class_docstr: Description
  -----------
  GraphSAINT with random walk sampler
  
  Parameters
  ----------
  num_roots : int
      the number of roots to generate random walks.
  length : int
      the length of each random walk.
class_docstr_tok: ["Description","--","--","--","--","--","-","GraphSAINT","with","random","walk","sampler","Parameters","--","--","--","--","--","num_roots",":","int","the","number","of","roots","to","generate","random","walks",".","length",":","int","the","length","of","each","random","walk","."]
code: def __generate_fn__(self):
      graph_fn = os.path.join(
          "./subgraphs/{}_RW_{}_{}_{}.npy".format(
              self.dn, self.num_roots, self.length, self.num_subg
          )
      )
      norm_fn = os.path.join(
          "./subgraphs/{}_RW_{}_{}_{}_norm.npy".format(
              self.dn, self.num_roots, self.length, 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 SAINTRandomWalkSampler obj's underlying member method code with example usage: obj.__generate_fn__() and returns: graph_fn,  norm_fn