def map_blocks(
        self,
        func: Callable[..., T_Xarray],
        args: Sequence[Any] = (),
        kwargs: Mapping[str, Any] | None = None,
        template: DataArray | Dataset | None = None,
    ) -> T_Xarray:
        """
        Apply a function to each block of this DataArray.

        .. warning::
            This method is experimental and its signature may change.

        Parameters
        ----------
        func : callable
            User-provided function that accepts a DataArray as its first
            parameter. The function will receive a subset or 'block' of this DataArray (see below),
            corresponding to one chunk along each chunked dimension. ``func`` will be
            executed as ``func(subset_dataarray, *subset_args, **kwargs)``.

            This function must return either a single DataArray or a single Dataset.

            This function cannot add a new chunked dimension.
        args : sequence
            Passed to func after unpacking and subsetting any xarray objects by blocks.
            xarray objects in args must be aligned with this object, otherwise an error is raised.
        kwargs : mapping
            Passed verbatim to func after unpacking. xarray objects, if any, will not be
            subset to blocks. Passing dask collections in kwargs is not allowed.
        template : DataArray or Dataset, optional
            xarray object representing the final result after compute is called. If not provided,
            the function will be first run on mocked-up data, that looks like this object but
            has sizes 0, to determine properties of the returned object such as dtype,
            variable names, attributes, new dimensions and new indexes (if any).
            ``template`` must be provided if the function changes the size of existing dimensions.
            When provided, ``attrs`` on variables in `template` are copied over to the result. Any
            ``attrs`` set by ``func`` will be ignored.

        Returns
        -------
        A single DataArray or Dataset with dask backend, reassembled from the outputs of the
        function.

        Notes
        -----
        This function is designed for when ``func`` needs to manipulate a whole xarray object
        subset to each block. Each block is loaded into memory. In the more common case where
        ``func`` can work on numpy arrays, it is recommended to use ``apply_ufunc``.

        If none of the variables in this object is backed by dask arrays, calling this function is
        equivalent to calling ``func(obj, *args, **kwargs)``.

        See Also
        --------
        :func:`dask.array.map_blocks <dask.array.map_blocks>`
        :func:`xarray.apply_ufunc <xarray.apply_ufunc>`
        :func:`xarray.Dataset.map_blocks <xarray.Dataset.map_blocks>`

        :doc:`xarray-tutorial:advanced/map_blocks/map_blocks`
            Advanced Tutorial on map_blocks with dask

        Examples
        --------
        Calculate an anomaly from climatology using ``.groupby()``. Using
        ``xr.map_blocks()`` allows for parallel operations with knowledge of ``xarray``,
        its indices, and its methods like ``.groupby()``.

        >>> def calculate_anomaly(da, groupby_type="time.month"):
        ...     gb = da.groupby(groupby_type)
        ...     clim = gb.mean(dim="time")
        ...     return gb - clim
        ...
        >>> time = xr.date_range("1990-01", "1992-01", freq="ME", use_cftime=True)
        >>> month = xr.DataArray(time.month, coords={"time": time}, dims=["time"])
        >>> np.random.seed(123)
        >>> array = xr.DataArray(
        ...     np.random.rand(len(time)),
        ...     dims=["time"],
        ...     coords={"time": time, "month": month},
        ... ).chunk()
        >>> array.map_blocks(calculate_anomaly, template=array).compute()
        <xarray.DataArray (time: 24)> Size: 192B
        array([ 0.12894847,  0.11323072, -0.0855964 , -0.09334032,  0.26848862,
                0.12382735,  0.22460641,  0.07650108, -0.07673453, -0.22865714,
               -0.19063865,  0.0590131 , -0.12894847, -0.11323072,  0.0855964 ,
                0.09334032, -0.26848862, -0.12382735, -0.22460641, -0.07650108,
                0.07673453,  0.22865714,  0.19063865, -0.0590131 ])
        Coordinates:
          * time     (time) object 192B 1990-01-31 00:00:00 ... 1991-12-31 00:00:00
            month    (time) int64 192B 1 2 3 4 5 6 7 8 9 10 ... 3 4 5 6 7 8 9 10 11 12

        Note that one must explicitly use ``args=[]`` and ``kwargs={}`` to pass arguments
        to the function being applied in ``xr.map_blocks()``:

        >>> array.map_blocks(
        ...     calculate_anomaly, kwargs={"groupby_type": "time.year"}, template=array
        ... )  # doctest: +ELLIPSIS
        <xarray.DataArray (time: 24)> Size: 192B
        dask.array<<this-array>-calculate_anomaly, shape=(24,), dtype=float64, chunksize=(24,), chunktype=numpy.ndarray>
        Coordinates:
          * time     (time) object 192B 1990-01-31 00:00:00 ... 1991-12-31 00:00:00
            month    (time) int64 192B dask.array<chunksize=(24,), meta=np.ndarray>
        """
        from xarray.core.parallel import map_blocks

        return map_blocks(func, self, args, kwargs, template)

        Apply a function to each block of this DataArray.

