code: def _shuffle_inputs(
      input_tensors, capacity, min_after_dequeue, num_threads
  ):
      """Shuffles tensors in `input_tensors`, maintaining grouping."""
      shuffle_queue = tf.RandomShuffleQueue(
          capacity,
          min_after_dequeue,
          dtypes=[t.dtype for t in input_tensors],
      )
      enqueue_op = shuffle_queue.enqueue(input_tensors)
      runner = tf.train.QueueRunner(
          shuffle_queue, [enqueue_op] * num_threads
      )
      tf.train.add_queue_runner(runner)
  
      output_tensors = shuffle_queue.dequeue()
  
      for i in range(len(input_tensors)):
          output_tensors[i].set_shape(input_tensors[i].shape)
  
      return output_tensors
  
imports: import math
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: _shuffle_inputs(input_tensors, capacity, min_after_dequeue, num_threads) and returns: output_tensors