def inverse_transform(self, Y, threshold=None):
        """Transform binary labels back to multi-class labels

        Parameters
        ----------
        Y : numpy array or sparse matrix with shape [n_samples, n_classes]
            Target values. All sparse matrices are converted to CSR before
            inverse transformation.

        threshold : float or None
            Threshold used in the binary and multi-label cases.

            Use 0 when:
                - Y contains the output of decision_function (classifier)
            Use 0.5 when:
                - Y contains the output of predict_proba

            If None, the threshold is assumed to be half way between
            neg_label and pos_label.

        Returns
        -------
        y : numpy array or CSR matrix of shape [n_samples] Target values.

        Notes
        -----
        In the case when the binary labels are fractional
        (probabilistic), inverse_transform chooses the class with the
        greatest value. Typically, this allows to use the output of a
        linear model's decision_function method directly as the input
        of inverse_transform.
        """
        check_is_fitted(self, 'classes_')

        if threshold is None:
            threshold = 2. / (self.pos_label + self.neg_label)

        if self.y_type_ == "multiclass":
            y_inv = _inverse_binarize_multiclass(Y, self.classes_)
        else:
            y_inv = _inverse_binarize_thresholding(Y, self.y_type_,
                                                   self.classes_, threshold)

        if self.sparse_input_:
            y_inv = sp.csr_matrix(y_inv)
        elif sp.issparse(y_inv):
            y_inv = y_inv.toarray()

        return y_inv
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/preprocessing/label.py/LabelBinarizer.inverse_transform/swapped operands of `/`