        .. warning::
            This method is experimental and its signature may change.

        Parameters
        ----------
        func : callable
            User-provided function that accepts a DataArray as its first
            parameter. The function will receive a subset or 'block' of this DataArray (see below),
            corresponding to one chunk along each chunked dimension. ``func`` will be
            executed as ``func(subset_dataarray, *subset_args, **kwargs)``.

            This function must return either a single DataArray or a single Dataset.

            This function cannot add a new chunked dimension.
        args : sequence
            Passed to func after unpacking and subsetting any xarray objects by blocks.
            xarray objects in args must be aligned with this object, otherwise an error is raised.
        kwargs : mapping
            Passed verbatim to func after unpacking. xarray objects, if any, will not be
            subset to blocks. Passing dask collections in kwargs is not allowed.
        template : DataArray or Dataset, optional
            xarray object representing the final result after compute is called. If not provided,
            the function will be first run on mocked-up data, that looks like this object but
            has sizes 0, to determine properties of the returned object such as dtype,
            variable names, attributes, new dimensions and new indexes (if any).
            ``template`` must be provided if the function changes the size of existing dimensions.
            When provided, ``attrs`` on variables in `template` are copied over to the result. Any
            ``attrs`` set by ``func`` will be ignored.

        Returns
        -------
        A single DataArray or Dataset with dask backend, reassembled from the outputs of the
        function.

        Notes
        -----
        This function is designed for when ``func`` needs to manipulate a whole xarray object
        subset to each block. Each block is loaded into memory. In the more common case where
        ``func`` can work on numpy arrays, it is recommended to use ``apply_ufunc``.

        If none of the variables in this object is backed by dask arrays, calling this function is
        equivalent to calling ``func(obj, *args, **kwargs)``.

        See Also
        --------
        :func:`dask.array.map_blocks <dask.array.map_blocks>`
        :func:`xarray.apply_ufunc <xarray.apply_ufunc>`
        :func:`xarray.Dataset.map_blocks <xarray.Dataset.map_blocks>`

        :doc:`xarray-tutorial:advanced/map_blocks/map_blocks`
            Advanced Tutorial on map_blocks with dask

        Examples
        --------
        Calculate an anomaly from climatology using ``.groupby()``. Using
        ``xr.map_blocks()`` allows for parallel operations with knowledge of ``xarray``,
        its indices, and its methods like ``.groupby()``.

        >>> def calculate_anomaly(da, groupby_type="time.month"):
        ...     gb = da.groupby(groupby_type)
        ...     clim = gb.mean(dim="time")
        ...     return gb - clim
        ...
        >>> time = xr.date_range("1990-01", "1992-01", freq="ME", use_cftime=True)
        >>> month = xr.DataArray(time.month, coords={"time": time}, dims=["time"])
        >>> np.random.seed(123)
        >>> array = xr.DataArray(
        ...     np.random.rand(len(time)),
        ...     dims=["time"],
        ...     coords={"time": time, "month": month},
        ... ).chunk()
        >>> array.map_blocks(calculate_anomaly, template=array).compute()
        <xarray.DataArray (time: 24)> Size: 192B
        array([ 0.12894847,  0.11323072, -0.0855964 , -0.09334032,  0.26848862,
                0.12382735,  0.22460641,  0.07650108, -0.07673453, -0.22865714,
               -0.19063865,  0.0590131 , -0.12894847, -0.11323072,  0.0855964 ,
                0.09334032, -0.26848862, -0.12382735, -0.22460641, -0.07650108,
                0.07673453,  0.22865714,  0.19063865, -0.0590131 ])
        Coordinates:
          * time     (time) object 192B 1990-01-31 00:00:00 ... 1991-12-31 00:00:00
            month    (time) int64 192B 1 2 3 4 5 6 7 8 9 10 ... 3 4 5 6 7 8 9 10 11 12

        Note that one must explicitly use ``args=[]`` and ``kwargs={}`` to pass arguments
        to the function being applied in ``xr.map_blocks()``:

        >>> array.map_blocks(
        ...     calculate_anomaly, kwargs={"groupby_type": "time.year"}, template=array
        ... )  # doctest: +ELLIPSIS
        <xarray.DataArray (time: 24)> Size: 192B
        dask.array<<this-array>-calculate_anomaly, shape=(24,), dtype=float64, chunksize=(24,), chunktype=numpy.ndarray>
        Coordinates:
          * time     (time) object 192B 1990-01-31 00:00:00 ... 1991-12-31 00:00:00
            month    (time) int64 192B dask.array<chunksize=(24,), meta=np.ndarray>
        
xarray/core/dataarray.py