code: def __init__(
      self,
      head,
      in_channel,
      roi_extractor=_get_class_default_kwargs(RoIAlign),
      bbox_assigner="BboxAssigner",
      with_pool=False,
      num_classes=80,
      bbox_weight=[10.0, 10.0, 5.0, 5.0],
      bbox_loss=None,
      loss_normalize_pos=False,
      cot_classes=None,
      loss_cot="COTLoss",
      use_cot=False,
  ):
      super(BBoxHead, self).__init__()
      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.with_pool = with_pool
      self.num_classes = num_classes
      self.bbox_weight = bbox_weight
      self.bbox_loss = bbox_loss
      self.loss_normalize_pos = loss_normalize_pos
  
      self.loss_cot = loss_cot
      self.cot_relation = None
      self.cot_classes = cot_classes
      self.use_cot = use_cot
      if use_cot:
          self.cot_bbox_score = nn.Linear(
              in_channel,
              self.num_classes + 1,
              weight_attr=paddle.ParamAttr(
                  initializer=Normal(mean=0.0, std=0.01)
              ),
          )
  
          self.bbox_score = nn.Linear(
              in_channel,
              self.cot_classes + 1,
              weight_attr=paddle.ParamAttr(
                  initializer=Normal(mean=0.0, std=0.01)
              ),
          )
          self.cot_bbox_score.skip_quant = True
      else:
          self.bbox_score = nn.Linear(
              in_channel,
              self.num_classes + 1,
              weight_attr=paddle.ParamAttr(
                  initializer=Normal(mean=0.0, std=0.01)
              ),
          )
      self.bbox_score.skip_quant = True
  
      self.bbox_delta = nn.Linear(
          in_channel,
          4 * self.num_classes,
          weight_attr=paddle.ParamAttr(
              initializer=Normal(mean=0.0, std=0.001)
          ),
      )
      self.bbox_delta.skip_quant = True
      self.assigned_label = None
      self.assigned_rois = None
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Normal, XavierUniform, KaimingNormal
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register, create
  from .roi_extractor import RoIAlign
  from ..shape_spec import ShapeSpec
  from ..bbox_utils import bbox2delta
  from ..cls_utils import _get_class_default_kwargs
  from ppdet.modeling.layers import ConvNormLayer
label_desc: Use image node_id 1 to create a new BBoxHead object from inherited base classes: nn with example: obj = BBoxHead(head, in_channel, roi_extractor, bbox_assigner, with_pool, num_classes, bbox_weight, bbox_loss, loss_normalize_pos, cot_classes, loss_cot, use_cot)