code: def create_mask(x, N, device=None):
      if isinstance(x, torch.Tensor):
          x = x.data
      mask = np.zeros((len(x), N))
      for i in range(len(x)):
          mask[i, : x[i]] = 1
  
      return to_cuda(torch.Tensor(mask), device)
  
imports: import os
  import pickle
  from collections import OrderedDict
  import numpy
  import torch
  import torch.nn
  import yaml
  from scipy import sparse
  from sklearn import preprocessing
  from graph4nlp.pytorch.data.data import GraphData
label_desc: Use image node_id 6 for calling a global function with example usage: create_mask(x, N, device) and returns: to_cuda