How to use the pymer4.utils.isPSD function in pymer4

elif (isinstance(corrs, np.ndarray) and corrs.ndim == 1) or isinstance(corrs, list):
        assert (
            len(corrs) == (num_features * (num_features - 1)) / 2
        ), "(num_features * (num_features - 1) / 2) correlation values are required for a flattened array or list"
        corrs = squareform(corrs)
        np.fill_diagonal(corrs, 1.0)
    elif isinstance(corrs, float):
        corrs = np.array([corrs] * int(((num_features * (num_features - 1)) / 2)))
        corrs = squareform(corrs)
        np.fill_diagonal(corrs, 1.0)
    else:
        raise ValueError(
            "Correlations must be num_features x num_feature, flattend numpy array/list or scalar"
        )

    if not isPSD(corrs):
        if forcePSD:
            # Tell user their correlations are being recomputed if they didnt ask to save them as they might not realize
            if not return_new_corrs:
                print(
                    "Correlation matrix is not positive semi-definite. Solved for new correlation matrix."
                )
            _corrs = np.array(nearestPSD(corrs, nit))

        else:
            raise ValueError(
                "Correlation matrix is not positive semi-definite. Pymer4 will not generate inaccurate multivariate data. Use the forcePD argument to automatically solve for the closest desired correlation matrix."
            )
    else:
        _corrs = corrs

    # Rescale correlation matrix by variances, given standard deviations of features