# tf.train.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)

### tf.train.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)

See the guide: Inputs and Readers > Input pipeline

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 scalar Tensor. If provided, this tensor controls whether the input is added to the queue or not. If it evaluates True, then tensors_list are added to the queue; otherwise they are dropped. 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.