code: def _init_weights(self):
      for m in self.inst_convs.modules():
          if isinstance(m, nn.Conv2d):
              kaiming_init(m)
      bias_value = -math.log((1 - self.prior_prob) / self.prior_prob)
      for module in [self.iam_conv, self.cls_score]:
          init.constant_(module.bias, bias_value)
      init.normal_(self.iam_conv.weight, std=0.01)
      init.normal_(self.cls_score.weight, std=0.01)
  
      init.normal_(self.mask_kernel.weight, std=0.01)
      init.constant_(self.mask_kernel.bias, 0.0)
      caffe2_xavier_init(self.fc)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.model.weight_init import caffe2_xavier_init, kaiming_init
  from torch.nn import init
  from mmdet.registry import MODELS
label_desc: Use image node_id 2 for calling the GroupInstanceBranch obj's underlying member method code with example usage: obj._init_weights() without return types