code: def _get_pred_bbox(self, deltas, proposals, weights):
      pred_proposals = (
          paddle.concat(proposals)
          if len(proposals) > 1
          else proposals[0]
      )
      pred_bbox = delta2bbox(deltas, pred_proposals, weights)
      pred_bbox = paddle.reshape(pred_bbox, [-1, deltas.shape[-1]])
      num_prop = []
      for p in proposals:
          num_prop.append(p.shape[0])
  
      # NOTE(dev): num_prob will be tagged as LoDTensorArray because it
      # depends on batch_size under @to_static. However the argument
      # num_or_sections in paddle.split does not support LoDTensorArray,
      # so we use [-1] to replace it if num_prop is not list. The modification
      # This ensures the correctness of both dynamic and static graphs.
      if not isinstance(num_prop, list):
          num_prop = [-1]
      return pred_bbox.split(num_prop)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Normal
  from ppdet.core.workspace import register
  from .bbox_head import BBoxHead, TwoFCHead, XConvNormHead
  from .roi_extractor import RoIAlign
  from ..shape_spec import ShapeSpec
  from ..bbox_utils import delta2bbox, clip_bbox, nonempty_bbox
  from ..cls_utils import _get_class_default_kwargs
label_desc: Use image node_id 4 for calling the CascadeHead obj's underlying member method code with example usage: obj._get_pred_bbox(deltas, proposals, weights) and returns: pred_bbox