class_docstr: An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
  models.
class_docstr_tok: ["An","abstract","class","to","handle","weights","initialization","and","a","simple","interface","for","downloading","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, Conv1D)):
          # 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=self.config.initializer_range
          )
          if (
              isinstance(module, (nn.Linear, Conv1D))
              and module.bias is not None
          ):
              module.bias.data.zero_()
      elif isinstance(module, nn.LayerNorm):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0)
  
imports: import os
  from dataclasses import dataclass
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from transformers.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
label_desc: Use image node_id 2 for calling the GPT2PreTrainedModel obj's underlying member method code with example usage: obj._init_weights(module) without return types