class_docstr: Holds a list of enqueue operations for a queue, each to be run in a thread.
  
  Queues are a convenient TensorFlow mechanism to compute tensors
  asynchronously using multiple threads. For example in the canonical 'Input
  Reader' setup one set of threads generates filenames in a queue; a second set
  of threads read records from the files, processes them, and enqueues tensors
  on a second queue; a third set of threads dequeues these input records to
  construct batches and runs them through training operations.
  
  There are several delicate issues when running multiple threads that way:
  closing the queues in sequence as the input is exhausted, correctly catching
  and reporting exceptions, etc.
  
  The `QueueRunner`, combined with the `Coordinator`, helps handle these issues.
  
  @compatibility(TF2)
  QueueRunners are not compatible with eager execution. Instead, please
  use [tf.data](https://www.tensorflow.org/guide/data) to get data into your
  model.
  @end_compatibility
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code: def queue_closed_exception_types(self):
      return self._queue_closed_exception_types
  
imports: import threading
  import weakref
  from tensorflow.core.protobuf import queue_runner_pb2
  from tensorflow.python.client import session
  from tensorflow.python.eager import context
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 8 for calling the QueueRunner obj's underlying member method code with example usage: obj.queue_closed_exception_types() and returns: self