code: def __init__(self, config):
      super().__init__(config)
  
      self.embed_dim = config.hidden_size
  
      self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
      self.wpe = nn.Embedding(
          config.max_position_embeddings, self.embed_dim
      )
  
      self.drop = nn.Dropout(config.embd_pdrop)
      self.h = nn.ModuleList(
          [
              GPT2Block(config, layer_idx=i)
              for i in range(config.num_hidden_layers)
          ]
      )
      self.ln_f = nn.LayerNorm(
          self.embed_dim, eps=config.layer_norm_epsilon
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.nn
  from transformers.activations import ACT2FN
  from transformers.models.gpt2.modeling_gpt2 import BaseModelOutputWithPastAndCrossAttentions, GPT2PreTrainedModel
  from transformers.pytorch_utils import Conv1D
label_desc: Use image node_id 1 to create a new GPT2Model object from inherited base classes: GPT2PreTrainedModel with example: obj = GPT2Model(config)