human
I wrote the below code

    def input_fn():
        u'Creates    features    and    labels.\n                '
        with tf.variable_scope((scope or u'input_fn')):
            data_provider = pipeline.make_data_provider()
            features_and_labels = pipeline.read_from_data_provider(data_provider)
            if bucket_boundaries:
                (_, batch) = tf.contrib.training.bucket_by_sequence_length(input_length=features_and_labels[u'source_len'], bucket_boundaries=bucket_boundaries, tensors=features_and_labels, batch_size=batch_size, keep_input=(features_and_labels[u'source_len'] >= 1), dynamic_pad=True, capacity=(5000 + (16 * batch_size)), allow_smaller_final_batch=allow_smaller_final_batch, name=u'bucket_queue')
            else:
                batch = tf.train.batch(tensors=features_and_labels, enqueue_many=False, batch_size=batch_size, dynamic_pad=True, capacity=(5000 + (16 * batch_size)), allow_smaller_final_batch=allow_smaller_final_batch, name=u'batch_queue')
            features_batch = {k: batch[k] for k in pipeline.feature_keys}
            if set(batch.keys()).intersection(pipeline.label_keys):
                labels_batch = {k: batch[k] for k in pipeline.label_keys}
            else:
                labels_batch = None
            return (features_batch, labels_batch)
    return input_fn

What's a good function header?

=====
Here's a good function header?
assistant
#Here's a good function header.

def create_input_fn(pipeline, batch_size, bucket_boundaries=None, allow_smaller_final_batch=False, scope=None):