def test_eegminer_corr_output_size():
    """
    Test that EEGMiner produces the correct number of features for the 'corr' method.
    """
    batch_size = 2
    n_chans = 6
    n_times = 256
    n_outputs = 2
    sfreq = 100.0  # Hz
    n_filters = 2

    input_tensor = torch.randn(batch_size, n_chans, n_times)

    eegminer = EEGMiner(
        method="corr",
        n_chans=n_chans,
        n_times=n_times,
        n_outputs=n_outputs,
        sfreq=sfreq,
        filter_f_mean=[10.0, 20.0],
        filter_bandwidth=[5.0, 5.0],
        filter_shape=[2.0, 2.0],
        group_delay=[20.0, 20.0],
    )

    output = eegminer(input_tensor)
    expected_n_features = n_filters * n_chans * (n_chans - 1) // 2
    assert eegminer.n_features == expected_n_features, \
        f"Expected {expected_n_features} features, got {eegminer.n_features}"
    assert output.shape == (batch_size, n_outputs), \
        f"Output shape should be ({batch_size}, {n_outputs}), got {output.shape}"

    Test that EEGMiner produces the correct number of features for the 'corr' method.
    
test/unit_tests/models/test_models.py