code: def initialize_weights(model):
      for m in model.modules():
          t = type(m)
          if t is nn.Conv2d:
              pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
          elif t is nn.BatchNorm2d:
              m.eps = 1e-4
              m.momentum = 0.03
          elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
              m.inplace = True
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/yolov3_spp/build_utils/torch_utils.py
imports: import math
  import time
  from copy import deepcopy
  import torch
  import torch.backends.cudnn
  import torch.nn
label_desc: Use image node_id 3 for calling a global function with example usage: initialize_weights(model) without return types