def test_eeginception_mi_binary_n_params(n_filter, reported):
    """Make sure the number of parameters is the same as in the paper when
    using the same architecture hyperparameters.

    Note
    ----
    For some reason, we match the correct number of parameters for all
    configurations in the binary classification case, but none for the 4-class
    case... Should be investigated by contacting the authors.
    """
    model = EEGInceptionMI(
        n_chans=3,
        n_outputs=2,
        input_window_seconds=3.0,  # input_time
        sfreq=250,
        n_convs=3,
        n_filters=n_filter,
        kernel_unit_s=0.1,
    )

    n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    # From first column of TABLE 2 in EEG-Inception paper
    assert n_params == reported
Make sure the number of parameters is the same as in the paper when
    using the same architecture hyperparameters.

    Note
    ----
    For some reason, we match the correct number of parameters for all
    configurations in the binary classification case, but none for the 4-class
    case... Should be investigated by contacting the authors.
    
test/unit_tests/models/test_models.py