parameters: (self, capacity, min_after_dequeue, dtypes, shapes=None,
                 names=None, seed=None, shared_name=None,
                 name="random_shuffle_queue")
docstring: Create a queue that dequeues elements in a random order.
  
      A `RandomShuffleQueue` has bounded capacity; supports multiple
      concurrent producers and consumers; and provides exactly-once
      delivery.
  
      A `RandomShuffleQueue` holds a list of up to `capacity`
      elements. Each element is a fixed-length tuple of tensors whose
      dtypes are described by `dtypes`, and whose shapes are optionally
      described by the `shapes` argument.
  
      If the `shapes` argument is specified, each component of a queue
      element must have the respective fixed shape. If it is
      unspecified, different queue elements may have different shapes,
      but the use of `dequeue_many` is disallowed.
  
      The `min_after_dequeue` argument allows the caller to specify a
      minimum number of elements that will remain in the queue after a
      `dequeue` or `dequeue_many` operation completes, to ensure a
      minimum level of mixing of elements. This invariant is maintained
      by blocking those operations until sufficient elements have been
      enqueued. The `min_after_dequeue` argument is ignored after the
      queue has been closed.
  
      Args:
        capacity: An integer. The upper bound on the number of elements
          that may be stored in this queue.
        min_after_dequeue: An integer (described above).
        dtypes:  A list of `DType` objects. The length of `dtypes` must equal
          the number of tensors in each queue element.
        shapes: (Optional.) A list of fully-defined `TensorShape` objects
          with the same length as `dtypes`, or `None`.
        names: (Optional.) A list of string naming the components in the queue
          with the same length as `dtypes`, or `None`.  If specified the dequeue
          methods return a dictionary with the names as keys.
        seed: A Python integer. Used to create a random seed. See
          @{tf.set_random_seed}
          for behavior.
        shared_name: (Optional.) If non-empty, this queue will be shared under
          the given name across multiple sessions.
        name: Optional name for the queue operation.
docstring_summary: Create a queue that dequeues elements in a random order.
docstring_tokens: Create a queue that dequeues elements in a random order .
function: def __init__(self, capacity, min_after_dequeue, dtypes, shapes=None,
                 names=None, seed=None, shared_name=None,
                 name="random_shuffle_queue"):
      """Create a queue that dequeues elements in a random order.
  
      A `RandomShuffleQueue` has bounded capacity; supports multiple
      concurrent producers and consumers; and provides exactly-once
      delivery.
  
      A `RandomShuffleQueue` holds a list of up to `capacity`
      elements. Each element is a fixed-length tuple of tensors whose
      dtypes are described by `dtypes`, and whose shapes are optionally
      described by the `shapes` argument.
  
      If the `shapes` argument is specified, each component of a queue
      element must have the respective fixed shape. If it is
      unspecified, different queue elements may have different shapes,
      but the use of `dequeue_many` is disallowed.
  
      The `min_after_dequeue` argument allows the caller to specify a
      minimum number of elements that will remain in the queue after a
      `dequeue` or `dequeue_many` operation completes, to ensure a
      minimum level of mixing of elements. This invariant is maintained
      by blocking those operations until sufficient elements have been
      enqueued. The `min_after_dequeue` argument is ignored after the
      queue has been closed.
  
      Args:
        capacity: An integer. The upper bound on the number of elements
          that may be stored in this queue.
        min_after_dequeue: An integer (described above).
        dtypes:  A list of `DType` objects. The length of `dtypes` must equal
          the number of tensors in each queue element.
        shapes: (Optional.) A list of fully-defined `TensorShape` objects
          with the same length as `dtypes`, or `None`.
        names: (Optional.) A list of string naming the components in the queue
          with the same length as `dtypes`, or `None`.  If specified the dequeue
          methods return a dictionary with the names as keys.
        seed: A Python integer. Used to create a random seed. See
          @{tf.set_random_seed}
          for behavior.
        shared_name: (Optional.) If non-empty, this queue will be shared under
          the given name across multiple sessions.
        name: Optional name for the queue operation.
      """
      dtypes = _as_type_list(dtypes)
      shapes = _as_shape_list(shapes, dtypes)
      names = _as_name_list(names, dtypes)
      seed1, seed2 = random_seed.get_seed(seed)
      if seed1 is None and seed2 is None:
        seed1, seed2 = 0, 0
      elif seed is None and shared_name is not None:
        # This means that graph seed is provided but op seed is not provided.
        # If shared_name is also provided, make seed2 depend only on the graph
        # seed and shared_name. (seed2 from get_seed() is generally dependent on
        # the id of the last op created.)
        string = (str(seed1) + shared_name).encode("utf-8")
        seed2 = int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF
      queue_ref = gen_data_flow_ops._random_shuffle_queue_v2(
          component_types=dtypes, shapes=shapes, capacity=capacity,
          min_after_dequeue=min_after_dequeue, seed=seed1, seed2=seed2,
          shared_name=shared_name, name=name)
  
      super(RandomShuffleQueue, self).__init__(dtypes, shapes, names, queue_ref)
function_tokens: def __init__ ( self , capacity , min_after_dequeue , dtypes , shapes = None , names = None , seed = None , shared_name = None , name = "random_shuffle_queue" ) : dtypes = _as_type_list ( dtypes ) shapes = _as_shape_list ( shapes , dtypes ) names = _as_name_list ( names , dtypes ) seed1 , seed2 = random_seed . get_seed ( seed ) if seed1 is None and seed2 is None : seed1 , seed2 = 0 , 0 elif seed is None and shared_name is not None : # This means that graph seed is provided but op seed is not provided. # If shared_name is also provided, make seed2 depend only on the graph # seed and shared_name. (seed2 from get_seed() is generally dependent on # the id of the last op created.) string = ( str ( seed1 ) + shared_name ) . encode ( "utf-8" ) seed2 = int ( hashlib . md5 ( string ) . hexdigest ( ) [ : 8 ] , 16 ) & 0x7FFFFFFF queue_ref = gen_data_flow_ops . _random_shuffle_queue_v2 ( component_types = dtypes , shapes = shapes , capacity = capacity , min_after_dequeue = min_after_dequeue , seed = seed1 , seed2 = seed2 , shared_name = shared_name , name = name ) super ( RandomShuffleQueue , self ) . __init__ ( dtypes , shapes , names , queue_ref )