code: def _init_hubert_pretrain_model(module):
      if isinstance(module, components.ConvLayerBlock):
          torch.nn.init.kaiming_normal_(module.conv.weight)
      elif isinstance(
          module, components.ConvolutionalPositionalEmbedding
      ):
          # normalize the weight to normal distribution.
          std = math.sqrt(4.0 / (module.embed_dim * module.kernel_size))
          torch.nn.init.normal_(module.conv.weight, mean=0.0, std=std)
          torch.nn.init.constant_(module.conv.bias, 0.0)
      elif isinstance(module, components.SelfAttention):
          # normalize the query, key, value, and out_proj parameters in self attention module.
          torch.nn.init.xavier_uniform_(
              module.k_proj.weight, gain=1 / math.sqrt(2)
          )
          torch.nn.init.xavier_uniform_(
              module.v_proj.weight, gain=1 / math.sqrt(2)
          )
          torch.nn.init.xavier_uniform_(
              module.q_proj.weight, gain=1 / math.sqrt(2)
          )
          torch.nn.init.xavier_uniform_(module.out_proj.weight)
          torch.nn.init.constant_(module.out_proj.bias, 0.0)
      elif isinstance(module, components.Transformer):
          module.apply(components._init_transformer_params)
      else:
          pass
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  from torch import Tensor
  from torch.nn import Module
  from .None import components
label_desc: Use image node_id 8 for calling a global function with example usage: _init_hubert_pretrain_model(module) without return types