parameters: (tensors_list, batch_size, capacity,
                               min_after_dequeue, keep_input, 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,
        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 conditionally-enqueued tensors.
  
    See docstring in `shuffle_batch_join` for more details.
  
    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.
      keep_input: A `bool` Tensor.  This tensor controls whether the input is
        added to the queue or not.  If it is a scalar and evaluates `True`, then
        `tensors` are all added to the queue. If it is a vector and `enqueue_many`
        is `True`, then each example is added to the queue only if the
        corresponding value in `keep_input` is `True`. This tensor essentially
        acts as a filtering mechanism.
      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`.
  
    @compatibility(eager)
    Input pipelines based on Queues are not supported when eager execution is
    enabled. Please use the `tf.data` API to ingest data under eager execution.
    @end_compatibility
docstring_summary: Create batches by randomly shuffling conditionally-enqueued tensors.
docstring_tokens: Create batches by randomly shuffling conditionally - enqueued tensors .
function: def maybe_shuffle_batch_join(tensors_list, batch_size, capacity,
                               min_after_dequeue, keep_input, seed=None,
                               enqueue_many=False, shapes=None,
                               allow_smaller_final_batch=False, shared_name=None,
                               name=None):
    """Create batches by randomly shuffling conditionally-enqueued tensors.
  
    See docstring in `shuffle_batch_join` for more details.
  
    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.
      keep_input: A `bool` Tensor.  This tensor controls whether the input is
        added to the queue or not.  If it is a scalar and evaluates `True`, then
        `tensors` are all added to the queue. If it is a vector and `enqueue_many`
        is `True`, then each example is added to the queue only if the
        corresponding value in `keep_input` is `True`. This tensor essentially
        acts as a filtering mechanism.
      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`.
  
    @compatibility(eager)
    Input pipelines based on Queues are not supported when eager execution is
    enabled. Please use the `tf.data` API to ingest data under eager execution.
    @end_compatibility
    """
    return _shuffle_batch_join(
        tensors_list,
        batch_size,
        capacity,
        min_after_dequeue,
        keep_input,
        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 maybe_shuffle_batch_join ( tensors_list , batch_size , capacity , min_after_dequeue , keep_input , 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 , seed = seed , enqueue_many = enqueue_many , shapes = shapes , allow_smaller_final_batch = allow_smaller_final_batch , shared_name = shared_name , name = name )