code: def __call__(
      self, scores, bboxes, scale_factor, ori_shape, masks=None
  ):
      assert (
          len(scores)
          == len(bboxes)
          == len(ori_shape)
          == len(scale_factor)
      )
      device = paddle.device.get_device()
      batch_size = len(ori_shape)
  
      scores = F.sigmoid(scores)
      has_mask = masks is not None
      if has_mask:
          masks = F.sigmoid(masks)
          masks = masks.reshape([batch_size, -1, *masks.shape[1:]])
  
      bbox_pred = []
      mask_pred = [] if has_mask else None
      bbox_num = paddle.zeros([batch_size], dtype="int32")
      for i in range(batch_size):
          score = scores[i]
          bbox = bboxes[i]
          score, indices = score.flatten(0, 1).topk(
              self.num_proposals, sorted=False
          )
          label = indices % self.num_classes
          if has_mask:
              mask = masks[i]
              mask = mask.flatten(0, 1)[indices]
  
          H, W = ori_shape[i][0], ori_shape[i][1]
          bbox = bbox[
              paddle.cast(indices / self.num_classes, indices.dtype)
          ]
          bbox /= scale_factor[i]
          bbox[:, 0::2] = paddle.clip(bbox[:, 0::2], 0, W)
          bbox[:, 1::2] = paddle.clip(bbox[:, 1::2], 0, H)
  
          keep = ((bbox[:, 2] - bbox[:, 0]).numpy() > 1.0) & (
              (bbox[:, 3] - bbox[:, 1]).numpy() > 1.0
          )
          if keep.sum() == 0:
              bbox = paddle.zeros([1, 6], dtype="float32")
              if has_mask:
                  mask = paddle.zeros([1, H, W], dtype="uint8")
          else:
              label = (
                  paddle.to_tensor(label.numpy()[keep])
                  .astype("float32")
                  .unsqueeze(-1)
              )
              score = (
                  paddle.to_tensor(score.numpy()[keep])
                  .astype("float32")
                  .unsqueeze(-1)
              )
              bbox = paddle.to_tensor(bbox.numpy()[keep]).astype(
                  "float32"
              )
              if has_mask:
                  mask = (
                      paddle.to_tensor(mask.numpy()[keep])
                      .astype("float32")
                      .unsqueeze(1)
                  )
                  mask = paste_mask(
                      mask, bbox, H, W, self.assign_on_cpu
                  )
                  mask = paddle.cast(
                      mask >= self.binary_thresh, "uint8"
                  )
              bbox = paddle.concat([label, score, bbox], axis=-1)
  
          bbox_num[i] = bbox.shape[0]
          bbox_pred.append(bbox)
          if has_mask:
              mask_pred.append(mask)
  
      bbox_pred = paddle.concat(bbox_pred)
      mask_pred = paddle.concat(mask_pred) if has_mask else None
  
      if self.assign_on_cpu:
          paddle.set_device(device)
  
      if has_mask:
          return bbox_pred, bbox_num, mask_pred
      else:
          return bbox_pred, bbox_num
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ppdet.modeling.bbox_utils import nonempty_bbox
  from .transformers import bbox_cxcywh_to_xyxy
label_desc: Use image node_id 2 for calling the SparsePostProcess obj's underlying member method code with example usage: obj.__call__(scores, bboxes, scale_factor, ori_shape, masks) and returns: bbox_pred,  bbox_num,  mask_pred, bbox_pred,  bbox_num