code: def __init__(
      self,
      head,
      in_channel,
      roi_extractor=_get_class_default_kwargs(RoIAlign),
      bbox_assigner="BboxAssigner",
      num_classes=80,
      bbox_weight=[
          [10.0, 10.0, 5.0, 5.0],
          [20.0, 20.0, 10.0, 10.0],
          [30.0, 30.0, 15.0, 15.0],
      ],
      num_cascade_stages=3,
      bbox_loss=None,
      reg_class_agnostic=True,
      stage_loss_weights=None,
      loss_normalize_pos=False,
      add_gt_as_proposals=[True, False, False],
  ):
      nn.Layer.__init__(
          self,
      )
      self.head = head
      self.roi_extractor = roi_extractor
      if isinstance(roi_extractor, dict):
          self.roi_extractor = RoIAlign(**roi_extractor)
      self.bbox_assigner = bbox_assigner
  
      self.num_classes = num_classes
      self.bbox_weight = bbox_weight
      self.num_cascade_stages = num_cascade_stages
      self.bbox_loss = bbox_loss
      self.stage_loss_weights = (
          [1.0 / num_cascade_stages for _ in range(num_cascade_stages)]
          if stage_loss_weights is None
          else stage_loss_weights
      )
      self.add_gt_as_proposals = add_gt_as_proposals
  
      assert (
          len(self.stage_loss_weights) == num_cascade_stages
      ), f"stage_loss_weights({len(self.stage_loss_weights)}) do not equal to num_cascade_stages({num_cascade_stages})"
  
      self.reg_class_agnostic = reg_class_agnostic
      num_bbox_delta = 4 if reg_class_agnostic else 4 * num_classes
      self.loss_normalize_pos = loss_normalize_pos
  
      self.bbox_score_list = []
      self.bbox_delta_list = []
      for i in range(num_cascade_stages):
          score_name = "bbox_score_stage{}".format(i)
          delta_name = "bbox_delta_stage{}".format(i)
          bbox_score = self.add_sublayer(
              score_name,
              nn.Linear(
                  in_channel,
                  self.num_classes + 1,
                  weight_attr=paddle.ParamAttr(
                      initializer=Normal(mean=0.0, std=0.01)
                  ),
              ),
          )
  
          bbox_delta = self.add_sublayer(
              delta_name,
              nn.Linear(
                  in_channel,
                  num_bbox_delta,
                  weight_attr=paddle.ParamAttr(
                      initializer=Normal(mean=0.0, std=0.001)
                  ),
              ),
          )
          self.bbox_score_list.append(bbox_score)
          self.bbox_delta_list.append(bbox_delta)
      self.assigned_label = None
      self.assigned_rois = None
  
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 1 to create a new CascadeHead object from inherited base classes: BBoxHead with example: obj = CascadeHead(head, in_channel, roi_extractor, bbox_assigner, num_classes, bbox_weight, num_cascade_stages, bbox_loss, reg_class_agnostic, stage_loss_weights, loss_normalize_pos, add_gt_as_proposals)