# tf.PriorityQueue

### class tf.PriorityQueue

See the guide: Inputs and Readers > Queues

A queue implementation that dequeues elements in prioritized order.

See tf.QueueBase for a description of the methods on this class.

## Properties

### dtypes

The list of dtypes for each component of a queue element.

### name

The name of the underlying queue.

### names

The list of names for each component of a queue element.

### queue_ref

The underlying queue reference.

### shapes

The list of shapes for each component of a queue element.

## Methods

### __init__(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.

### close(cancel_pending_enqueues=False, name=None)

Closes this queue.

This operation signals that no more elements will be enqueued in the given queue. Subsequent enqueue and enqueue_many operations will fail. Subsequent dequeue and dequeue_many operations will continue to succeed if sufficient elements remain in the queue. Subsequent dequeue and dequeue_many operations that would block will fail immediately.

If cancel_pending_enqueues is True, all pending requests will also be cancelled.

#### Args:

• cancel_pending_enqueues: (Optional.) A boolean, defaulting to False (described above).
• name: A name for the operation (optional).

#### Returns:

The operation that closes the queue.

### dequeue(name=None)

Dequeues one element from this queue.

If the queue is empty when this operation executes, it will block until there is an element to dequeue.

At runtime, this operation may raise an error if the queue is tf.QueueBase.close before or during its execution. If the queue is closed, the queue is empty, and there are no pending enqueue operations that can fulfill this request, tf.errors.OutOfRangeError will be raised. If the session is tf.Session.close, tf.errors.CancelledError will be raised.

#### Args:

• name: A name for the operation (optional).

#### Returns:

The tuple of tensors that was dequeued.

### dequeue_many(n, name=None)

Dequeues and concatenates n elements from this queue.

This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension.

If the queue is closed and there are less than n elements left, then an OutOfRange exception is raised.

At runtime, this operation may raise an error if the queue is tf.QueueBase.close before or during its execution. If the queue is closed, the queue contains fewer than n elements, and there are no pending enqueue operations that can fulfill this request, tf.errors.OutOfRangeError will be raised. If the session is tf.Session.close, tf.errors.CancelledError will be raised.

#### Args:

• n: A scalar Tensor containing the number of elements to dequeue.
• name: A name for the operation (optional).

#### Returns:

The tuple of concatenated tensors that was dequeued.

### dequeue_up_to(n, name=None)

Dequeues and concatenates n elements from this queue.

Note This operation is not supported by all queues. If a queue does not support DequeueUpTo, then a tf.errors.UnimplementedError is raised.

This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. If the queue has not been closed, all of the components in the dequeued tuple will have size n in the 0th dimension.

If the queue is closed and there are more than 0 but fewer than n elements remaining, then instead of raising a tf.errors.OutOfRangeError like tf.QueueBase.dequeue_many, less than n elements are returned immediately. If the queue is closed and there are 0 elements left in the queue, then a tf.errors.OutOfRangeError is raised just like in dequeue_many. Otherwise the behavior is identical to dequeue_many.

#### Args:

• n: A scalar Tensor containing the number of elements to dequeue.
• name: A name for the operation (optional).

#### Returns:

The tuple of concatenated tensors that was dequeued.

### enqueue(vals, name=None)

Enqueues one element to this queue.

If the queue is full when this operation executes, it will block until the element has been enqueued.

At runtime, this operation may raise an error if the queue is tf.QueueBase.close before or during its execution. If the queue is closed before this operation runs, tf.errors.CancelledError will be raised. If this operation is blocked, and either (i) the queue is closed by a close operation with cancel_pending_enqueues=True, or (ii) the session is tf.Session.close, tf.errors.CancelledError will be raised.

#### Args:

• vals: A tensor, a list or tuple of tensors, or a dictionary containing the values to enqueue.
• name: A name for the operation (optional).

#### Returns:

The operation that enqueues a new tuple of tensors to the queue.

### enqueue_many(vals, name=None)

Enqueues zero or more elements to this queue.

This operation slices each component tensor along the 0th dimension to make multiple queue elements. All of the tensors in vals must have the same size in the 0th dimension.

If the queue is full when this operation executes, it will block until all of the elements have been enqueued.

At runtime, this operation may raise an error if the queue is tf.QueueBase.close before or during its execution. If the queue is closed before this operation runs, tf.errors.CancelledError will be raised. If this operation is blocked, and either (i) the queue is closed by a close operation with cancel_pending_enqueues=True, or (ii) the session is tf.Session.close, tf.errors.CancelledError will be raised.

#### Args:

• vals: A tensor, a list or tuple of tensors, or a dictionary from which the queue elements are taken.
• name: A name for the operation (optional).

#### Returns:

The operation that enqueues a batch of tuples of tensors to the queue.

### from_list(index, queues)

Create a queue using the queue reference from queues[index].

#### Args:

• index: An integer scalar tensor that determines the input that gets selected.
• queues: A list of QueueBase objects.

#### Returns:

A QueueBase object.

#### Raises:

• TypeError: When queues is not a list of QueueBase objects, or when the data types of queues are not all the same.

### size(name=None)

Compute the number of elements in this queue.

#### Args:

• name: A name for the operation (optional).

#### Returns:

A scalar tensor containing the number of elements in this queue.