How to use the torchaudio.functional.lfilter function in torchaudio

def _test_lfilter_basic(self, dtype, device):
        """
        Create a very basic signal,
        Then make a simple 4th order delay
        The output should be same as the input but shifted
        """

        torch.random.manual_seed(42)
        waveform = torch.rand(2, 44100 * 1, dtype=dtype, device=device)
        b_coeffs = torch.tensor([0, 0, 0, 1], dtype=dtype, device=device)
        a_coeffs = torch.tensor([1, 0, 0, 0], dtype=dtype, device=device)
        output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs)

        assert torch.allclose(waveform[:, 0:-3], output_waveform[:, 3:], atol=1e-5)

device=device,
        )
        a_coeffs = torch.tensor(
            [
                1.0,
                -4.8155751,
                10.2217618,
                -12.14481273,
                8.49018171,
                -3.3066882,
                0.56088705,
            ],
            device=device,
        )

        output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs)
        assert len(output_waveform.size()) == 2
        assert output_waveform.size(0) == waveform.size(0)
        assert output_waveform.size(1) == waveform.size(1)
        _test_torchscript_functional(F.lfilter, waveform, a_coeffs, b_coeffs)

b2 = 0.9
        a0 = 0.7
        a1 = 0.2
        a2 = 0.6

        # SoX method
        E = torchaudio.sox_effects.SoxEffectsChain()
        E.set_input_file(fn_sine)
        _timing_sox = time.time()
        E.append_effect_to_chain("biquad", [b0, b1, b2, a0, a1, a2])
        waveform_sox_out, sr = E.sox_build_flow_effects()
        _timing_sox_run_time = time.time() - _timing_sox

        _timing_lfilter_filtering = time.time()
        waveform, sample_rate = torchaudio.load(fn_sine, normalization=True)
        waveform_lfilter_out = F.lfilter(
            waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2])
        )
        _timing_lfilter_run_time = time.time() - _timing_lfilter_filtering

        assert torch.allclose(waveform_sox_out, waveform_lfilter_out, atol=1e-4)
        _test_torchscript_functional(
            F.lfilter, waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2])
        )

1.0,
                -4.8155751,
                10.2217618,
                -12.14481273,
                8.49018171,
                -3.3066882,
                0.56088705,
            ],
            device=device,
        )

        output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs)
        assert len(output_waveform.size()) == 2
        assert output_waveform.size(0) == waveform.size(0)
        assert output_waveform.size(1) == waveform.size(1)
        _test_torchscript_functional(F.lfilter, waveform, a_coeffs, b_coeffs)