code: def forward(self, x):
      x = x.reshape(
          x.shape[0], x.shape[1], x.shape[2] * x.shape[3]
      ).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,
          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[0]
  
imports: from collections import OrderedDict
  from itertools import repeat
  import collections.abc
  import math
  import torch
  import torch.nn.functional
  from torch import nn
  from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
  from lavis.models.eva_vit import convert_weights_to_fp16
  from lavis.common.dist_utils import download_cached_file
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