def METHOD_NAME(self, name, conversion_options):
    input_gain = 2.0
    interface_kwargs = dict(file_path=str(DATA_PATH / "neuroscope" / "test1" / "test1.dat"), gain=input_gain)
    nwbfile_path = str(self.savedir / f"test_neuroscope_gains_{name}.nwb")
    class TestConverter(NWBConverter):
        data_interface_classes = dict(TestRecording=NeuroScopeRecordingInterface)
    converter = TestConverter(source_data=dict(TestRecording=interface_kwargs))
    metadata = converter.get_metadata()
    metadata["NWBFile"].update(session_start_time=datetime.now().astimezone())
    converter.run_conversion(
        nwbfile_path=nwbfile_path, overwrite=True, metadata=metadata, conversion_options=conversion_options
    )
    with NWBHDF5IO(path=nwbfile_path, mode="r") as io:
        nwbfile = io.read()
        # output_channel_conversion = nwbfile.acquisition["ElectricalSeriesRaw"].channel_conversion[:]
        # input_gain_array = np.ones_like(output_channel_conversion) * input_gain
        # np.testing.assert_array_almost_equal(input_gain_array, output_channel_conversion)
        assert nwbfile.acquisition["ElectricalSeries"].channel_conversion is None
        nwb_recording = NwbRecordingExtractor(file_path=nwbfile_path)
        nwb_recording_gains = nwb_recording.get_channel_gains()
        npt.assert_almost_equal(input_gain * np.ones_like(nwb_recording_gains), nwb_recording_gains)