code: def generate_mask_tensor(mask):
      """Generate mask tensor according to different backend
      For torch and tensorflow, it will create a bool tensor
      For mxnet, it will create a float tensor
      Parameters
      ----------
      mask: numpy ndarray
          input mask tensor
      """
      assert isinstance(mask, np.ndarray), (
          "input for generate_mask_tensor" "should be an numpy ndarray"
      )
      if F.backend_name == "mxnet":
          return F.tensor(mask, dtype=F.data_type_dict["float32"])
      else:
          return F.tensor(mask, dtype=F.data_type_dict["bool"])
  
imports: from __future__ import absolute_import
  import errno
  import hashlib
  import os
  import pickle
  import sys
  import warnings
  import networkx.algorithms
  import numpy
  import requests
  from tqdm import tqdm
  from ..None import backend
  from .graph_serialize import load_graphs, load_labels, save_graphs
  from .tensor_serialize import load_tensors, save_tensors
label_desc: Use image node_id 15 for calling a global function with example usage: generate_mask_tensor(mask) and returns: F, F