code: def __init__(self, layer_id: int, args: ModelArgs):
      super().__init__()
      self.n_heads = args.n_heads
      self.dim = args.dim
      self.head_dim = args.dim // args.n_heads
      self.attention = Attention(args)
      self.feed_forward = FeedForward(
          dim=args.dim,
          hidden_dim=4 * args.dim,
          multiple_of=args.multiple_of,
          ffn_dim_multiplier=args.ffn_dim_multiplier,
      )
      self.layer_id = layer_id
      self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
      self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
  
imports: import math
  from dataclasses import dataclass
  from typing import Any, Optional, Tuple
  import fairscale.nn.model_parallel.initialize
  import torch
  import torch.nn.functional
  from fairscale.nn.model_parallel.layers import ColumnParallelLinear, ParallelEmbedding, RowParallelLinear
  from torch import nn
label_desc: Use image node_id 1 to create a new TransformerBlock object from inherited base classes: nn with example: obj = TransformerBlock(layer_id, args)