code: def forward(
      self,
      tokens: Tensor,
      start_pos: Union[Variable, int],
      temperature: float = 0.0,
  ):
      _bsz, seqlen = tokens.shape
      freqs_cis = self.freqs_cis.shrink(
          (None, (start_pos, start_pos + seqlen), None, None, None)
      )
  
      h = self.tok_embeddings(tokens)
      mask = (
          Tensor.full(
              (1, 1, seqlen, start_pos + seqlen),
              float("-inf"),
              dtype=h.dtype,
              device=h.device,
          )
          .triu(start_pos + 1)
          .realize()
          if seqlen > 1
          else None
      )
      for layer in self.layers:
          h = layer(h, start_pos, freqs_cis, mask)
      logits = self.output(self.norm(h))[:, -1, :]
      if temperature < 1e-6:
          ret = logits.argmax(-1)
      else:
          ret = (logits / temperature).softmax().multinomial()
      return ret.realize()
  
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 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(tokens, start_pos, temperature) and returns: ret