def METHOD_NAME(kf,
                cost_matrix,
                tracks,
                detections,
                only_position=False,
                lambda_=0.98):
    if cost_matrix.size == 0:
        return cost_matrix
    gating_dim = 2 if only_position else 4
    gating_threshold = kalman_filter.chi2inv95[gating_dim]
    measurements = np.asarray([det.to_xyah() for det in detections])
    for row, track in enumerate(tracks):
        gating_distance = kf.gating_distance(
            track.mean,
            track.covariance,
            measurements,
            only_position,
            metric='maha')
        cost_matrix[row, gating_distance > gating_threshold] = np.inf
        cost_matrix[row] = lambda_ * cost_matrix[row] + (1 - lambda_
                                                         ) * gating_distance
    return cost_matrix