code: def init_bert_params(module):
      """
      Initialize the weights specific to the BERT Model.
      This overrides the default initializations depending on the specified arguments.
          1. If normal_init_linear_weights is set then weights of linear
             layer will be initialized using the normal distribution and
             bais will be set to the specified value.
          2. If normal_init_embed_weights is set then weights of embedding
             layer will be initialized using the normal distribution.
          3. If normal_init_proj_weights is set then weights of
             in_project_weight for MultiHeadAttention initialized using
             the normal distribution (to be validated).
      """
  
      if isinstance(module, LunaSentenceEncoder):
          module.projected_embeddings.data.normal_(mean=0.0, std=0.02)
      if isinstance(module, nn.Linear):
          module.weight.data.normal_(mean=0.0, std=0.02)
          if module.bias is not None:
              module.bias.data.zero_()
      if isinstance(module, nn.Embedding):
          module.weight.data.normal_(mean=0.0, std=0.02)
          if module.padding_idx is not None:
              module.weight.data[module.padding_idx].zero_()
      if isinstance(module, LunarMultiheadAttention):
          module.pq_proj.weight.data.normal_(mean=0.0, std=0.02)
          module.q_proj.weight.data.normal_(mean=0.0, std=0.02)
          if module.pc_proj is not None:
              module.pc_proj.weight.data.normal_(mean=0.0, std=0.02)
              module.c_proj.weight.data.normal_(mean=0.0, std=0.02)
          else:
              module.pk_proj.weight.data.normal_(mean=0.0, std=0.02)
              module.pv_proj.weight.data.normal_(mean=0.0, std=0.02)
              module.k_proj.weight.data.normal_(mean=0.0, std=0.02)
              module.v_proj.weight.data.normal_(mean=0.0, std=0.02)
  
imports: import math
  from typing import Callable, Optional
  import torch
  from fairseq import utils
  from fairseq.modules import LayerDropModuleList, LayerNorm, PositionalEmbedding
  from fairseq.modules.fairseq_dropout import FairseqDropout
  from pytext.utils.usage import log_class_usage
  from torch import nn
  from torch.nn import Parameter
  from .None import LunarMultiheadAttention
label_desc: Use image node_id 1 for calling a global function with example usage: init_bert_params(module) without return types