code: def forward(self, tokens: torch.Tensor, start_pos: int):
      _bsz, seqlen = tokens.shape
      h = self.tok_embeddings(tokens)
      self.freqs_cis = self.freqs_cis.to(
          "cuda" if device == "cuda" else "cpu"
      )
      freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
  
      mask = None
      if seqlen > 1:
          mask = torch.full(
              (1, 1, seqlen, seqlen),
              float("-inf"),
              device=torch.device("cpu"),
          )
          mask = (
              mask.to(torch.float32)
              .triu(diagonal=start_pos + 1)
              .type_as(h)
          )
  
      for layer in self.layers:
          h = layer(
              h,
              start_pos,
              freqs_cis,
              (mask.to(device) if mask is not None else mask),
          )
      h = self.norm(h)
      output = self.output(h).float()
      return output
  
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 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(tokens, start_pos) and returns: output