code: def _init_weights(self, module):
      std = self.config.init_std
      if isinstance(module, (nn.Linear, nn.Conv1d)):
          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 math
  import random
  from typing import Optional, Tuple
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_whisper import WhisperConfig
label_desc: Use image node_id 1 for calling the WhisperPreTrainedModel obj's underlying member method code with example usage: obj._init_weights(module) without return types