class_docstr: This class helps dealing with the invalid images in the dataset by using
  two queue. One queue is used to enqueue seen and valid images from previous
  batches. The other queue is used to dequeue. The class is implemented such
  that the same batch will never have duplicate images. If we can't dequeue a
  valid image, we return None for that instance.
  
  Args:
      queue_size: size the the queue (ideally set it to batch_size). Both queues
                  will be of the same size
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code: def _get_enqueue_buffer_size(self):
      return self.enqueue_images_queue.qsize()
  
imports: import contextlib
  import logging
  import queue
  from typing import Dict, List
  import numpy
  import torch
  from PIL import Image
  from torch.utils.data import Dataset
  from torch.utils.data.distributed import DistributedSampler
label_desc: Use image node_id 6 for calling the QueueDataset obj's underlying member method code with example usage: obj._get_enqueue_buffer_size() and returns: self