class_docstr: An abstract class to handle weights initialization and
  a simple interface for dowloading and loading pretrained models.
class_docstr_tok: ["An","abstract","class","to","handle","weights","initialization","and","a","simple","interface","for","dowloading","and","loading","pretrained","models","."]
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=0.02)
      elif isinstance(module, LayerNorm):
          if "beta" in dir(module) and "gamma" in dir(module):
              module.beta.data.zero_()
              module.gamma.data.fill_(1.0)
          else:
              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_()
  
file_path: towhee/towhee/models/clip4clip/until_module.py
imports: import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 for calling the PreTrainedModel obj's underlying member method code with example usage: obj.init_weights(module) without return types