code: def forward(self, x):
      cls_token, tokens = x[:, :1], x[:, 1:]
  
      cls_token = self.cls_ff(cls_token)
  
      tokens = rearrange(
          tokens, "b (h w) c -> b c h w", h=int(sqrt(tokens.shape[1]))
      )
      tokens = self.downsample(tokens)
      tokens = rearrange(tokens, "b c h w -> b (h w) c")
  
      return torch.cat((cls_token, tokens), dim=1)
  
imports: from math import sqrt
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 2 for calling the Pool obj's underlying member method code with example usage: obj.forward(x) and returns: torch