code: def forward(self, x, feat_size: List[int]):
      B, N, _ = x.shape
  
      fold_dim = 1 if self.unshared else self.num_heads
      x = x.reshape(B, N, fold_dim, -1).permute(0, 2, 1, 3)
      q = k = v = x
  
      if self.pool_q is not None:
          q, q_tok = reshape_pre_pool(q, feat_size, self.has_cls_token)
          q = self.pool_q(q)
          q, q_size = reshape_post_pool(q, self.num_heads, q_tok)
      else:
          q_size = feat_size
      if self.norm_q is not None:
          q = self.norm_q(q)
  
      if self.pool_k is not None:
          k, k_tok = reshape_pre_pool(k, feat_size, self.has_cls_token)
          k = self.pool_k(k)
          k, k_size = reshape_post_pool(k, self.num_heads, k_tok)
      else:
          k_size = feat_size
      if self.norm_k is not None:
          k = self.norm_k(k)
  
      if self.pool_v is not None:
          v, v_tok = reshape_pre_pool(v, feat_size, self.has_cls_token)
          v = self.pool_v(v)
          v, v_size = reshape_post_pool(v, self.num_heads, v_tok)
      else:
          v_size = feat_size
      if self.norm_v is not None:
          v = self.norm_v(v)
  
      q_N = q_size[0] * q_size[1] + int(self.has_cls_token)
      q = q.transpose(1, 2).reshape(B, q_N, -1)
      q = self.q(q).reshape(B, q_N, self.num_heads, -1).transpose(1, 2)
  
      k_N = k_size[0] * k_size[1] + int(self.has_cls_token)
      k = k.transpose(1, 2).reshape(B, k_N, -1)
      k = self.k(k).reshape(B, k_N, self.num_heads, -1)
  
      v_N = v_size[0] * v_size[1] + int(self.has_cls_token)
      v = v.transpose(1, 2).reshape(B, v_N, -1)
      v = self.v(v).reshape(B, v_N, self.num_heads, -1).transpose(1, 2)
  
      attn = (q * self.scale) @ k
      if self.rel_pos_type == "spatial":
          attn = cal_rel_pos_type(
              attn,
              q,
              self.has_cls_token,
              q_size,
              k_size,
              self.rel_pos_h,
              self.rel_pos_w,
          )
      attn = attn.softmax(dim=-1)
      x = attn @ v
  
      if self.residual_pooling:
          x = x + q
  
      x = x.transpose(1, 2).reshape(B, -1, self.dim_out)
      x = self.proj(x)
  
      return x, q_size
  
imports: import operator
  from collections import OrderedDict
  from dataclasses import dataclass
  from functools import partial, reduce
  from typing import Union, List, Tuple, Optional
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import Mlp, DropPath, trunc_normal_tf_, get_norm_layer, to_2tuple
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._registry import register_model, register_model_deprecations, generate_default_cfgs
label_desc: Use image node_id 2 for calling the MultiScaleAttentionPoolFirst obj's underlying member method code with example usage: obj.forward(x, feat_size) and returns: x,  q_size