code: def forward(self, img):
      x = self.to_patch_embedding(img)
  
      x, ps = pack_one(x, "b * d")
  
      b, n, _ = x.shape
      x += self.pos_embedding[:, :n]
  
      x = unpack_one(x, ps, "b * d")
  
      x = self.dropout(x)
  
      x = self.xcit_transformer(x)
  
      x = self.final_norm(x)
  
      cls_tokens = repeat(self.cls_token, "d -> b 1 d", b=b)
  
      x = rearrange(x, "b ... d -> b (...) d")
      cls_tokens = self.cls_transformer(cls_tokens, context=x)
  
      return self.mlp_head(cls_tokens[:, 0])
  
imports: from random import randrange
  import torch
  from torch import nn, einsum
  from torch.nn import Module, ModuleList
  import torch.nn.functional
  from einops import rearrange, repeat, pack, unpack
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 2 for calling the XCiT obj's underlying member method code with example usage: obj.forward(img) and returns: self