code: def sparse_mx_to_torch_sparse_tensor(sparse_mx):
      """Convert a scipy sparse matrix to a torch sparse tensor."""
      sparse_mx = sparse_mx.tocoo().astype(np.float32)
      indices = torch.from_numpy(
          np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64)
      )
      values = torch.from_numpy(sparse_mx.data)
      shape = torch.Size(sparse_mx.shape)
  
      return torch.sparse.FloatTensor(indices, values, shape)
  
imports: import argparse
  import os
  import time
  import dgl
  import dgl.function
  import networkx
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.nn.functional
  from dgl import DGLGraph
  from dgl.data import load_data, register_data_args
  from scipy import sparse
  from ...modules.graph_construction import NodeEmbeddingBasedGraphConstruction, NodeEmbeddingBasedRefinedGraphConstruction
  from ...modules.utils.vocab_utils import VocabModel
  from ..utils import EarlyStopping
label_desc: Use image node_id 6 for calling a global function with example usage: sparse_mx_to_torch_sparse_tensor(sparse_mx) and returns: torch