Repository: scikit-learn/scikit-learn
File: sklearn/preprocessing/_label.py (Python)

Code before:

    def transform(self, y):
        """Transform multi-class labels to binary labels.

        The output of transform is sometimes referred to by some authors as
        the 1-of-K coding scheme.

        Parameters
        ----------
        y : {array, sparse matrix} of shape (n_samples,) or \
                (n_samples, n_classes)
            Target values. The 2-d matrix should only contain 0 and 1,
            represents multilabel classification. Sparse matrix can be
            CSR, CSC, COO, DOK, or LIL.

        Returns
        -------
        Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
            Shape will be (n_samples, 1) for binary problems. Sparse matrix
            will be of CSR format.
        """
        check_is_fitted(self)

        if self.sparse_output and not _is_numpy_namespace(get_namespace(y)[0]):
            raise ValueError(
                "`sparse_output=True` is not supported for Array API "
                "namespace {xp.__name__}. "
                "Use `sparse_output=False` to return a dense array instead."
            )

        y_is_multilabel = type_of_target(y).startswith("multilabel")
        if y_is_multilabel and not self.y_type_.startswith("multilabel"):
            raise ValueError("The object was not fitted with multilabel input.")

        return label_binarize(
            y,
            classes=self.classes_,
            pos_label=self.pos_label,
            neg_label=self.neg_label,
            sparse_output=self.sparse_output,
        )

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

        Parameters
        ----------
        Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
            Target values. All sparse matrices are converted to CSR before
            inverse transformation.


Review comment (suggestion):
I think the official casing convention is the following:

```suggestion
                "`sparse_output=True` is not supported for array API "
```

Code after:
        """
        return self.fit(y).transform(y)

    def transform(self, y):
        """Transform multi-class labels to binary labels.

        The output of transform is sometimes referred to by some authors as
        the 1-of-K coding scheme.

        Parameters
        ----------
        y : {array, sparse matrix} of shape (n_samples,) or \
                (n_samples, n_classes)
            Target values. The 2-d matrix should only contain 0 and 1,
            represents multilabel classification. Sparse matrix can be
            CSR, CSC, COO, DOK, or LIL.

        Returns
        -------
        Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
            Shape will be (n_samples, 1) for binary problems. Sparse matrix
            will be of CSR format.
        """
        check_is_fitted(self)

        xp, is_array_api = get_namespace(y)

        if is_array_api and self.sparse_output and not _is_numpy_namespace(xp):
            raise ValueError(
                "`sparse_output=True` is not supported for array API "
                f"namespace {xp.__name__}. "
                "Use `sparse_output=False` to return a dense array instead."
            )

        y_is_multilabel = type_of_target(y).startswith("multilabel")
        if y_is_multilabel and not self.y_type_.startswith("multilabel"):
            raise ValueError("The object was not fitted with multilabel input.")

        return label_binarize(
            y,
            classes=self.classes_,
            pos_label=self.pos_label,
            neg_label=self.neg_label,
            sparse_output=self.sparse_output,
        )

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

        Parameters
        ----------