def chunk(
        self,
        chunks: T_Chunks = {},  # noqa: B006  # even though it's unsafe, it is being used intentionally here (#4667)
        chunked_array_type: str | ChunkManagerEntrypoint[Any] | None = None,
        from_array_kwargs: Any = None,
        **chunks_kwargs: Any,
    ) -> Self:
        """Coerce this array's data into a dask array with the given chunks.

        If this variable is a non-dask array, it will be converted to dask
        array. If it's a dask array, it will be rechunked to the given chunk
        sizes.

        If neither chunks is not provided for one or more dimensions, chunk
        sizes along that dimension will not be updated; non-dask arrays will be
        converted into dask arrays with a single block.

        Parameters
        ----------
        chunks : int, tuple or dict, optional
            Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or
            ``{'x': 5, 'y': 5}``.
        chunked_array_type: str, optional
            Which chunked array type to coerce this datasets' arrays to.
            Defaults to 'dask' if installed, else whatever is registered via the `ChunkManagerEntrypoint` system.
            Experimental API that should not be relied upon.
        from_array_kwargs: dict, optional
            Additional keyword arguments passed on to the `ChunkManagerEntrypoint.from_array` method used to create
            chunked arrays, via whichever chunk manager is specified through the `chunked_array_type` kwarg.
            For example, with dask as the default chunked array type, this method would pass additional kwargs
            to :py:func:`dask.array.from_array`. Experimental API that should not be relied upon.
        **chunks_kwargs : {dim: chunks, ...}, optional
            The keyword arguments form of ``chunks``.
            One of chunks or chunks_kwargs must be provided.

        Returns
        -------
        chunked : xarray.Variable

        See Also
        --------
        Variable.chunks
        Variable.chunksizes
        xarray.unify_chunks
        dask.array.from_array
        """

        if from_array_kwargs is None:
            from_array_kwargs = {}

        if chunks is None:
            warnings.warn(
                "None value for 'chunks' is deprecated. "
                "It will raise an error in the future. Use instead '{}'",
                category=FutureWarning,
                stacklevel=2,
            )
            chunks = {}

        if isinstance(chunks, float | str | int | tuple | list):
            # TODO we shouldn't assume here that other chunkmanagers can handle these types
            # TODO should we call normalize_chunks here?
            pass  # dask.array.from_array can handle these directly
        else:
            chunks = either_dict_or_kwargs(chunks, chunks_kwargs, "chunk")

        if is_dict_like(chunks):
            # This method of iteration allows for duplicated dimension names, GH8579
            chunks = {
                dim_number: chunks[dim]
                for dim_number, dim in enumerate(self.dims)
                if dim in chunks
            }

        chunkmanager = guess_chunkmanager(chunked_array_type)

        data_old = self._data
        if chunkmanager.is_chunked_array(data_old):
            data_chunked = chunkmanager.rechunk(data_old, chunks)  # type: ignore[arg-type]
        else:
            ndata: duckarray[Any, Any]
            if not isinstance(data_old, ExplicitlyIndexed):
                ndata = data_old
            else:
                # Unambiguously handle array storage backends (like NetCDF4 and h5py)
                # that can't handle general array indexing. For example, in netCDF4 you
                # can do "outer" indexing along two dimensions independent, which works
                # differently from how NumPy handles it.
                # da.from_array works by using lazy indexing with a tuple of slices.
                # Using OuterIndexer is a pragmatic choice: dask does not yet handle
                # different indexing types in an explicit way:
                # https://github.com/dask/dask/issues/2883
                ndata = ImplicitToExplicitIndexingAdapter(data_old, OuterIndexer)  # type: ignore[assignment]

            if is_dict_like(chunks):
                chunks = tuple(chunks.get(n, s) for n, s in enumerate(ndata.shape))

            data_chunked = chunkmanager.from_array(ndata, chunks, **from_array_kwargs)  # type: ignore[arg-type]

        return self._replace(data=data_chunked)
Coerce this array's data into a dask array with the given chunks.

        If this variable is a non-dask array, it will be converted to dask
        array. If it's a dask array, it will be rechunked to the given chunk
        sizes.

        If neither chunks is not provided for one or more dimensions, chunk
        sizes along that dimension will not be updated; non-dask arrays will be
        converted into dask arrays with a single block.

        Parameters
        ----------
        chunks : int, tuple or dict, optional
            Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or
            ``{'x': 5, 'y': 5}``.
        chunked_array_type: str, optional
            Which chunked array type to coerce this datasets' arrays to.
            Defaults to 'dask' if installed, else whatever is registered via the `ChunkManagerEntrypoint` system.
            Experimental API that should not be relied upon.
        from_array_kwargs: dict, optional
            Additional keyword arguments passed on to the `ChunkManagerEntrypoint.from_array` method used to create
            chunked arrays, via whichever chunk manager is specified through the `chunked_array_type` kwarg.
            For example, with dask as the default chunked array type, this method would pass additional kwargs
            to :py:func:`dask.array.from_array`. Experimental API that should not be relied upon.
        **chunks_kwargs : {dim: chunks, ...}, optional
            The keyword arguments form of ``chunks``.
            One of chunks or chunks_kwargs must be provided.

        Returns
        -------
        chunked : xarray.Variable

        See Also
        --------
        Variable.chunks
        Variable.chunksizes
        xarray.unify_chunks
        dask.array.from_array
        
xarray/namedarray/core.py