code: def __init__(self):
      super().__init__()
      self.input1d = torch.randn(1, 4, 36)
      self.input2d = torch.randn(1, 4, 30, 10)
      self.input3d = torch.randn(1, 4, 10, 4, 4)
      self.module1d = nn.ModuleList(
          [
              nn.Conv1d(4, 33, 3),
              nn.ConvTranspose1d(4, 33, 3),
              nn.Fold(output_size=(5, 10), kernel_size=(2, 2)),
          ]
      )
      self.module2d = nn.ModuleList(
          [
              nn.Conv2d(4, 33, 3),
              nn.ConvTranspose2d(4, 33, 3),
              nn.Unfold(kernel_size=3),
          ]
      )
      self.module3d = nn.ModuleList(
          [
              nn.Conv3d(4, 33, 2),
              nn.ConvTranspose3d(4, 33, 3),
          ]
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new NNConvolutionModule object from inherited base classes: torch.nn with example: obj = NNConvolutionModule()