class_docstr: Unsupervised representation learning dataset.
      
code: def pass_generator(self, epoch=None):
      # pass_generator, open a thread for processing the data
      dataset_list = []
      t = threading.Thread(
          target=self.preload_thread, args=(dataset_list,)
      )
      t.setDaemon(True)
      t.start()
  
      pass_id = 0
      while 1:
          self.ins_ready_sem.acquire()
  
          if len(dataset_list) == 0:
              log.info(
                  "train pass[%d] dataset_list is empty" % (pass_id)
              )
              break
  
          dataset = dataset_list.pop(0)
          if dataset is None:
              log.info("train pass[%d] dataset is null" % (pass_id))
              self.could_load_sem.release()
              continue
  
          data_size = dataset.get_memory_data_size()
          if data_size == 0:
              log.info("train pass[%d], dataset size is 0" % (pass_id))
              self.could_load_sem.release()
              continue
  
          if (
              self.config.max_steps > 0
              and model_util.print_count >= self.config.max_steps
          ):
              log.info(
                  "reach max_steps: %d, epoch[%d] train end"
                  % (self.config.max_steps, epoch)
              )
              self.embedding.begin_pass()
              dataset.release_memory()
              self.embedding.end_pass()
              self.could_load_sem.release()
              continue
  
          beginpass_begin = time.time()
          self.embedding.begin_pass()
          beginpass_end = time.time()
          log.info(
              "train pass[%d] STAGE [BEGIN PASS] finished, time cost: %f sec"
              % (pass_id, beginpass_end - beginpass_begin)
          )
          trainpass_begin = time.time()
  
          yield dataset
  
          trainpass_end = time.time()
          log.info(
              "train pass[%d] STAGE [TRAIN] finished, time cost: %f sec"
              % (pass_id, trainpass_end - trainpass_begin)
          )
          dataset.release_memory()
          endpass_begin = time.time()
          self.embedding.end_pass()
          endpass_end = time.time()
          log.info(
              "train pass[%d] STAGE [END PASS] finished, time cost: %f sec"
              % (pass_id, endpass_end - endpass_begin)
          )
          self.could_load_sem.release()
  
          if pass_id % self.config.save_cache_frequency == 0:
              cache_pass_id = pass_id - self.config.mem_cache_passid_num
              cache_pass_id = 0 if cache_pass_id < 0 else cache_pass_id
              cache_begin = time.time()
              fleet.save_cache_table(0, cache_pass_id)
              cache_end = time.time()
              log.info(
                  "train pass[%d] STAGE [SSD CACHE TABLE] finished, time cost: %f sec",
                  pass_id,
                  cache_end - cache_begin,
              )
  
          pass_id = pass_id + 1
  
      t.join()
  
imports: import os
  import time
  import threading
  import traceback
  import numpy
  import paddle
  from paddle.distributed import fleet
  import paddle.fluid
  from pgl.utils.logger import log
  import util
  import helper
  from place import get_cuda_places
  import models.model_util
label_desc: Use image node_id 2 for calling the UnsupReprLearningDataset obj's underlying member method code with example usage: obj.pass_generator(epoch) without return types