docstr: Initialize the weights.
docstr_tok: ["Initialize","the","weights","."]
code: def init_weights(self, module):
      """Initialize the weights."""
      if isinstance(module, (nn.Linear, nn.Embedding)):
          # Slightly different from the TF version which uses truncated_normal
          # for initialization
          # cf https://github.com/pytorch/pytorch/pull/5617
          module.weight.data.normal_(
              mean=0.0, std=self.bert_config.initializer_range
          )
      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 torch
  from mmf.common.registry import registry
  from mmf.models.pythia import Pythia
  from mmf.modules.embeddings import ProjectionEmbedding
  from mmf.utils.transform import transform_to_batch_sequence
  from torch import nn
label_desc: Use image node_id 13 for calling the MMFBert obj's underlying member method code with example usage: obj.init_weights(module) without return types