def unstack(
        self,
        dim: Dims = None,
        *,
        fill_value: Any = dtypes.NA,
        sparse: bool = False,
    ) -> Self:
        """
        Unstack existing dimensions corresponding to MultiIndexes into
        multiple new dimensions.

        New dimensions will be added at the end.

        Parameters
        ----------
        dim : str, Iterable of Hashable or None, optional
            Dimension(s) over which to unstack. By default unstacks all
            MultiIndexes.
        fill_value : scalar or dict-like, default: nan
            Value to be filled. If a dict-like, maps variable names to
            fill values. Use the data array's name to refer to its
            name. If not provided or if the dict-like does not contain
            all variables, the dtype's NA value will be used.
        sparse : bool, default: False
            Use sparse-array if True

        Returns
        -------
        unstacked : DataArray
            Array with unstacked data.

        Examples
        --------
        >>> arr = xr.DataArray(
        ...     np.arange(6).reshape(2, 3),
        ...     coords=[("x", ["a", "b"]), ("y", [0, 1, 2])],
        ... )
        >>> arr
        <xarray.DataArray (x: 2, y: 3)> Size: 48B
        array([[0, 1, 2],
               [3, 4, 5]])
        Coordinates:
          * x        (x) <U1 8B 'a' 'b'
          * y        (y) int64 24B 0 1 2
        >>> stacked = arr.stack(z=("x", "y"))
        >>> stacked.indexes["z"]
        MultiIndex([('a', 0),
                    ('a', 1),
                    ('a', 2),
                    ('b', 0),
                    ('b', 1),
                    ('b', 2)],
                   name='z')
        >>> roundtripped = stacked.unstack()
        >>> arr.identical(roundtripped)
        True

        See Also
        --------
        DataArray.stack
        """
        ds = self._to_temp_dataset().unstack(dim, fill_value=fill_value, sparse=sparse)
        return self._from_temp_dataset(ds)

        Unstack existing dimensions corresponding to MultiIndexes into
        multiple new dimensions.

        New dimensions will be added at the end.

        Parameters
        ----------
        dim : str, Iterable of Hashable or None, optional
            Dimension(s) over which to unstack. By default unstacks all
            MultiIndexes.
        fill_value : scalar or dict-like, default: nan
            Value to be filled. If a dict-like, maps variable names to
            fill values. Use the data array's name to refer to its
            name. If not provided or if the dict-like does not contain
            all variables, the dtype's NA value will be used.
        sparse : bool, default: False
            Use sparse-array if True

        Returns
        -------
        unstacked : DataArray
            Array with unstacked data.

        Examples
        --------
        >>> arr = xr.DataArray(
        ...     np.arange(6).reshape(2, 3),
        ...     coords=[("x", ["a", "b"]), ("y", [0, 1, 2])],
        ... )
        >>> arr
        <xarray.DataArray (x: 2, y: 3)> Size: 48B
        array([[0, 1, 2],
               [3, 4, 5]])
        Coordinates:
          * x        (x) <U1 8B 'a' 'b'
          * y        (y) int64 24B 0 1 2
        >>> stacked = arr.stack(z=("x", "y"))
        >>> stacked.indexes["z"]
        MultiIndex([('a', 0),
                    ('a', 1),
                    ('a', 2),
                    ('b', 0),
                    ('b', 1),
                    ('b', 2)],
                   name='z')
        >>> roundtripped = stacked.unstack()
        >>> arr.identical(roundtripped)
        True

        See Also
        --------
        DataArray.stack
        
xarray/core/dataarray.py