code: def _initialize_weights(self, zero_init_bn_residuals):
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
          elif isinstance(m, nn.BatchNorm2d):
              nn.init.constant_(m.weight, 1)
              nn.init.constant_(m.bias, 0)
  
      # Init BatchNorm gamma to 0.0 for last BN layer, it gets 0.2-0.3% higher
      # final val top1 for larger batch sizes.
      if zero_init_bn_residuals:
          for m in self.modules():
              if isinstance(m, GenericLayer):
                  if hasattr(m, "bn"):
                      nn.init.constant_(m.bn.weight, 0)
  
imports: import copy
  import math
  import re
  import warnings
  from collections import OrderedDict
  from typing import Any, Dict, Optional, Sequence
  import torch.nn
  from classy_vision.generic.util import is_pos_int, is_pos_int_tuple
  from .None import register_model
  from .classy_model import ClassyModel
  from .squeeze_and_excitation_layer import SqueezeAndExcitationLayer
label_desc: Use image node_id 2 for calling the ResNeXt obj's underlying member method code with example usage: obj._initialize_weights(zero_init_bn_residuals) without return types