code: def forward(
      self,
      token_values: torch.Tensor,
      token_lengths: torch.Tensor,
      start_pos: torch.Tensor,
      cache: list[LayerCache],
      kv_padding: int,
  ) -> torch.Tensor:
      attn_bias = AttnBias.from_seqlens(
          q_seqlen=token_lengths.tolist(),
          kv_seqlen=(start_pos + token_lengths).tolist(),
          kv_padding=kv_padding,
      )
      return self.forward_with_attn_bias(token_values, attn_bias, cache)
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import mp_utils
  import torch
  from torch import nn
  from torch.nn import functional
  from xformers.ops import RMSNorm, fmha, rope_padded
  from xformers.ops.fmha.attn_bias import BlockDiagonalCausalWithOffsetPaddedKeysMask
label_desc: Use image node_id 3 for calling the Transformer obj's underlying member method code with example usage: obj.forward(token_values, token_lengths, start_pos, cache, kv_padding) and returns: self