code: def _init_weights(module):
      """
      ### Initialize weights
  
      Weights of linear layers and embedding layers are initialized
      to $\mathcal{N}(0, 0.02)$
      instead of the default Xavier initialzation.
      """
  
      if not isinstance(module, (nn.Linear, nn.Embedding)):
          return
  
      module.weight.data.normal_(mean=0.0, std=0.02)
  
      # Initialize biases to $0$
      if isinstance(module, nn.Linear) and module.bias is not None:
          module.bias.data.zero_()
  
imports: import torch
  from torch import nn
  from labml import experiment
  from labml.configs import option
  from labml_helpers.module import Module
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
  from labml_nn.optimizers.configs import OptimizerConfigs
  from labml_nn.transformers import TransformerConfigs, Encoder
  from labml_nn.transformers.utils import subsequent_mask
label_desc: Use image node_id 2 for calling a global function with example usage: _init_weights(module) without return types