Repository: pandas-dev/pandas
File: pandas/core/indexes/multi.py (Python)

Code before:
                masked = []
                for new_label in taken:
                    label_values = new_label
                    label_values[mask] = na_value
                    masked.append(np.asarray(label_values))
                taken = masked

        return MultiIndex(
            levels=self.levels, codes=taken, names=self.names, verify_integrity=False
        )

    def append(self, other):
        """
        Append a collection of Index options together.

        The `append` method is used to combine multiple `Index` objects into a single
        `Index`. This is particularly useful when dealing with multi-level indexing
        (MultiIndex) where you might need to concatenate different levels of indices.
        The method handles the alignment of the levels and codes of the indices being
        appended to ensure consistency in the resulting `MultiIndex`.

        Parameters
        ----------
        other : Index or list/tuple of indices
            Index or list/tuple of Index objects to be appended.

        Returns
        -------
        Index
            The combined index.

        See Also
        --------
        MultiIndex: A multi-level, or hierarchical, index object for pandas objects.
        Index.append : Append a collection of Index options together.
        concat : Concatenate pandas objects along a particular axis.

        Examples
        --------
        >>> mi = pd.MultiIndex.from_arrays([["a"], ["b"]])
        >>> mi
        MultiIndex([('a', 'b')],
                   )
        >>> mi.append(mi)
        MultiIndex([('a', 'b'), ('a', 'b')],
                   )
        """
        if not isinstance(other, (list, tuple)):
            other = [other]

        if all(

Review comment (suggestion):
If you're interested in follow with with another PR, could you change these examples to use a `MultiIndex` instead of an `Index`

Code after:
            * True: negative values in `indices` indicate
            missing values. These values are set to `fill_value`. Any other
            other negative values raise a ``ValueError``.

        fill_value : scalar, default None
            If allow_fill=True and fill_value is not None, indices specified by
            -1 are regarded as NA. If Index doesn't hold NA, raise ValueError.
        **kwargs
            Required for compatibility with numpy.

        Returns
        -------
        Index
            An index formed of elements at the given indices. Will be the same
            type as self, except for RangeIndex.

        See Also
        --------
        numpy.ndarray.take: Return an array formed from the
            elements of a at the given indices.

        Examples
        --------
        >>> idx = pd.Index(["a", "b", "c"])
        >>> idx.take([2, 2, 1, 2])
        Index(['c', 'c', 'b', 'c'], dtype='str')
        """
        nv.validate_take((), kwargs)
        indices = ensure_platform_int(indices)

        # only fill if we are passing a non-None fill_value
        allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices)

        if indices.ndim == 1 and lib.is_range_indexer(indices, len(self)):
            return self.copy()

        na_value = -1

        taken = [lab.take(indices) for lab in self.codes]
        if allow_fill:
            mask = indices == -1
            if mask.any():
                masked = []
                for new_label in taken:
                    label_values = new_label
                    label_values[mask] = na_value
                    masked.append(np.asarray(label_values))
                taken = masked

        return MultiIndex(
            levels=self.levels, codes=taken, names=self.names, verify_integrity=False