code: def init_weights_truncated_normal(m) -> None:
      """
      Initialise weights with truncated normal.
      Weights that fall outside 2 stds are resampled.
      See torch.nn.init.trunc_normal_ for details.
  
      Args:
          m: weights
  
      Examples:
      >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
      >>> net.apply(init_weights_truncated_normal)
      """
      std = 0.02
      if isinstance(m, nn.Linear):
          torch.nn.init.trunc_normal_(
              m.weight, std=std, a=-2 * std, b=2 * std
          )
          m.bias.data.fill_(0.01)
  
imports: from typing import Optional, Tuple
  import torch
  import torch.nn
  from torch import Tensor
label_desc: Use image node_id 4 for calling a global function with example usage: init_weights_truncated_normal(m) without return types