code: def __init__(self, args: ModelArgs):
      super().__init__()
      mp_size = mp_utils.get_world_size()
      assert args.dim % mp_size == 0
      assert args.vocab_size > 0
      assert args.vocab_size % mp_size == 0
  
      self.tok_embeddings = nn.Embedding(
          num_embeddings=args.vocab_size,
          embedding_dim=args.dim // mp_size,
      )
  
      self.layers = nn.ModuleList()
      for _ in range(args.n_layers):
          self.layers.append(TransformerBlock(args))
  
      self.norm = RMSNorm(args.dim, eps=args.norm_eps)
  
      self.output = nn.Linear(
          args.dim,
          args.vocab_size // mp_size,
          bias=False,
      )
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import mp_utils
  import torch
  from torch import nn
  from torch.nn import functional
  from xformers.ops import RMSNorm, fmha, rope_padded
  from xformers.ops.fmha.attn_bias import BlockDiagonalCausalWithOffsetPaddedKeysMask
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(args)