code: def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
      if self.num_branches == 1:
          return [self.branches[0](x[0])]
  
      for i, branch in enumerate(self.branches):
          x[i] = branch(x[i])
  
      x_fuse = []
      for i, fuse_outer in enumerate(self.fuse_layers):
          y = None
          for j, f in enumerate(fuse_outer):
              if y is None:
                  y = f(x[j])
              else:
                  y = y + f(x[j])
          x_fuse.append(self.fuse_act(y))
      return x_fuse
  
imports: import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg, pretrained_cfg_for_features
  from ._features import FeatureInfo
  from ._registry import register_model, generate_default_cfgs
  from .resnet import BasicBlock, Bottleneck
label_desc: Use image node_id 7 for calling the HighResolutionModule obj's underlying member method code with example usage: obj.forward(x) and returns: x_fuse, list