code: def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
      outs_b = []
      for i, block in enumerate(self.blocks):
          outs_b.append(block(x[i]))
  
      # only take the cls token out
      proj_cls_token = torch.jit.annotate(List[torch.Tensor], [])
      for i, proj in enumerate(self.projs):
          proj_cls_token.append(proj(outs_b[i][:, 0:1, ...]))
  
      # cross attention
      outs = []
      for i, (fusion, revert_proj) in enumerate(
          zip(self.fusion, self.revert_projs)
      ):
          tmp = torch.cat(
              (
                  proj_cls_token[i],
                  outs_b[(i + 1) % self.num_branches][:, 1:, ...],
              ),
              dim=1,
          )
          tmp = fusion(tmp)
          reverted_proj_cls_token = revert_proj(tmp[:, 0:1, ...])
          tmp = torch.cat(
              (reverted_proj_cls_token, outs_b[i][:, 1:, ...]), dim=1
          )
          outs.append(tmp)
      return outs
  
imports: from functools import partial
  from typing import List
  from typing import Tuple
  import torch
  import torch.hub
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Block
label_desc: Use image node_id 2 for calling the MultiScaleBlock obj's underlying member method code with example usage: obj.forward(x) and returns: outs