code: def forward(self, img):
      x = self.to_patch_embedding(img)
      b, c, h, w = x.shape
  
      num_hierarchies = len(self.layers)
  
      for level, (transformer, aggregate) in zip(
          reversed(range(num_hierarchies)), self.layers
      ):
          block_size = 2**level
          x = rearrange(
              x,
              "b c (b1 h) (b2 w) -> (b b1 b2) c h w",
              b1=block_size,
              b2=block_size,
          )
          x = transformer(x)
          x = rearrange(
              x,
              "(b b1 b2) c h w -> b c (b1 h) (b2 w)",
              b1=block_size,
              b2=block_size,
          )
          x = aggregate(x)
  
      return self.mlp_head(x)
  
imports: from functools import partial
  import torch
  from torch import nn, einsum
  from einops import rearrange
  from einops.layers.torch import Rearrange, Reduce
label_desc: Use image node_id 2 for calling the NesT obj's underlying member method code with example usage: obj.forward(img) and returns: self