code: def forward(
      self,
      x: torch.Tensor,
      cache: LayerCache,
      attn_bias: AttnBias,
  ) -> torch.Tensor:
      h = x + self.attention.forward(
          self.attention_norm(x),
          cache,
          attn_bias,
      )
      out = h + self.feed_forward(self.ffn_norm(h))
      return out
  
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 2 for calling the TransformerBlock obj's underlying member method code with example usage: obj.forward(x, cache, attn_bias) and returns: out