class_docstr: Simplest RoI head, with only two fc layers for classification and
  regression respectively.
class_docstr_tok: ["Simplest","RoI","head",",","with","only","two","fc","layers","for","classification","and","regression","respectively","."]
code: def init_weights(self):
      # conv layers are already initialized by ConvModule
      if self.with_cls:
          nn.init.normal_(self.fc_cls.weight, 0, 0.01)
          nn.init.constant_(self.fc_cls.bias, 0)
      if self.with_reg:
          nn.init.normal_(self.fc_reg.weight, 0, 0.001)
          nn.init.constant_(self.fc_reg.bias, 0)
  
file_path: insightface/detection/scrfd/mmdet/models/roi_heads/bbox_heads/bbox_head.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.runner import auto_fp16, force_fp32
  from torch.nn.modules.utils import _pair
  from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
  from mmdet.models.builder import HEADS, build_loss
  from mmdet.models.losses import accuracy
label_desc: Use image node_id 2 for calling the BBoxHead obj's underlying member method code with example usage: obj.init_weights() without return types