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
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.utils.checkpoint
  from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutput
  from transformers.utils import logging
  from ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
  from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 1 for calling the LDMBertPreTrainedModel obj's underlying member method code with example usage: obj._init_weights(module) without return types