code: def div(
      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
  
file_path: dgl/python/dgl/sparse/elementwise_op.py
imports: from typing import Union
  from .sparse_matrix import SparseMatrix
  from .utils import Scalar
label_desc: Use image node_id 4 for calling a global function with example usage: div(A, B) and returns: unknown