code: def sparse_dense_mul(self, s, d):
      i = s._indices()
      v = s._values()
      dv = d[
          i[0, :], i[1, :]
      ]  # get values from relevant entries of dense matrix
      return torch.sparse.FloatTensor(i, v * dv, s.size())
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  from typing import Text, Union
  import copy
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from ...contrib.model.pytorch_lstm import LSTMModel
  from ...contrib.model.pytorch_gru import GRUModel
label_desc: Use image node_id 3 for calling the IGMTFModel obj's underlying member method code with example usage: obj.sparse_dense_mul(s, d) and returns: torch