code: def __init__(self, out_feats, *shapes):
      super().__init__()
  
      _, max_size = max(shapes, key=lambda x: x[1])
  
      self.scale_factors = []
      for i, shape in enumerate(shapes):
          feat, size = shape
          if max_size % size != 0:
              raise ValueError(
                  "max_size not divisble by shape {}".format(i)
              )
  
          self.scale_factors.append(max_size // size)
          self.add_module(
              "resolve{}".format(i),
              nn.Conv2d(
                  feat,
                  out_feats,
                  kernel_size=3,
                  stride=1,
                  padding=1,
                  bias=False,
              ),
          )
  
imports: import torch.nn
label_desc: Use image node_id 1 to create a new MultiResolutionFusion object from inherited base classes: nn with example: obj = MultiResolutionFusion(out_feats)