code: def init_net_weights(
      model: nn.Module,
      init_std: float = 0.01,
      style: str = "resnet",
  ) -> None:
      """
      Performs weight initialization. Options include ResNet style weight initialization
      and transformer style weight initialization.
  
      Args:
          model (nn.Module): Model to be initialized.
          init_std (float): The expected standard deviation for initialization.
          style (str): Options include "resnet" and "vit".
      """
      assert style in ["resnet", "vit"]
      if style == "resnet":
          return _init_resnet_weights(model, init_std)
      elif style == "vit":
          return _init_vit_weights(model, init_std)
      else:
          raise NotImplementedError
  
imports: import torch.nn
  from fvcore.nn.weight_init import c2_msra_fill, c2_xavier_fill
  from pytorchvideo.layers import SpatioTemporalClsPositionalEncoding
label_desc: Use image node_id 3 for calling a global function with example usage: init_net_weights(model, init_std, style) and returns: _init_resnet_weights, _init_vit_weights