code: def __init__(self, config):
      super().__init__(config)
  
      self.wte = nn.Embedding(config.vocab_size, config.n_embd)
      # self.wpe = nn.Embedding(config.n_positions, config.n_embd)
      self.drop = nn.Dropout(config.embd_pdrop)
      self.h = nn.ModuleList(
          [
              Block(config.n_ctx, config, scale=True)
              for _ in range(config.n_layer)
          ]
      )
      self.ln_f = nn.LayerNorm(
          config.n_embd, eps=config.layer_norm_epsilon
      )
  
      self.init_weights()
      # Model parallel
      self.model_parallel = False
      self.device_map = None
  
      self.use_layers = None
  
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 1 to create a new GPT2Model object from inherited base classes: GPT2PreTrainedModel with example: obj = GPT2Model(config)