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

        Parameters
        ----------
        dim : str, Iterable of Hashable, "..." or None, default: None
            Name of dimension[s] along which to apply ``all``. For e.g. ``dim="x"``
            or ``dim=["x", "y"]``. If None, will reduce over the Resample dimensions.
            If "...", 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 ``all`` on this object's data.
            These could include dask-specific kwargs like ``split_every``.

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

        See Also
        --------
        numpy.all
        dask.array.all
        DataArray.all
        :ref:`resampling`
            User guide on resampling operations.

        Notes
        -----
        Use the ``flox`` package to significantly speed up resampling computations,
        especially with dask arrays. Xarray will use flox by default if installed.
        Pass flox-specific keyword arguments in ``**kwargs``.
        See the `flox documentation <https://flox.readthedocs.io>`_ for more.

        Examples
        --------
        >>> da = xr.DataArray(
        ...     np.array([True, True, True, True, True, False], dtype=bool),
        ...     dims="time",
        ...     coords=dict(
        ...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
        ...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
        ...     ),
        ... )
        >>> da
        <xarray.DataArray (time: 6)> Size: 6B
        array([ True,  True,  True,  True,  True, False])
        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'

        >>> da.resample(time="3ME").all()
        <xarray.DataArray (time: 3)> Size: 3B
        array([ True,  True, False])
        Coordinates:
          * time     (time) datetime64[ns] 24B 2001-01-31 2001-04-30 2001-07-31
        """
        if (
            flox_available
            and OPTIONS["use_flox"]
            and contains_only_chunked_or_numpy(self._obj)
        ):
            return self._flox_reduce(
                func="all",
                dim=dim,
                # fill_value=fill_value,
                keep_attrs=keep_attrs,
                **kwargs,
            )
        else:
            return self.reduce(
                duck_array_ops.array_all,
                dim=dim,
                keep_attrs=keep_attrs,
                **kwargs,
            )

        Reduce this DataArray's data by applying ``all`` along some dimension(s).

        Parameters
        ----------
        dim : str, Iterable of Hashable, "..." or None, default: None
            Name of dimension[s] along which to apply ``all``. For e.g. ``dim="x"``
            or ``dim=["x", "y"]``. If None, will reduce over the Resample dimensions.
            If "...", 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 ``all`` on this object's data.
            These could include dask-specific kwargs like ``split_every``.

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

        See Also
        --------
        numpy.all
        dask.array.all
        DataArray.all
        :ref:`resampling`
            User guide on resampling operations.

        Notes
        -----
        Use the ``flox`` package to significantly speed up resampling computations,
        especially with dask arrays. Xarray will use flox by default if installed.
        Pass flox-specific keyword arguments in ``**kwargs``.
        See the `flox documentation <https://flox.readthedocs.io>`_ for more.

        Examples
        --------
        >>> da = xr.DataArray(
        ...     np.array([True, True, True, True, True, False], dtype=bool),
        ...     dims="time",
        ...     coords=dict(
        ...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
        ...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
        ...     ),
        ... )
        >>> da
        <xarray.DataArray (time: 6)> Size: 6B
        array([ True,  True,  True,  True,  True, False])
        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'

        >>> da.resample(time="3ME").all()
        <xarray.DataArray (time: 3)> Size: 3B
        array([ True,  True, False])
        Coordinates:
          * time     (time) datetime64[ns] 24B 2001-01-31 2001-04-30 2001-07-31
        
xarray/core/_aggregations.py