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
class_docstr_tok: ["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"]
docstr: Exceptions raised but not handled by the `QueueRunner` threads.
  
  Exceptions raised in queue runner threads are handled in one of two ways
  depending on whether or not a `Coordinator` was passed to
  `create_threads()`:
  
  * With a `Coordinator`, exceptions are reported to the coordinator and
    forgotten by the `QueueRunner`.
  * Without a `Coordinator`, exceptions are captured by the `QueueRunner` and
    made available in this `exceptions_raised` property.
  
  Returns:
    A list of Python `Exception` objects.  The list is empty if no exception
    was captured.  (No exceptions are captured when using a Coordinator.)
docstr_tok: ["Exceptions","raised","but","not","handled","by","the","`","QueueRunner","`","threads",".","Exceptions","raised","in","queue","runner","threads","are","handled","in","one","of","two","ways","depending","on","whether","or","not","a","`","Coordinator","`","was","passed","to","`","create_threads","(",")","`",":","*","With","a","`","Coordinator","`",",","exceptions","are","reported","to","the","coordinator","and","forgotten","by","the","`","QueueRunner","`",".","*","Without","a","`","Coordinator","`",",","exceptions","are","captured","by","the","`","QueueRunner","`","and","made","available","in","this","`","exceptions_raised","`","property",".","Returns",":","A","list","of","Python","`","Exception","`","objects",".","The","list","is","empty","if","no","exception","was","captured",".","(","No","exceptions","are","captured","when","using","a","Coordinator",".",")"]
code: def exceptions_raised(self):
      """Exceptions raised but not handled by the `QueueRunner` threads.
  
      Exceptions raised in queue runner threads are handled in one of two ways
      depending on whether or not a `Coordinator` was passed to
      `create_threads()`:
  
      * With a `Coordinator`, exceptions are reported to the coordinator and
        forgotten by the `QueueRunner`.
      * Without a `Coordinator`, exceptions are captured by the `QueueRunner` and
        made available in this `exceptions_raised` property.
  
      Returns:
        A list of Python `Exception` objects.  The list is empty if no exception
        was captured.  (No exceptions are captured when using a Coordinator.)
      """
      return self._exceptions_raised
  
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 9 for calling the QueueRunner obj's underlying member method code with example usage: obj.exceptions_raised() and returns: self