code: def __init__(self):
      super().__init__()
      self.input1d = torch.randn(1, 4, 50)
      self.module1d = nn.ModuleList(
          [
              nn.BatchNorm1d(4),
              nn.InstanceNorm1d(4),
          ]
      )
  
      self.input2d = torch.randn(1, 4, 30, 10)
      self.module2d = nn.ModuleList(
          [
              nn.BatchNorm2d(4),
              nn.GroupNorm(4, 4),
              nn.InstanceNorm2d(4),
              nn.LayerNorm([4, 30, 10]),
              nn.LocalResponseNorm(2),
          ]
      )
  
      self.input3d = torch.randn(1, 4, 10, 4, 4)
      self.module3d = nn.ModuleList(
          [
              nn.BatchNorm3d(4),
              nn.InstanceNorm3d(4),
              nn.ChannelShuffle(2),
          ]
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new NNNormalizationModule object from inherited base classes: torch.nn with example: obj = NNNormalizationModule()