class_docstr: LINE is a graph embedding model.
  
  We implement the model to train users and items embedding for recommendation.
code: def random_num(self, num):
      value_id = []
      self.random_pr %= self.random_list_length
      while True:
          if self.random_pr + num <= self.random_list_length:
              value_id.append(
                  self.random_list[
                      self.random_pr : self.random_pr + num
                  ]
              )
              self.random_pr += num
              break
          else:
              value_id.append(self.random_list[self.random_pr :])
              num -= self.random_list_length - self.random_pr
              self.random_pr = 0
              np.random.shuffle(self.random_list)
      return np.concatenate(value_id)
  
imports: import random
  import numpy
  import torch
  import torch.nn
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils import InputType
label_desc: Use image node_id 4 for calling the LINE obj's underlying member method code with example usage: obj.random_num(num) and returns: np