code: def __init__(self, config):
      super().__init__()
      assert config.input_vocab_size is not None
      assert config.output_vocab_size is not None
      assert config.block_size is not None
      self.config = config
  
      self.transformer = nn.ModuleDict(
          dict(
              wte=nn.Embedding(config.input_vocab_size, config.n_embd),
              wpe=nn.Embedding(config.block_size, config.n_embd),
              drop=nn.Dropout(config.dropout),
              h=nn.ModuleList(
                  [Block(config, idx) for idx in range(config.n_layer)]
              ),
              ln_f=LayerNorm(config.n_embd, bias=config.bias),
          )
      )
      self.lm_head = nn.Linear(
          config.n_embd, config.output_vocab_size, bias=False
      )
  
imports: import math
  from dataclasses import dataclass
  import torch
  import torch.nn
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new GPT object from inherited base classes: nn with example: obj = GPT(config)