def test_eegminer_initialization_and_forward(method):
    """
    Test EEGMiner initialization and forward pass for different methods ('plv', 'mag', 'corr').
    """
    batch_size = 4
    n_chans = 8
    n_times = 256
    n_outputs = 2
    sfreq = 100.0  # Hz
    input_tensor = torch.randn(batch_size, n_chans, n_times)

    eegminer = EEGMiner(
        method=method,
        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)
    assert output.shape == (batch_size, n_outputs), \
        f"Output shape should be ({batch_size}, {n_outputs}) for method '{method}', got {output.shape}"

    Test EEGMiner initialization and forward pass for different methods ('plv', 'mag', 'corr').
    
test/unit_tests/models/test_models.py