class_docstr: Implementation of a sequential bottleneck adapter block.
docstr: Initialize the weights.
docstr_tok: ["Initialize","the","weights","."]
code: def init_bert_weights(module):
      """Initialize the weights."""
      if isinstance(module, (nn.Linear, nn.Embedding)):
          # std defaults to 0.02, this might need to be changed
          module.weight.data.normal_(mean=0.0, std=0.02)
      elif isinstance(module, nn.LayerNorm):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0)
      if isinstance(module, nn.Linear) and module.bias is not None:
          module.bias.data.zero_()
  
imports: import math
  import torch
  from torch import nn
  from transformers.activations import get_activation
  from ..configuration import AdapterFusionConfig, BnConfig
  from ..context import ForwardContext
label_desc: Use image node_id 5 for calling the Adapter obj's underlying member method code with example usage: obj.init_bert_weights(module) without return types