code: def init_weights(m):
      """Performs ResNet-style weight initialization."""
      if isinstance(m, nn.Conv2d):
          # Note that there is no bias due to BN
          fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
          m.weight.data.normal_(mean=0.0, std=np.sqrt(2.0 / fan_out))
      elif isinstance(m, nn.BatchNorm2d):
          zero_init_gamma = cfg.BN.ZERO_INIT_FINAL_GAMMA
          zero_init_gamma = (
              hasattr(m, "final_bn") and m.final_bn and zero_init_gamma
          )
          m.weight.data.fill_(0.0 if zero_init_gamma else 1.0)
          m.bias.data.zero_()
      elif isinstance(m, nn.Linear):
          m.weight.data.normal_(mean=0.0, std=0.01)
          m.bias.data.zero_()
  
imports: import numpy
  import torch
  import torch.nn
  from pycls.core.config import cfg
  from torch.nn import Module
label_desc: Use image node_id 17 for calling a global function with example usage: init_weights(m) without return types