def sampled_csc(
        self,
    ) -> Union[CSCFormatBase, Dict[str, CSCFormatBase],]:
        """Returns the node pairs representing edges in csc format.
          - If `sampled_csc` is a CSCFormatBase: It should be in the csc
            format. `indptr` stores the index in the data array where each
            column starts. `indices` stores the row indices of the non-zero
            elements.
          - If `sampled_csc` is a dictionary: The keys should be edge type and
            the values should be corresponding node pairs. The ids inside is
            heterogeneous ids.

        Examples
        --------
        1. Homogeneous graph.

        >>> import dgl.graphbolt as gb
        >>> import torch
        >>> sampled_csc = gb.CSCFormatBase(
        ...     indptr=torch.tensor([0, 1, 2, 3]),
        ...     indices=torch.tensor([0, 1, 2]))
        >>> print(sampled_csc)
        CSCFormatBase(indptr=tensor([0, 1, 2, 3]),
                    indices=tensor([0, 1, 2]),
        )

        2. Heterogeneous graph.

        >>> sampled_csc = {"A:relation:B": gb.CSCFormatBase(
        ...     indptr=torch.tensor([0, 1, 2, 3]),
        ...     indices=torch.tensor([0, 1, 2]))}
        >>> print(sampled_csc)
        {'A:relation:B': CSCFormatBase(indptr=tensor([0, 1, 2, 3]),
                    indices=tensor([0, 1, 2]),
        )}
        """
        raise NotImplementedError
Returns the node pairs representing edges in csc format.
          - If `sampled_csc` is a CSCFormatBase: It should be in the csc
            format. `indptr` stores the index in the data array where each
            column starts. `indices` stores the row indices of the non-zero
            elements.
          - If `sampled_csc` is a dictionary: The keys should be edge type and
            the values should be corresponding node pairs. The ids inside is
            heterogeneous ids.

        Examples
        --------
        1. Homogeneous graph.

        >>> import dgl.graphbolt as gb
        >>> import torch
        >>> sampled_csc = gb.CSCFormatBase(
        ...     indptr=torch.tensor([0, 1, 2, 3]),
        ...     indices=torch.tensor([0, 1, 2]))
        >>> print(sampled_csc)
        CSCFormatBase(indptr=tensor([0, 1, 2, 3]),
                    indices=tensor([0, 1, 2]),
        )

        2. Heterogeneous graph.

        >>> sampled_csc = {"A:relation:B": gb.CSCFormatBase(
        ...     indptr=torch.tensor([0, 1, 2, 3]),
        ...     indices=torch.tensor([0, 1, 2]))}
        >>> print(sampled_csc)
        {'A:relation:B': CSCFormatBase(indptr=tensor([0, 1, 2, 3]),
                    indices=tensor([0, 1, 2]),
        )}
        
python/dgl/graphbolt/sampled_subgraph.py