code: def weights_init(self, m):
      classname = m.__class__.__name__
      if classname.find("Conv") != -1:
          nn.init.kaiming_normal_(m.weight.data)
      elif classname.find("BatchNorm") != -1:
          m.weight.data.fill_(1.0)
          m.bias.data.fill_(1e-4)
  
file_path: EasyOCR/easyocr/DBNet/decoders/seg_detector_asf.py
imports: from collections import OrderedDict
  import pdb
  import torch
  import torch.nn
  from .feature_attention import ScaleFeatureSelection
label_desc: Use image node_id 2 for calling the SegSpatialScaleDetector obj's underlying member method code with example usage: obj.weights_init(m) without return types