def any(
        self,
        dim: Dims = None,
        *,
        keep_attrs: bool | None = None,
        **kwargs: Any,
    ) -> Self:
        """
        Reduce this DataTree's data by applying ``any`` along some dimension(s).

        Parameters
        ----------
        dim : str, Iterable of Hashable, "..." or None, default: None
            Name of dimension[s] along which to apply ``any``. For e.g. ``dim="x"``
            or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
        keep_attrs : bool or None, optional
            If True, ``attrs`` will be copied from the original
            object to the new one.  If False, the new object will be
            returned without attributes.
        **kwargs : Any
            Additional keyword arguments passed on to the appropriate array
            function for calculating ``any`` on this object's data.
            These could include dask-specific kwargs like ``split_every``.

        Returns
        -------
        reduced : DataTree
            New DataTree with ``any`` applied to its data and the
            indicated dimension(s) removed

        See Also
        --------
        numpy.any
        dask.array.any
        Dataset.any
        DataArray.any
        :ref:`agg`
            User guide on reduction or aggregation operations.

        Examples
        --------
        >>> dt = xr.DataTree(
        ...     xr.Dataset(
        ...         data_vars=dict(
        ...             foo=(
        ...                 "time",
        ...                 np.array([True, True, True, True, True, False], dtype=bool),
        ...             )
        ...         ),
        ...         coords=dict(
        ...             time=(
        ...                 "time",
        ...                 pd.date_range("2001-01-01", freq="ME", periods=6),
        ...             ),
        ...             labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
        ...         ),
        ...     ),
        ... )
        >>> dt
        <xarray.DataTree>
        Group: /
            Dimensions:  (time: 6)
            Coordinates:
              * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
                labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
            Data variables:
                foo      (time) bool 6B True True True True True False

        >>> dt.any()
        <xarray.DataTree>
        Group: /
            Dimensions:  ()
            Data variables:
                foo      bool 1B True
        """
        return self.reduce(
            duck_array_ops.array_any,
            dim=dim,
            numeric_only=False,
            keep_attrs=keep_attrs,
            **kwargs,
        )

        Reduce this DataTree's data by applying ``any`` along some dimension(s).

        Parameters
        ----------
        dim : str, Iterable of Hashable, "..." or None, default: None
            Name of dimension[s] along which to apply ``any``. For e.g. ``dim="x"``
            or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions.
        keep_attrs : bool or None, optional
            If True, ``attrs`` will be copied from the original
            object to the new one.  If False, the new object will be
            returned without attributes.
        **kwargs : Any
            Additional keyword arguments passed on to the appropriate array
            function for calculating ``any`` on this object's data.
            These could include dask-specific kwargs like ``split_every``.

        Returns
        -------
        reduced : DataTree
            New DataTree with ``any`` applied to its data and the
            indicated dimension(s) removed

        See Also
        --------
        numpy.any
        dask.array.any
        Dataset.any
        DataArray.any
        :ref:`agg`
            User guide on reduction or aggregation operations.

        Examples
        --------
        >>> dt = xr.DataTree(
        ...     xr.Dataset(
        ...         data_vars=dict(
        ...             foo=(
        ...                 "time",
        ...                 np.array([True, True, True, True, True, False], dtype=bool),
        ...             )
        ...         ),
        ...         coords=dict(
        ...             time=(
        ...                 "time",
        ...                 pd.date_range("2001-01-01", freq="ME", periods=6),
        ...             ),
        ...             labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
        ...         ),
        ...     ),
        ... )
        >>> dt
        <xarray.DataTree>
        Group: /
            Dimensions:  (time: 6)
            Coordinates:
              * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
                labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
            Data variables:
                foo      (time) bool 6B True True True True True False

        >>> dt.any()
        <xarray.DataTree>
        Group: /
            Dimensions:  ()
            Data variables:
                foo      bool 1B True
        
xarray/core/_aggregations.py