code: def forward(self, x: Tensor, H, W) -> Tensor:
      from xformers.ops import memory_efficient_attention, unbind
  
      B, N, C = x.shape
      qkv = self.qkv(x).reshape(
          B, N, 3, self.num_heads, C // self.num_heads
      )
  
      q, k, v = unbind(qkv, 2)
  
      x = memory_efficient_attention(q, k, v)
      x = x.reshape([B, N, C])
  
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
imports: import logging
  import math
  from functools import partial
  from itertools import repeat
  from typing import Callable, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.runner import BaseModule, load_checkpoint
  from mmseg.ops import resize
  from mmseg.utils import get_root_logger
  from torch import Tensor
  from .drop_path import DropPath
label_desc: Use image node_id 2 for calling the MemEffAttention obj's underlying member method code with example usage: obj.forward(x, H, W) and returns: x