def num_nodes(self) -> Union[int, Dict[str, int]]:
        """The number of nodes in the graph.
        - If the graph is homogenous, returns an integer.
        - If the graph is heterogenous, returns a dictionary.

        Returns
        -------
        Union[int, Dict[str, int]]
            The number of nodes. Integer indicates the total nodes number of a
            homogenous graph; dict indicates nodes number per node types of a
            heterogenous graph.

        Examples
        --------
        >>> import dgl.graphbolt as gb, torch
        >>> total_num_nodes = 5
        >>> total_num_edges = 12
        >>> ntypes = {"N0": 0, "N1": 1}
        >>> etypes = {"N0:R0:N0": 0, "N0:R1:N1": 1,
        ...     "N1:R2:N0": 2, "N1:R3:N1": 3}
        >>> indptr = torch.LongTensor([0, 3, 5, 7, 9, 12])
        >>> indices = torch.LongTensor([0, 1, 4, 2, 3, 0, 1, 1, 2, 0, 3, 4])
        >>> node_type_offset = torch.LongTensor([0, 2, 5])
        >>> type_per_edge = torch.LongTensor(
        ...     [0, 0, 2, 2, 2, 1, 1, 1, 3, 1, 3, 3])
        >>> graph = gb.fused_csc_sampling_graph(indptr, indices,
        ...     node_type_offset=node_type_offset,
        ...     type_per_edge=type_per_edge,
        ...     node_type_to_id=ntypes,
        ...     edge_type_to_id=etypes)
        >>> print(graph.num_nodes)
        {'N0': 2, 'N1': 3}
        """

        offset = self._node_type_offset_list

        # Homogenous.
        if offset is None or self.node_type_to_id is None:
            return self._c_csc_graph.num_nodes()

        # Heterogenous
        else:
            num_nodes_per_type = {
                _type: offset[_idx + 1] - offset[_idx]
                for _type, _idx in self.node_type_to_id.items()
            }

            return num_nodes_per_type
The number of nodes in the graph.
        - If the graph is homogenous, returns an integer.
        - If the graph is heterogenous, returns a dictionary.

        Returns
        -------
        Union[int, Dict[str, int]]
            The number of nodes. Integer indicates the total nodes number of a
            homogenous graph; dict indicates nodes number per node types of a
            heterogenous graph.

        Examples
        --------
        >>> import dgl.graphbolt as gb, torch
        >>> total_num_nodes = 5
        >>> total_num_edges = 12
        >>> ntypes = {"N0": 0, "N1": 1}
        >>> etypes = {"N0:R0:N0": 0, "N0:R1:N1": 1,
        ...     "N1:R2:N0": 2, "N1:R3:N1": 3}
        >>> indptr = torch.LongTensor([0, 3, 5, 7, 9, 12])
        >>> indices = torch.LongTensor([0, 1, 4, 2, 3, 0, 1, 1, 2, 0, 3, 4])
        >>> node_type_offset = torch.LongTensor([0, 2, 5])
        >>> type_per_edge = torch.LongTensor(
        ...     [0, 0, 2, 2, 2, 1, 1, 1, 3, 1, 3, 3])
        >>> graph = gb.fused_csc_sampling_graph(indptr, indices,
        ...     node_type_offset=node_type_offset,
        ...     type_per_edge=type_per_edge,
        ...     node_type_to_id=ntypes,
        ...     edge_type_to_id=etypes)
        >>> print(graph.num_nodes)
        {'N0': 2, 'N1': 3}
        
python/dgl/graphbolt/impl/fused_csc_sampling_graph.py