code: def __init__(
      self,
      dim: int,
      hidden_dim: int,
      n_heads: int,
      n_layers: int,
      norm_eps: float,
      vocab_size,
      linear=nn.Linear,
      n_kv_heads=None,
      rope_theta=10000,
      max_context=1024,
      jit=True,
      feed_forward=FeedForward,
  ):
      self.layers = [
          TransformerBlock(
              dim,
              hidden_dim,
              n_heads,
              n_kv_heads,
              norm_eps,
              max_context,
              linear,
              feed_forward=feed_forward,
          )
          for _ in range(n_layers)
      ]
      self.norm = RMSNorm(dim, norm_eps)
      self.tok_embeddings = nn.Embedding(vocab_size, dim)
      self.output = linear(dim, vocab_size, bias=False)
      self.max_context = max_context
      self.freqs_cis = precompute_freqs_cis(
          dim // n_heads, self.max_context * 2, rope_theta
      )
      self.forward_jit = TinyJit(self.forward) if jit else None
  
imports: from typing import Tuple, Union, Optional, Dict
  from tinygrad import Tensor, Variable, TinyJit, dtypes, nn, Device
  from tinygrad.helpers import getenv
label_desc: Use image node_id 1 to create a new Transformer object  with example: obj = Transformer(dim, hidden_dim, n_heads, n_layers, norm_eps, vocab_size, linear, n_kv_heads, rope_theta, max_context, jit, feed_forward)