Method __init__

tensorflow/python/ops/data_flow_ops.py:855–911

Creates a queue that dequeues elements in a first-in first-out order. A `PriorityQueue` has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A `PriorityQueue` holds a list of up to `capacity` elements. Each element is a fix

(self,
               capacity,
               types,
               shapes=None,
               names=None,
               shared_name=None,
               name="priority_queue")

  """

  def __init__(self,
               capacity,
               types,
               shapes=None,
               names=None,
               shared_name=None,
               name="priority_queue"):
    """Creates a queue that dequeues elements in a first-in first-out order.

    A `PriorityQueue` has bounded capacity; supports multiple concurrent
    producers and consumers; and provides exactly-once delivery.

    A `PriorityQueue` holds a list of up to `capacity` elements. Each
    element is a fixed-length tuple of tensors whose dtypes are
    described by `types`, 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.

    Enqueues and Dequeues to the `PriorityQueue` must include an additional
    tuple entry at the beginning: the `priority`.  The priority must be
    an int64 scalar (for `enqueue`) or an int64 vector (for `enqueue_many`).

    Args:
      capacity: An integer. The upper bound on the number of elements
        that may be stored in this queue.
      types:  A list of `DType` objects. The length of `types` must equal
        the number of tensors in each queue element, except the first priority
        element.  The first tensor in each element is the priority,
        which must be type int64.
      shapes: (Optional.) A list of fully-defined `TensorShape` objects,
        with the same length as `types`, or `None`.
      names: (Optional.) A list of strings 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.
      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.
    """
    types = _as_type_list(types)
    shapes = _as_shape_list(shapes, types)

    queue_ref = gen_data_flow_ops.priority_queue_v2(
        component_types=types,
        shapes=shapes,
        capacity=capacity,
        shared_name=_shared_name(shared_name),
        name=name)

    priority_dtypes = [_dtypes.int64] + types
    priority_shapes = [()] + shapes if shapes else shapes

    super(PriorityQueue, self).__init__(priority_dtypes, priority_shapes, names,
                                        queue_ref)