class_docstr: A queue implementation that dequeues elements in first-in first-out order.
  See `tf.queue.QueueBase` for a description of the methods on
  this class.
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docstr: Creates a queue that dequeues elements in a first-in first-out order.
  A `FIFOQueue` has bounded capacity; supports multiple concurrent
  producers and consumers; and provides exactly-once delivery.
  A `FIFOQueue` 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.
  Args:
    capacity: An integer. The upper bound on the number of elements
      that may be stored in this queue.
    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.
    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.
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code: def __init__(
      self,
      capacity,
      dtypes,
      shapes=None,
      names=None,
      shared_name=None,
      name="fifo_queue",
  ):
      """Creates a queue that dequeues elements in a first-in first-out order.
      A `FIFOQueue` has bounded capacity; supports multiple concurrent
      producers and consumers; and provides exactly-once delivery.
      A `FIFOQueue` 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.
      Args:
        capacity: An integer. The upper bound on the number of elements
          that may be stored in this queue.
        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.
        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)
      with ops.init_scope(), ops.device("CPU"):
          queue_ref = gen_data_flow_ops.fifo_queue_v2(
              component_types=dtypes,
              shapes=shapes,
              capacity=capacity,
              shared_name=shared_name,
              name=name,
          )
  
      super(TFFIFOQueue, self).__init__(
          dtypes, shapes, names, queue_ref
      )
  
file_path: tf-encrypted/tf_encrypted/queue/fifo.py
imports: from __future__ import absolute_import
  import abc
  import tensorflow
  from tensorflow.python.framework import ops
  from tensorflow.python.ops.data_flow_ops import _as_name_list
  from tensorflow.python.ops.data_flow_ops import _as_shape_list
  from tensorflow.python.ops.data_flow_ops import _as_type_list
  from tensorflow.python.ops.data_flow_ops import gen_data_flow_ops
  import tf_encrypted
label_desc: Use image node_id 1 to create a new TFFIFOQueue object from inherited base classes: tf.queue with example: obj = TFFIFOQueue(capacity, dtypes, shapes, names, shared_name, name)