code: def __init__(self, params: ModelArgs):
      super().__init__()
      self.params = params
      self.vocab_size = params.vocab_size
      self.n_layers = params.n_layers
  
      self.tok_embeddings = ParallelEmbedding(
          params.vocab_size,
          params.dim,
          init_method=lambda x: x,
      )
  
      self.layers = torch.nn.ModuleList()
      for layer_id in range(params.n_layers):
          self.layers.append(TransformerBlock(layer_id, params))
  
      self.norm = RMSNorm(params.dim, eps=params.norm_eps)
      self.output = ColumnParallelLinear(
          params.dim,
          params.vocab_size,
          bias=False,
          init_method=lambda x: x,
      )
  
      self.freqs_cis = precompute_freqs_cis(
          self.params.dim // self.params.n_heads,
          self.params.max_seq_len * 2,
          params.rope_theta,
      )
  
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 Transformer object from inherited base classes: nn with example: obj = Transformer(params)