code: def test_SincConv(device):
      from speechbrain.nnet.CNN import SincConv
  
      input = torch.rand([4, 16000], device=device)
      convolve = SincConv(
          input_shape=input.shape,
          out_channels=8,
          kernel_size=65,
          padding="same",
      ).to(device)
      output = convolve(input)
      assert output.shape[-1] == 8
  
      assert torch.jit.trace(convolve, input)
  
      # Multichannel case
      input = torch.rand([10, 16000, 8], device=device)
      convolve = SincConv(
          input_shape=input.shape,
          out_channels=16,
          kernel_size=11,
          padding="same",
      ).to(device)
      output = convolve(input)
      assert output.shape[-1] == 16
  
      assert torch.jit.trace(convolve, input)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 for calling a global function with example usage: test_SincConv(device) without return types