code: def forward(self, x):
      B, N, C = x.shape
      qkv = self.qkv(x).reshape(
          B, N, 3, self.num_heads, C // self.num_heads
      )
      q, k, v = xops.unbind(qkv, dim=2)
  
      x = xops.memory_efficient_attention(q, k, v, op=self.op).reshape(
          B, N, C
      )
  
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
imports: import itertools
  from functools import partial, reduce
  from typing import Iterator
  import timm
  import torch
  import torch.nn
  from timm.models.layers import Mlp
  from timm.models.vision_transformer import Attention
  from timm.models.vision_transformer import Block
  from torch.utils import benchmark
  from utils import benchmark_main_helper
  import xformers.ops
label_desc: Use image node_id 2 for calling the TimmMemEffAttention obj's underlying member method code with example usage: obj.forward(x) and returns: x