def METHOD_NAME(A: SparseMatrix, B: Union[SparseMatrix, Scalar]) -> SparseMatrix:
    r"""Elementwise division for ``SparseMatrix``, equivalent to ``A / B``.
    If both :attr:`A` and :attr:`B` are sparse matrices, both of them should be
    diagonal matrices.
    Parameters
    ----------
    A : SparseMatrix
        Sparse matrix
    B : SparseMatrix or Scalar
        Sparse matrix or scalar value
    Returns
    -------
    SparseMatrix
        Sparse matrix
    Examples
    --------
    >>> A = dglsp.diag(torch.arange(1, 4))
    >>> B = dglsp.diag(torch.arange(10, 13))
    >>> dglsp.div(A, B)
    SparseMatrix(indices=tensor([[0, 1, 2],
                                 [0, 1, 2]]),
                 values=tensor([0.1000, 0.1818, 0.2500]),
                 shape=(3, 3), nnz=3)
    >>> A = dglsp.diag(torch.arange(1, 4))
    >>> dglsp.div(A, 2)
    SparseMatrix(indices=tensor([[0, 1, 2],
                                 [0, 1, 2]]),
                 values=tensor([0.5000, 1.0000, 1.5000]),
                 shape=(3, 3), nnz=3)
    >>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
    >>> val = torch.tensor([1, 2, 3])
    >>> A = dglsp.spmatrix(indices, val, shape=(3, 4))
    >>> dglsp.div(A, 2)
    SparseMatrix(indices=tensor([[1, 0, 2],
                                 [0, 3, 2]]),
                 values=tensor([0.5000, 1.0000, 1.5000]),
                 shape=(3, 4), nnz=3)
    """
    return A / B