code: def sample_function(
      user_train,
      usernum,
      itemnum,
      batch_size,
      maxlen,
      result_queue,
      seed,
  ):
      """Batch sampler that creates a sequence of negative items based on the
      original sequence of items (positive) that the user has interacted with.
  
      Args:
          user_train (dict): dictionary of training exampled for each user
          usernum (int): number of users
          itemnum (int): number of items
          batch_size (int): batch size
          maxlen (int): maximum input sequence length
          result_queue (multiprocessing.Queue): queue for storing sample results
          seed (int): seed for random generator
      """
  
      def sample():
          user = np.random.randint(1, usernum + 1)
          while len(user_train[user]) <= 1:
              user = np.random.randint(1, usernum + 1)
  
          seq = np.zeros([maxlen], dtype=np.int32)
          pos = np.zeros([maxlen], dtype=np.int32)
          neg = np.zeros([maxlen], dtype=np.int32)
          nxt = user_train[user][-1]
          idx = maxlen - 1
  
          ts = set(user_train[user])
          for i in reversed(user_train[user][:-1]):
              seq[idx] = i
              pos[idx] = nxt
              if nxt != 0:
                  neg[idx] = random_neq(1, itemnum + 1, ts)
              nxt = i
              idx -= 1
              if idx == -1:
                  break
  
          return (user, seq, pos, neg)
  
      np.random.seed(seed)
      while True:
          one_batch = []
          for i in range(batch_size):
              one_batch.append(sample())
  
          result_queue.put(zip(*one_batch))
  
file_path: recommenders/recommenders/models/sasrec/sampler.py
imports: import numpy
  from multiprocessing import Process, Queue
label_desc: Use image node_id 2 for calling a global function with example usage: sample_function(user_train, usernum, itemnum, batch_size, maxlen, result_queue, seed) and returns: user,  seq,  pos,  neg