def METHOD_NAME(device):
    from speechbrain.nnet.normalization import InstanceNorm2d
    input = torch.randn(100, 10, 20, 2, device=device) + 2.0
    norm = InstanceNorm2d(input_shape=input.shape).to(device)
    output = norm(input)
    assert input.shape == output.shape
    current_mean = output.mean(dim=[2, 3]).mean()
    assert torch.abs(current_mean) < 1e-06
    current_std = output.std(dim=[2, 3]).mean()
    assert torch.abs(1.0 - current_std) < 0.01
    assert torch.jit.trace(norm, input)