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)
  
file_path: graph4nlp/graph4nlp/pytorch/test/link_prediction/utils.py
imports: import pickle
  import networkx
  import numpy
  import scipy.sparse
  import torch
  from sklearn.metrics import average_precision_score, roc_auc_score
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