code: def register_chunk_type(type):
      """Register the given type as a valid chunk and downcast array type
  
      Parameters
      ----------
      type : type
          Duck array type to be registered as a type Dask can safely wrap as a chunk and
          to which Dask does not defer in arithmetic operations and NumPy
          functions/ufuncs.
  
      Notes
      -----
      A :py:class:`dask.array.Array` can contain any sufficiently "NumPy-like" array in
      its chunks. These are also referred to as "duck arrays" since they match the most
      important parts of NumPy's array API, and so, behave the same way when relying on
      duck typing.
  
      However, for multiple duck array types to interoperate properly, they need to
      properly defer to each other in arithmetic operations and NumPy functions/ufuncs
      according to a well-defined type casting hierarchy (
      `see NEP 13 <https://numpy.org/neps/nep-0013-ufunc-overrides.html#type-casting-hierarchy>`__
      ). In an effort to maintain this hierarchy, Dask defers to all other duck array
      types except those in its internal registry. By default, this registry contains
  
      * :py:class:`numpy.ndarray`
      * :py:class:`numpy.ma.MaskedArray`
      * :py:class:`cupy.ndarray`
      * :py:class:`sparse.SparseArray`
      * :py:class:`scipy.sparse.spmatrix`
  
      This function exists to append any other types to this registry. If a type is not
      in this registry, and yet is a downcast type (it comes below
      :py:class:`dask.array.Array` in the type casting hierarchy), a ``TypeError`` will
      be raised due to all operand types returning ``NotImplemented``.
  
      Examples
      --------
      Using a mock ``FlaggedArray`` class as an example chunk type unknown to Dask with
      minimal duck array API:
  
      >>> import numpy.lib.mixins
      >>> class FlaggedArray(numpy.lib.mixins.NDArrayOperatorsMixin):
      ...     def __init__(self, a, flag=False):
      ...         self.a = a
      ...         self.flag = flag
      ...     def __repr__(self):
      ...         return f"Flag: {self.flag}, Array: " + repr(self.a)
      ...     def __array__(self):
      ...         return np.asarray(self.a)
      ...     def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
      ...         if method == '__call__':
      ...             downcast_inputs = []
      ...             flag = False
      ...             for input in inputs:
      ...                 if isinstance(input, self.__class__):
      ...                     flag = flag or input.flag
      ...                     downcast_inputs.append(input.a)
      ...                 elif isinstance(input, np.ndarray):
      ...                     downcast_inputs.append(input)
      ...                 else:
      ...                     return NotImplemented
      ...             return self.__class__(ufunc(*downcast_inputs, **kwargs), flag)
      ...         else:
      ...             return NotImplemented
      ...     @property
      ...     def shape(self):
      ...         return self.a.shape
      ...     @property
      ...     def ndim(self):
      ...         return self.a.ndim
      ...     @property
      ...     def dtype(self):
      ...         return self.a.dtype
      ...     def __getitem__(self, key):
      ...         return type(self)(self.a[key], self.flag)
      ...     def __setitem__(self, key, value):
      ...         self.a[key] = value
  
      Before registering ``FlaggedArray``, both types will attempt to defer to the
      other:
  
      >>> import dask.array as da
      >>> da.ones(5) - FlaggedArray(np.ones(5), True)
      Traceback (most recent call last):
      ...
      TypeError: operand type(s) all returned NotImplemented ...
  
      However, once registered, Dask will be able to handle operations with this new
      type:
  
      >>> da.register_chunk_type(FlaggedArray)
      >>> x = da.ones(5) - FlaggedArray(np.ones(5), True)
      >>> x
      dask.array<sub, shape=(5,), dtype=float64, chunksize=(5,), chunktype=dask.FlaggedArray>
      >>> x.compute()
      Flag: True, Array: array([0., 0., 0., 0., 0.])
      """
      _HANDLED_CHUNK_TYPES.append(type)
  
imports: from __future__ import annotations
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: register_chunk_type(type) without return types