def compact(
        self,
        dim: int,
        leading_indices: Optional[torch.Tensor] = None,
    ):
        """Compact sparse matrix by removing rows or columns without non-zero
        elements in the sparse matrix and relabeling indices of the dimension.

        This function serves a dual purpose: it allows you to reorganize the
        indices within a specific dimension (rows or columns) of the sparse
        matrix and, if needed, place certain 'leading_indices' at the beginning
        of the relabeled dimension.

        In the absence of 'leading_indices' (when it's set to `None`), the order
        of relabeled indices remains the same as the original order, except that
        rows or columns without non-zero elements are removed. When
        'leading_indices' are provided, they are positioned at the start of the
        relabeled dimension. To be precise, all rows selected by the specified
        indices will be remapped from 0 to length(indices) - 1. Rows that are not
        selected and contain any non-zero elements will be positioned after those
        remapped rows while maintaining their original order.

        This function mimics 'dgl.to_block', a method used to compress a sampled
        subgraph by eliminating redundant nodes. The 'leading_indices' parameter
        replicates the behavior of 'include_dst_in_src' in 'dgl.to_block',
        adding destination node information for message passing.
        Setting 'leading_indices' to column IDs when relabeling the row
        dimension, for example, achieves the same effect as including destination
        nodes in source nodes.

        Parameters
        ----------
        dim : int
            The dimension to relabel. Should be 0 or 1. Use `dim = 0` for rowwise
            relabeling and `dim = 1` for columnwise relabeling.
        leading_indices : torch.Tensor, optional
            An optional tensor containing row or column ids that should be placed
            at the beginning of the relabeled dimension.

        Returns
        -------
        Tuple[SparseMatrix, torch.Tensor]
            A tuple containing the relabeled sparse matrix and the index mapping
            of the relabeled dimension from the new index to the original index.

        Examples
        --------
        >>> indices = torch.tensor([[0, 2],
                                    [1, 2]])
        >>> A = dglsp.spmatrix(indices)
        >>> print(A.to_dense())
        tensor([[0., 1., 0.],
                [0., 0., 0.],
                [0., 0., 1.]])

        Case 1: Compact rows without indices.

        >>> B, original_rows = A.compact(dim=0, leading_indices=None)
        >>> print(B.to_dense())
        tensor([[0., 1., 0.],
                [0., 0., 1.]])
        >>> print(original_rows)
        torch.Tensor([0, 2])

        Case 2: Compact rows with indices.

        >>> B, original_rows = A.compact(dim=0, leading_indices=[1, 2])
        >>> print(B.to_dense())
        tensor([[0., 0., 0.],
                [0., 0., 1.],
                [0., 1., 0.],])
        >>> print(original_rows)
        torch.Tensor([1, 2, 0])
        """
        mat, idx = torch.ops.dgl_sparse.compact(
            self.c_sparse_matrix, dim, leading_indices
        )
        return SparseMatrix(mat), idx
Compact sparse matrix by removing rows or columns without non-zero
        elements in the sparse matrix and relabeling indices of the dimension.

        This function serves a dual purpose: it allows you to reorganize the
        indices within a specific dimension (rows or columns) of the sparse
        matrix and, if needed, place certain 'leading_indices' at the beginning
        of the relabeled dimension.

        In the absence of 'leading_indices' (when it's set to `None`), the order
        of relabeled indices remains the same as the original order, except that
        rows or columns without non-zero elements are removed. When
        'leading_indices' are provided, they are positioned at the start of the
        relabeled dimension. To be precise, all rows selected by the specified
        indices will be remapped from 0 to length(indices) - 1. Rows that are not
        selected and contain any non-zero elements will be positioned after those
        remapped rows while maintaining their original order.

        This function mimics 'dgl.to_block', a method used to compress a sampled
        subgraph by eliminating redundant nodes. The 'leading_indices' parameter
        replicates the behavior of 'include_dst_in_src' in 'dgl.to_block',
        adding destination node information for message passing.
        Setting 'leading_indices' to column IDs when relabeling the row
        dimension, for example, achieves the same effect as including destination
        nodes in source nodes.

        Parameters
        ----------
        dim : int
            The dimension to relabel. Should be 0 or 1. Use `dim = 0` for rowwise
            relabeling and `dim = 1` for columnwise relabeling.
        leading_indices : torch.Tensor, optional
            An optional tensor containing row or column ids that should be placed
            at the beginning of the relabeled dimension.

        Returns
        -------
        Tuple[SparseMatrix, torch.Tensor]
            A tuple containing the relabeled sparse matrix and the index mapping
            of the relabeled dimension from the new index to the original index.

        Examples
        --------
        >>> indices = torch.tensor([[0, 2],
                                    [1, 2]])
        >>> A = dglsp.spmatrix(indices)
        >>> print(A.to_dense())
        tensor([[0., 1., 0.],
                [0., 0., 0.],
                [0., 0., 1.]])

        Case 1: Compact rows without indices.

        >>> B, original_rows = A.compact(dim=0, leading_indices=None)
        >>> print(B.to_dense())
        tensor([[0., 1., 0.],
                [0., 0., 1.]])
        >>> print(original_rows)
        torch.Tensor([0, 2])

        Case 2: Compact rows with indices.

        >>> B, original_rows = A.compact(dim=0, leading_indices=[1, 2])
        >>> print(B.to_dense())
        tensor([[0., 0., 0.],
                [0., 0., 1.],
                [0., 1., 0.],])
        >>> print(original_rows)
        torch.Tensor([1, 2, 0])
        
python/dgl/sparse/sparse_matrix.py