def _check_good_input(self, X, y=None):
        if isinstance(X, dict):
            lengths = [len(X1) for X1 in X.values()]
            if 1 > len(set(lengths)):
                raise ValueError("Not all values of X are of equal length.")
            x_len = lengths[0]
        else:
            x_len = len(X)

        if y is not None:
            if len(y) != x_len:
                raise ValueError("X and y are not of equal length.")

        if self.regression and y is not None and y.ndim == 1:
            y = y.reshape(-1, 1)

        return X, y
dataset/ETHPy150Open dnouri/nolearn/nolearn/lasagne/base.py/NeuralNet._check_good_input/swapped operands of `>`