code: def test():
      model = UNet8(50, num_filters=32).cuda()
      inputs = torch.randn(2, 3, 128, 128).cuda()
      out, _ = model(inputs)
      # print(model)
      print(out.size(), _.size())
  
imports: from torch import nn
  from torch.nn import functional
  import torch
  from torchvision import models
  from torchvision.models import resnet34, resnet101, resnet50, resnet152
  import torchvision
label_desc: Use image node_id 3 for calling a global function with example usage: test() without return types