code: def _aug_data(self, data):
      node_num, feat_dim = data.x.size()
      mask_num = int(node_num * self.aug_ratio)
  
      token = data.x.mean(dim=0)
      idx_mask = np.random.choice(node_num, mask_num, replace=False)
      data.x[idx_mask] = torch.tensor(token, dtype=torch.float32)
  
      return data
  
imports: import random
  import torch
  import numpy
  from itertools import repeat, product
  from torch_geometric.data import Batch
label_desc: Use image node_id 2 for calling the MaskNode obj's underlying member method code with example usage: obj._aug_data(data) and returns: data