret = list()
          batch_size = range(bounding_boxes_scales[0].shape[0])
          for img in batch_size:
              bboxlist = [scale[img:img+1] for scale in bounding_boxes_scales]
              boxes = self._post_process(bboxlist)
              bboxlist = self._nms(boxes, 0.5)
 
</s> remove                 ret.append(list())
 </s> add                 ret.append([]) </s> remove         name = "conv{}".format(idx)
 </s> add         name = f"conv{idx}" </s> remove         ret_boxes = list()
        ret_points = list()
 </s> add         ret_boxes = []
        ret_points = [] </s> remove                 ret.append(list())
 </s> add                 ret.append([]) </s> remove         name = "conv{}".format(idx)
 </s> add         name = f"conv{idx}"
        ret = []
          ----------
          bounding_boxes_scales: list
              The output predictions from the S3FD model
          """
        ret = []
          batch_size = range(bounding_boxes_scales[0].shape[0])
          for img in batch_size:
              bboxlist = [scale[img:img+1] for scale in bounding_boxes_scales]
              boxes = self._post_process(bboxlist)
              bboxlist = self._nms(boxes, 0.5)
keep keep keep keep replace keep keep keep keep keep
 Update all Keras Imports to be conditional (#1214)

* Remove custom keras importer

* first round keras imports fix

* launcher.py: Remove KerasFinder references

* 2nd round keras imports update (lib and extract)

* 3rd round keras imports update (train)

* remove KerasFinder from tests

* 4th round keras imports update (tests) 
plugins/extract/detect/s3fd.py