code: def __init__(self, module, name):
      self.label = name
      args = module.trace_kwargs
      if isinstance(args["num_features"], ValueChoice):
          args["num_features"] = max(args["num_features"].candidates)
      super().__init__(**args)
  
      # no architecture parameter is needed for BatchNorm2d Layers
      self.alpha = nn.Parameter(torch.tensor([]))
  
file_path: nni/nni/nas/oneshot/pytorch/supermodule/_singlepathnas.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from nni.nas.nn.pytorch import ValueChoice
label_desc: Use image node_id 1 to create a new DifferentiableBatchNorm2d object from inherited base classes: nn with example: obj = DifferentiableBatchNorm2d(module, name)