class_docstr: Description
  -----------
  GraphSAINT with edge sampler.
  
  Parameters
  ----------
  edge_budget : int
      the expected number of edges in each subgraph, which is specifically explained in the paper.
class_docstr_tok: ["Description","--","--","--","--","--","-","GraphSAINT","with","edge","sampler",".","Parameters","--","--","--","--","--","edge_budget",":","int","the","expected","number","of","edges","in","each","subgraph",",","which","is","specifically","explained","in","the","paper","."]
code: def __init__(self, edge_budget, **kwargs):
      self.edge_budget = edge_budget
      self.rng = np.random.default_rng()
  
      super(SAINTEdgeSampler, self).__init__(
          node_budget=edge_budget * 2, **kwargs
      )
  
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 1 to create a new SAINTEdgeSampler object from inherited base classes: SAINTSampler with example: obj = SAINTEdgeSampler(edge_budget)