docstr: random walk sampler
  
  Parameter
  ---------
  G dgl.Graph : the input graph
  seeds torch.LongTensor : starting nodes
  walk_length int : walk length
docstr_tok: ["random","walk","sampler","Parameter","--","--","--","--","-","G","dgl.Graph",":","the","input","graph","seeds","torch.LongTensor",":","starting","nodes","walk_length","int",":","walk","length"]
code: def __init__(self, G, seeds, walk_length):
      """random walk sampler
  
      Parameter
      ---------
      G dgl.Graph : the input graph
      seeds torch.LongTensor : starting nodes
      walk_length int : walk length
      """
      self.G = G
      self.seeds = seeds
      self.walk_length = walk_length
  
imports: import os
  import pickle
  import random
  import time
  import dgl
  import numpy
  import scipy.sparse
  import torch
  from dgl.data.utils import _get_dgl_url, download, extract_archive, get_download_dir
  from torch.utils.data import DataLoader
  from utils import shuffle_walks
label_desc: Use image node_id 1 to create a new DeepwalkSampler object from inherited base classes: object with example: obj = DeepwalkSampler(G, seeds, walk_length)