class_docstr: Sampler object that creates an iterator for feeding batch data while training.
  
  Attributes:
      User: dict, all the users (keys) with items as values
      usernum: integer, total number of users
      itemnum: integer, total number of items
      batch_size (int): batch size
      maxlen (int): maximum input sequence length
      n_workers (int): number of workers for parallel execution
code: def next_batch(self):
      return self.result_queue.get()
  
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 the WarpSampler obj's underlying member method code with example usage: obj.next_batch() and returns: self