code: def dense_mask(dense, sparse):
      ret = torch.zeros_like(dense)
      row, col = sparse.coo()
      for r, c in zip(row, col):
          ret[r, c] = dense[r, c]
      return ret
  
file_path: dgl/tests/python/pytorch/sparse/utils.py
imports: import numpy
  import torch
  from dgl.sparse import diag, from_csc, from_csr, SparseMatrix, spmatrix
label_desc: Use image node_id 12 for calling a global function with example usage: dense_mask(dense, sparse) and returns: ret