parameters: (tensors_list, batch_size, capacity,
                         min_after_dequeue, seed=None, enqueue_many=False,
                         shapes=None, allow_smaller_final_batch=False,
                         shared_name=None, name=None)
return_statement: return _shuffle_batch_join(
        tensors_list,
        batch_size,
        capacity,
        min_after_dequeue,
        keep_input=True,
        seed=seed,
        enqueue_many=enqueue_many,
        shapes=shapes,
        allow_smaller_final_batch=allow_smaller_final_batch,
        shared_name=shared_name,
        name=name)
docstring: Create batches by randomly shuffling tensors.
  
    The `tensors_list` argument is a list of tuples of tensors, or a list of
    dictionaries of tensors.  Each element in the list is treated similarly
    to the `tensors` argument of `tf.train.shuffle_batch()`.
  
    This version enqueues a different list of tensors in different threads.
    It adds the following to the current `Graph`:
  
    * A shuffling queue into which tensors from `tensors_list` are enqueued.
    * A `dequeue_many` operation to create batches from the queue.
    * A `QueueRunner` to `QUEUE_RUNNER` collection, to enqueue the tensors
      from `tensors_list`.
  
    `len(tensors_list)` threads will be started, with thread `i` enqueuing
    the tensors from `tensors_list[i]`. `tensors_list[i1][j]` must match
    `tensors_list[i2][j]` in type and shape, except in the first dimension if
    `enqueue_many` is true.
  
    If `enqueue_many` is `False`, each `tensors_list[i]` is assumed
    to represent a single example.  An input tensor with shape `[x, y, z]`
    will be output as a tensor with shape `[batch_size, x, y, z]`.
  
    If `enqueue_many` is `True`, `tensors_list[i]` is assumed to
    represent a batch of examples, where the first dimension is indexed
    by example, and all members of `tensors_list[i]` should have the
    same size in the first dimension.  If an input tensor has shape `[*, x,
    y, z]`, the output will have shape `[batch_size, x, y, z]`.
  
    The `capacity` argument controls the how long the prefetching is allowed to
    grow the queues.
  
    The returned operation is a dequeue operation and will throw
    `tf.errors.OutOfRangeError` if the input queue is exhausted. If this
    operation is feeding another input queue, its queue runner will catch
    this exception, however, if this operation is used in your main thread
    you are responsible for catching this yourself.
  
    If `allow_smaller_final_batch` is `True`, a smaller batch value than
    `batch_size` is returned when the queue is closed and there are not enough
    elements to fill the batch, otherwise the pending elements are discarded.
    In addition, all output tensors' static shapes, as accessed via the
    `shape` property will have a first `Dimension` value of `None`, and
    operations that depend on fixed batch_size would fail.
  
    Args:
      tensors_list: A list of tuples or dictionaries of tensors to enqueue.
      batch_size: An integer. The new batch size pulled from the queue.
      capacity: An integer. The maximum number of elements in the queue.
      min_after_dequeue: Minimum number elements in the queue after a
        dequeue, used to ensure a level of mixing of elements.
      seed: Seed for the random shuffling within the queue.
      enqueue_many: Whether each tensor in `tensor_list_list` is a single
        example.
      shapes: (Optional) The shapes for each example.  Defaults to the
        inferred shapes for `tensors_list[i]`.
      allow_smaller_final_batch: (Optional) Boolean. If `True`, allow the final
        batch to be smaller if there are insufficient items left in the queue.
      shared_name: (optional). If set, this queue will be shared under the given
        name across multiple sessions.
      name: (Optional) A name for the operations.
  
    Returns:
      A list or dictionary of tensors with the same number and types as
      `tensors_list[i]`.
  
    Raises:
      ValueError: If the `shapes` are not specified, and cannot be
        inferred from the elements of `tensors_list`.
docstring_summary: Create batches by randomly shuffling tensors.
docstring_tokens: Create batches by randomly shuffling tensors .
function: def shuffle_batch_join(tensors_list, batch_size, capacity,
                         min_after_dequeue, seed=None, enqueue_many=False,
                         shapes=None, allow_smaller_final_batch=False,
                         shared_name=None, name=None):
    """Create batches by randomly shuffling tensors.
  
