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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def write_object_array(f, data, options):
 """ Writes an array of objects recursively. Writes the elements of the given object array recursively in the HDF5 Group ``options.group_for_references`` and returns an ``h5py.Reference`` array to all the elements. Parameters f : h5py.File The HDF5 file handle that is open. data : numpy.ndarray of objects Numpy object array to write the elements of. options : hdf5storage.core.Options hdf5storage options object. Returns ------- obj_array : numpy.ndarray of h5py.Reference A reference array pointing to all the elements written to the HDF5 file. For those that couldn't be written, the respective element points to the canonical empty. Raises ------ TypeNotMatlabCompatibleError If writing a type not compatible with MATLAB and `options.action_for_matlab_incompatible` is set to ``'error'``. See Also -------- read_object_array hdf5storage.Options.group_for_references h5py.Reference """

    # We need to grab the special reference dtype and make an empty
    # array to store all the references in.
    ref_dtype = h5py.special_dtype(ref=h5py.Reference)
    data_refs = np.zeros(shape=data.shape, dtype='object')

    # We need to make sure that the group to hold references is present,
    # and create it if it isn't.

    if options.group_for_references not in f:
        f.create_group(options.group_for_references)

    grp2 = f[options.group_for_references]

    if not isinstance(grp2, h5py.Group):
        del f[options.group_for_references]
        f.create_group(options.group_for_references)
        grp2 = f[options.group_for_references]

    # The Dataset 'a' needs to be present as the canonical empty. It is
    # just and np.uint32/64([0, 0]) with its a MATLAB_class of
    # 'canonical empty' and the 'MATLAB_empty' attribute set. If it
    # isn't present or is incorrectly formatted, it is created
    # truncating anything previously there.
    try:
        dset_a = grp2['a']
        if dset_a.shape != (2,) \
                or not dset_a.dtype.name.startswith('uint') \
                or np.any(dset_a[...] != np.uint64([0, 0])) \
                or get_attribute_string(dset_a, 'MATLAB_class') != \
                'canonical empty' \
                or get_attribute(dset_a, 'MATLAB_empty') != 1:
            del grp2['a']
            dset_a = grp2.create_dataset('a', data=np.uint64([0, 0]))
            set_attribute_string(dset_a, 'MATLAB_class',
                                 'canonical empty')
            set_attribute(dset_a, 'MATLAB_empty',
                          np.uint8(1))
    except:
        dset_a = grp2.create_dataset('a', data=np.uint64([0, 0]))
        set_attribute_string(dset_a, 'MATLAB_class',
                             'canonical empty')
        set_attribute(dset_a, 'MATLAB_empty',
                      np.uint8(1))

    # Go through all the elements of data and write them, gabbing their
    # references and putting them in data_refs. They will be put in
    # group_for_references, which is also what the H5PATH needs to be
    # set to if we are doing MATLAB compatibility (otherwise, the
    # attribute needs to be deleted). If an element can't be written
    # (doing matlab compatibility, but it isn't compatible with matlab
    # and action_for_matlab_incompatible option is True), the reference
    # to the canonical empty will be used for the reference array to
    # point to.
    grp2name = grp2.name
    for index, x in np.ndenumerate(data):
        name_for_ref = next_unused_name_in_group(grp2, 16)
        write_data(f, grp2, name_for_ref, x, None, options)
        try:
            dset = grp2[name_for_ref]
            data_refs[index] = dset.ref
            if options.matlab_compatible:
                set_attribute_string(dset,
                                     'H5PATH', grp2name)
            else:
                del_attribute(dset, 'H5PATH')
        except:
            data_refs[index] = dset_a.ref

    # Now, the dtype needs to be changed to the reference type and the
    # whole thing copied over to data_to_store.
    return data_refs.astype(ref_dtype).copy()