code: def forward(self, x):
      x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
      x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
      x = x + self.positional_embedding[:, None, :].to(
          x.dtype
      )  # (HW+1)NC
      x, _ = F.multi_head_attention_forward(
          query=x[:1],
          key=x,
          value=x,
          embed_dim_to_check=x.shape[-1],
          num_heads=self.num_heads,
          q_proj_weight=self.q_proj.weight,
          k_proj_weight=self.k_proj.weight,
          v_proj_weight=self.v_proj.weight,
          in_proj_weight=None,
          in_proj_bias=torch.cat(
              [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]
          ),
          bias_k=None,
          bias_v=None,
          add_zero_attn=False,
          dropout_p=0,
          out_proj_weight=self.c_proj.weight,
          out_proj_bias=self.c_proj.bias,
          use_separate_proj_weight=True,
          training=self.training,
          need_weights=False,
      )
      return x.squeeze(0)
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the AttentionPool2d obj's underlying member method code with example usage: obj.forward(x) and returns: x