    The `tensors_list` argument is a list of tuples of tensors, or a list of
    dictionaries of tensors.  Each element in the list is treated similarly
    to the `tensors` argument of `tf.train.shuffle_batch()`.
  
    This version enqueues a different list of tensors in different threads.
    It adds the following to the current `Graph`:
  
    * A shuffling queue into which tensors from `tensors_list` are enqueued.
    * A `dequeue_many` operation to create batches from the queue.
    * A `QueueRunner` to `QUEUE_RUNNER` collection, to enqueue the tensors
      from `tensors_list`.
  
    `len(tensors_list)` threads will be started, with thread `i` enqueuing
    the tensors from `tensors_list[i]`. `tensors_list[i1][j]` must match
    `tensors_list[i2][j]` in type and shape, except in the first dimension if
    `enqueue_many` is true.
  
    If `enqueue_many` is `False`, each `tensors_list[i]` is assumed
    to represent a single example.  An input tensor with shape `[x, y, z]`
    will be output as a tensor with shape `[batch_size, x, y, z]`.
  
    If `enqueue_many` is `True`, `tensors_list[i]` is assumed to
    represent a batch of examples, where the first dimension is indexed
    by example, and all members of `tensors_list[i]` should have the
    same size in the first dimension.  If an input tensor has shape `[*, x,
    y, z]`, the output will have shape `[batch_size, x, y, z]`.
  
    The `capacity` argument controls the how long the prefetching is allowed to
    grow the queues.
  
    The returned operation is a dequeue operation and will throw
    `tf.errors.OutOfRangeError` if the input queue is exhausted. If this
    operation is feeding another input queue, its queue runner will catch
    this exception, however, if this operation is used in your main thread
    you are responsible for catching this yourself.
  
    If `allow_smaller_final_batch` is `True`, a smaller batch value than
    `batch_size` is returned when the queue is closed and there are not enough
    elements to fill the batch, otherwise the pending elements are discarded.
    In addition, all output tensors' static shapes, as accessed via the
    `shape` property will have a first `Dimension` value of `None`, and
    operations that depend on fixed batch_size would fail.
  
    Args:
      tensors_list: A list of tuples or dictionaries of tensors to enqueue.
      batch_size: An integer. The new batch size pulled from the queue.
      capacity: An integer. The maximum number of elements in the queue.
      min_after_dequeue: Minimum number elements in the queue after a
        dequeue, used to ensure a level of mixing of elements.
      seed: Seed for the random shuffling within the queue.
      enqueue_many: Whether each tensor in `tensor_list_list` is a single
        example.
      shapes: (Optional) The shapes for each example.  Defaults to the
        inferred shapes for `tensors_list[i]`.
      allow_smaller_final_batch: (Optional) Boolean. If `True`, allow the final
        batch to be smaller if there are insufficient items left in the queue.
      shared_name: (optional). If set, this queue will be shared under the given
        name across multiple sessions.
      name: (Optional) A name for the operations.
  
    Returns:
      A list or dictionary of tensors with the same number and types as
      `tensors_list[i]`.
  
    Raises:
      ValueError: If the `shapes` are not specified, and cannot be
        inferred from the elements of `tensors_list`.
    """
    return _shuffle_batch_join(
        tensors_list,
        batch_size,
        capacity,
        min_after_dequeue,
        keep_input=True,
        seed=seed,
        enqueue_many=enqueue_many,
        shapes=shapes,
        allow_smaller_final_batch=allow_smaller_final_batch,
        shared_name=shared_name,
        name=name)
function_tokens: def shuffle_batch_join ( tensors_list , batch_size , capacity , min_after_dequeue , seed = None , enqueue_many = False , shapes = None , allow_smaller_final_batch = False , shared_name = None , name = None ) : return _shuffle_batch_join ( tensors_list , batch_size , capacity , min_after_dequeue , keep_input = True , seed = seed , enqueue_many = enqueue_many , shapes = shapes , allow_smaller_final_batch = allow_smaller_final_batch , shared_name = shared_name , name = name )