class_docstr: Description
  -----------
  GraphSAINT with node sampler.
  
  Parameters
  ----------
  node_budget : int
      the expected number of nodes in each subgraph, which is specifically explained in the paper.
class_docstr_tok: ["Description","--","--","--","--","--","-","GraphSAINT","with","node","sampler",".","Parameters","--","--","--","--","--","node_budget",":","int","the","expected","number","of","nodes","in","each","subgraph",",","which","is","specifically","explained","in","the","paper","."]
code: def __generate_fn__(self):
      graph_fn = os.path.join(
          "./subgraphs/{}_Node_{}_{}.npy".format(
              self.dn, self.node_budget, self.num_subg
          )
      )
      norm_fn = os.path.join(
          "./subgraphs/{}_Node_{}_{}_norm.npy".format(
              self.dn, self.node_budget, self.num_subg
          )
      )
      return graph_fn, norm_fn
  
imports: import math
  import os
  import random
  import time
  import dgl
  import dgl.function
  import numpy
  import scipy
  import torch
  from dgl.sampling import pack_traces, random_walk
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling the SAINTNodeSampler obj's underlying member method code with example usage: obj.__generate_fn__() and returns: graph_fn,  norm_fn