code: def test_Conv2d(device):
      from speechbrain.nnet.CNN import Conv2d
  
      input = torch.rand([4, 11, 32, 1], device=device)
      convolve = Conv2d(
          out_channels=1,
          input_shape=input.shape,
          kernel_size=(1, 1),
          padding="same",
      ).to(device)
      output = convolve(input)
      assert output.shape[-1] == 1
  
      convolve.conv.weight = torch.nn.Parameter(
          torch.zeros(convolve.conv.weight.shape, device=device)
      )
      convolve.conv.bias = torch.nn.Parameter(
          torch.tensor([0], device=device).float()
      )
      output = convolve(input)
      assert torch.all(
          torch.eq(torch.zeros(input.shape, device=device), output)
      )
  
      convolve.conv.weight = torch.nn.Parameter(
          torch.ones(convolve.conv.weight.shape, device=device)
      )
      convolve.conv.bias = torch.nn.Parameter(
          torch.tensor([0], device=device).float()
      )
      output = convolve(input)
      assert torch.all(torch.eq(input, output))
  
      assert torch.jit.trace(convolve, input)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 3 for calling a global function with example usage: test_Conv2d(device) without return types