class_docstr: The neck of YOLOV3.
  
  It can be treated as a simplified version of FPN. It
  will take the result from Darknet backbone and do some upsampling and
  concatenation. It will finally output the detection result.
  
  Note:
      The input feats should be from top to bottom.
          i.e., from high-lvl to low-lvl
      But YOLOV3Neck will process them in reversed order.
          i.e., from bottom (high-lvl) to top (low-lvl)
  
  Args:
      num_scales (int): The number of scales / stages.
      in_channels (int): The number of input channels.
      out_channels (int): The number of output channels.
      conv_cfg (dict): Config dict for convolution layer. Default: None.
      norm_cfg (dict): Dictionary to construct and config norm layer.
          Default: dict(type='BN', requires_grad=True)
      act_cfg (dict): Config dict for activation layer.
          Default: dict(type='LeakyReLU', negative_slope=0.1).
docstr: Initialize the weights of module.
docstr_tok: ["Initialize","the","weights","of","module","."]
code: def init_weights(self):
      """Initialize the weights of module."""
      # init is done in ConvModule
      pass
  
file_path: insightface/detection/scrfd/mmdet/models/necks/yolo_neck.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from ..builder import NECKS
label_desc: Use image node_id 3 for calling the YOLOV3Neck obj's underlying member method code with example usage: obj.init_weights() without return types