code: def forward(self, x):
      b, w = x.shape[0], self.window_size
  
      x = self.conv_stem(x)
  
      for (
          conv,
          (block_attn, block_ff),
          (grid_attn, grid_ff),
      ), register_tokens in zip(self.layers, self.register_tokens):
          x = conv(x)
  
          # block-like attention
  
          x = rearrange(
              x, "b d (x w1) (y w2) -> b x y w1 w2 d", w1=w, w2=w
          )
  
          # prepare register tokens
  
          r = repeat(
              register_tokens,
              "n d -> b x y n d",
              b=b,
              x=x.shape[1],
              y=x.shape[2],
          )
          r, register_batch_ps = pack_one(r, "* n d")
  
          x, window_ps = pack_one(x, "b x y * d")
          x, batch_ps = pack_one(x, "* n d")
          x, register_ps = pack([r, x], "b * d")
  
          x = block_attn(x) + x
          x = block_ff(x) + x
  
          r, x = unpack(x, register_ps, "b * d")
  
          x = unpack_one(x, batch_ps, "* n d")
          x = unpack_one(x, window_ps, "b x y * d")
          x = rearrange(x, "b x y w1 w2 d -> b d (x w1) (y w2)")
  
          r = unpack_one(r, register_batch_ps, "* n d")
  
          # grid-like attention
  
          x = rearrange(
              x, "b d (w1 x) (w2 y) -> b x y w1 w2 d", w1=w, w2=w
          )
  
          # prepare register tokens
  
          r = reduce(r, "b x y n d -> b n d", "mean")
          r = repeat(
              r, "b n d -> b x y n d", x=x.shape[1], y=x.shape[2]
          )
          r, register_batch_ps = pack_one(r, "* n d")
  
          x, window_ps = pack_one(x, "b x y * d")
          x, batch_ps = pack_one(x, "* n d")
          x, register_ps = pack([r, x], "b * d")
  
          x = grid_attn(x) + x
  
          r, x = unpack(x, register_ps, "b * d")
  
          x = grid_ff(x) + x
  
          x = unpack_one(x, batch_ps, "* n d")
          x = unpack_one(x, window_ps, "b x y * d")
          x = rearrange(x, "b x y w1 w2 d -> b d (w1 x) (w2 y)")
  
      return self.mlp_head(x)
  
imports: from functools import partial
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from torch.nn import Module, ModuleList, Sequential
  from einops import rearrange, repeat, reduce, pack, unpack
  from einops.layers.torch import Rearrange, Reduce
label_desc: Use image node_id 2 for calling the MaxViT obj's underlying member method code with example usage: obj.forward(x) and returns: self