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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def label_accuracy_score(label_trues, label_preds, n_class):
 """Returns accuracy score evaluation result. - overall accuracy - mean accuracy - mean IU - fwavacc """

    hist = np.zeros((n_class, n_class))
    for lt, lp in zip(label_trues, label_preds):
        hist += _fast_hist(lt.flatten(), lp.flatten(), n_class)
    acc = np.diag(hist).sum() / hist.sum()
    with np.errstate(divide='ignore', invalid='ignore'):
        acc_cls = np.diag(hist) / hist.sum(axis=1)
    acc_cls = np.nanmean(acc_cls)
    with np.errstate(divide='ignore', invalid='ignore'):
        iu = np.diag(hist) / (
            hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist)
        )
    mean_iu = np.nanmean(iu)
    freq = hist.sum(axis=1) / hist.sum()
    fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
    return acc, acc_cls, mean_iu, fwavacc