code: def forward(
      self,
      x: torch.Tensor,
      cache: LayerCache,
      attn_bias: AttnBias,
  ) -> torch.Tensor:
      # x.shape is (sum(seq_lens), dim)
      #
      # Since we support heterogenous sequence
      # lengths, the hidden states are all
      # concatenated together along the usual
      # sequence dimension. The attention below
      # finds out where sequences start & end
      # using the provided attention bias.
  
      xqkv = self.wqkv(x)
      xq = xqkv[:, : (self.n_local_heads * self.head_dim)]
      xkv = xqkv[:, (self.n_local_heads * self.head_dim) :]
      xk, xv = xkv.chunk(2, 1)
  
      output_shape = xq.shape
      heads_per_group = self.n_local_heads // self.n_local_kv_heads
      xq = xq.view(
          1,
          xq.shape[0],
          self.n_local_kv_heads,
          heads_per_group,
          self.head_dim,
      )
      xk = xk.view(
          1, xk.shape[0], self.n_local_kv_heads, 1, self.head_dim
      )
      xv = xv.view(
          1, xv.shape[0], self.n_local_kv_heads, 1, self.head_dim
      )
      cache_k, cache_v = cache
  
      xq = rope_padded(
          xq=xq,
          xk=xk,
          xv=xv,
          cache_k=cache_k,
          cache_v=cache_v,
          attn_bias=attn_bias,
          theta=self.rope_theta,
      )
  
      # rope_padded() updated the caches, so we
      # call attention directly
      output = fmha.memory_efficient_attention_forward(
          xq, cache_k, cache_v, attn_bias
      )
  
      output = self.wo(output.reshape(output_shape))
      mp_utils.all_reduce(output)
  
      return output
  
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 Attention obj's underlying member method code with example usage: obj.forward(x, cache, attn_bias) and returns: output