code: def forward(self, q: torch.Tensor, *_, **__):
      # Expose the 2D token structure
      B, HW, C = q.shape
      H = int(math.sqrt(HW))
      assert H * H == HW
  
      q = q.transpose(-2, -1).reshape(B, C, H, H)
  
      # 2D pool
      x_pool = self.pool(q) - q  # compensate for the residual path
  
      # Get back to B HW C
      return x_pool.flatten(2, 3).transpose(-2, -1)
  
file_path: xformers/xformers/components/attention/pooling.py
imports: import math
  from dataclasses import dataclass
  from typing import Optional
  import torch
  import torch.nn
  from xformers.components.attention import Attention, AttentionConfig, register_attention
label_desc: Use image node_id 2 for calling the Pooling obj's underlying member method code with example usage: obj.forward(q) and returns: x_pool