code: def __init__(self, config: ModelArgs) -> None:
      super().__init__()
      self.config = config
  
      self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
      self.layers = nn.ModuleList(
          TransformerBlock(config) for _ in range(config.n_layer)
      )
      self.norm = RMSNorm(config.dim, eps=config.norm_eps)
      self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
  
      self.freqs_cis: Optional[Tensor] = None
      self.mask_cache: Optional[Tensor] = None
      self.max_batch_size = -1
      self.max_seq_length = -1
  
imports: from dataclasses import dataclass
  from typing import Optional
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(config)