code: def _init_weights(self):
      # init all parameters.
      bias_value = -math.log((1 - self.prior_prob) / self.prior_prob)
      for p in self.parameters():
          if p.dim() > 1:
              nn.init.xavier_uniform_(p)
  
          # initialize the bias for focal loss and fed loss.
          if self.use_focal_loss or self.use_fed_loss:
              if (
                  p.shape[-1] == self.num_classes
                  or p.shape[-1] == self.num_classes + 1
              ):
                  nn.init.constant_(p, bias_value)
  
imports: import copy
  import math
  import random
  import warnings
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import build_activation_layer
  from mmcv.ops import batched_nms
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox2roi, bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, get_box_wh, scale_boxes
  from mmdet.utils import InstanceList
label_desc: Use image node_id 2 for calling the DynamicDiffusionDetHead obj's underlying member method code with example usage: obj._init_weights() without return types