code: def test_Conv1d(device):
      from speechbrain.nnet.CNN import Conv1d
  
      input = (
          torch.tensor([-1, -1, -1, -1], device=device)
          .unsqueeze(0)
          .unsqueeze(2)
          .float()
      )
      convolve = Conv1d(
          out_channels=1,
          kernel_size=1,
          input_shape=input.shape,
          padding="same",
      ).to(device)
      output = convolve(input)
      assert input.shape == output.shape
  
      convolve.conv.weight = torch.nn.Parameter(
          torch.tensor([-1], device=device)
          .float()
          .unsqueeze(0)
          .unsqueeze(1)
      )
      convolve.conv.bias = torch.nn.Parameter(
          torch.tensor([0], device=device).float()
      )
      output = convolve(input)
      assert torch.all(
          torch.eq(torch.ones(input.shape, device=device), output)
      )
  
      assert torch.jit.trace(convolve, input)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling a global function with example usage: test_Conv1d(device) without return types