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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 get_structure_components(bonded_structure, inc_orientation=False, inc_site_ids=False, inc_molecule_graph=False):
 """ Gets information on the components in a bonded structure. Correctly determines the dimensionality of all structures, regardless of structure type or improper connections due to periodic boundary conditions. Requires a StructureGraph object as input. This can be generated using one of the NearNeighbor classes. For example, using the CrystalNN class:: bonded_structure = CrystalNN().get_bonded_structure(structure) Based on the modified breadth-first-search algorithm described in: P. Larsem, M. Pandey, M. Strange, K. W. Jacobsen, 2018, arXiv:1808.02114 Args: bonded_structure (StructureGraph):
 A structure with bonds, represented as a pymatgen structure graph. For example, generated using the CrystalNN.get_bonded_structure() method. inc_orientation (bool, optional):
 Whether to include the orientation of the structure component. For surfaces, the miller index is given, for one-dimensional structures, the direction of the chain is given. inc_site_ids (bool, optional):
 Whether to include the site indices of the sites in the structure component. inc_molecule_graph (bool, optional):
 Whether to include MoleculeGraph objects for zero-dimensional components. Returns: (list of dict):
 Information on the components in a structure as a list of dictionaries with the keys: - "structure_graph": A pymatgen StructureGraph object for the component. - "dimensionality": The dimensionality of the structure component as an int. - "orientation": If inc_orientation is `True`, the orientation of the component as a tuple. E.g. (1, 1, 1) - "site_ids": If inc_site_ids is `True`, the site indices of the sites in the component as a tuple. - "molecule_graph": If inc_molecule_graph is `True`, the site a MoleculeGraph object for zero-dimensional components. """

    import networkx as nx  # optional dependency therefore not top level import

    comp_graphs = (bonded_structure.graph.subgraph(c) for c in
                   nx.weakly_connected_components(bonded_structure.graph))

    components = []
    for graph in comp_graphs:
        dimensionality, vertices = calculate_dimensionality_of_site(
            bonded_structure, list(graph.nodes())[0], inc_vertices=True)

        component = {'dimensionality': dimensionality}

        if inc_orientation:
            if dimensionality in [1, 2]:
                vertices = np.array(vertices)

                g = vertices.sum(axis=0) / vertices.shape[0]

                # run singular value decomposition
                _, _, vh = np.linalg.svd(vertices - g)

                # get direction (first column is best fit line,
                # 3rd column is unitary norm)
                index = 2 if dimensionality == 2 else 0
                orientation = get_integer_index(vh[index, :])
            else:
                orientation = None

            component['orientation'] = orientation

        if inc_site_ids:
            component['site_ids'] = tuple(graph.nodes())

        if inc_molecule_graph and dimensionality == 0:
            component['molecule_graph'] = zero_d_graph_to_molecule_graph(
                bonded_structure, graph)

        component_structure = Structure.from_sites(
            [bonded_structure.structure[n] for n in sorted(graph.nodes())])

        sorted_graph = nx.convert_node_labels_to_integers(
            graph, ordering="sorted")
        component_graph = StructureGraph(
            component_structure,
            graph_data=json_graph.adjacency_data(sorted_graph))
        component['structure_graph'] = component_graph

        components.append(component)
    return components