class_docstr: Head of DDQDETR: Dense Distinct Query for
      End-to-End Object Detection.
  
  Code is modified from the `official github repo
      <https://github.com/jshilong/DDQ>`_.
  
  More details can be found in the `paper
      <https://arxiv.org/abs/2303.12776>`_ .
  
  Args:
      aux_num_pos (int): Number of positive targets assigned to a
          perdicted object. Defaults to 4.
docstr: Initialize weights of the Deformable DETR head.
docstr_tok: ["Initialize","weights","of","the","Deformable","DETR","head","."]
code: def init_weights(self) -> None:
      """Initialize weights of the Deformable DETR head."""
      bias_init = bias_init_with_prob(0.01)
      for m in self.cls_branches:
          nn.init.constant_(m.bias, bias_init)
      for m in self.aux_cls_branches:
          nn.init.constant_(m.bias, bias_init)
      for m in self.reg_branches:
          constant_init(m[-1], 0, bias=0)
      for m in self.reg_branches:
          nn.init.constant_(m[-1].bias.data[2:], 0.0)
  
      for m in self.aux_reg_branches:
          constant_init(m[-1], 0, bias=0)
  
      for m in self.aux_reg_branches:
          nn.init.constant_(m[-1].bias.data[2:], 0.0)
  
imports: import copy
  from typing import Dict, List, Tuple
  import torch
  from mmengine.model import bias_init_with_prob, constant_init
  from torch import Tensor, nn
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox_cxcywh_to_xyxy
  from mmdet.utils import InstanceList, OptInstanceList, reduce_mean
  from ..layers import inverse_sigmoid
  from ..losses import DDQAuxLoss
  from ..utils import multi_apply
  from .dino_head import DINOHead
label_desc: Use image node_id 3 for calling the DDQDETRHead obj's underlying member method code with example usage: obj.init_weights() without return types