code: def __init__(self, config: GPT2Config):
      super().__init__()
      self.transformer = GPT2Model(config)
      self.lm_head = nn.Linear(
          config.n_embd, config.vocab_size, bias=False
      )
      # Initialize weights and apply final processing
      self.apply(
          partial(
              _init_weights,
              n_layer=config.num_hidden_layers,
              initializer_range=config.initializer_range,
          )
      )
      self.tie_weights()
  
imports: import math
  from functools import partial
  from collections import namedtuple
  from collections.abc import Sequence
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
  from src.models.modules.mha import MHA
  from src.models.modules.mlp import Mlp, FusedDenseGeluDense
  from src.models.modules.block import Block
  from src.models.modules.embedding import GPT2Embeddings
label_desc: Use image node_id 1 to create a new GPT2LMHeadModel object from inherited base classes: nn with example: obj = GPT2LMHeadModel(config)