code: def _init_weights(self, module):
      # important: this ported version of VipLlava isn't meant for training from scratch - only
      # inference and fine-tuning - so the proper init weights code has been removed - the original codebase
      # https://github.com/haotian-liu/LLaVA/tree/main/vipllava should serve for that purpose
      std = (
          self.config.initializer_range
          if hasattr(self.config, "initializer_range")
          else self.config.text_config.initializer_range
      )
  
      if hasattr(module, "class_embedding"):
          module.class_embedding.data.normal_(mean=0.0, std=std)
  
      if isinstance(module, (nn.Linear, nn.Conv2d)):
          module.weight.data.normal_(mean=0.0, std=std)
          if module.bias is not None:
              module.bias.data.zero_()
      elif isinstance(module, nn.Embedding):
          module.weight.data.normal_(mean=0.0, std=std)
          if module.padding_idx is not None:
              module.weight.data[module.padding_idx].zero_()
  
imports: from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from ...None import PreTrainedModel
  from ...activations import ACT2FN
  from ...cache_utils import Cache
  from ...modeling_outputs import ModelOutput
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ..auto import AutoModel, AutoModelForCausalLM
  from .configuration_vipllava import VipLlavaConfig
label_desc: Use image node_id 1 for calling the VipLlavaPreTrainedModel obj's underlying member method code with example usage: obj._init_weights(module) without return types