code: def _init_weights(self, module):
      std = self.config.init_std
      if isinstance(module, nn.Linear):
          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: import inspect
  import warnings
  from typing import Optional, Tuple, Dict, Any
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.utils import ModelOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers.configuration_utils import PretrainedConfig
  from torch.nn import CrossEntropyLoss
  from .abstract_model import AbstractModel
label_desc: Use image node_id 1 for calling the RNNPretrainedModel obj's underlying member method code with example usage: obj._init_weights(module) without return types