human
Complete the below
def create_input_fn(pipeline, batch_size, bucket_boundaries=None, allow_smaller_final_batch=False, scope=None):

    ''''Creates an input function that can be used with tf.learn estimators.
    Note that you must pass "factory funcitons" for both the data provider and
    featurizer to ensure that everything will be created in  the same graph.
    Args:
    pipeline: An instance of `seq2seq.data.InputPipeline`.
    batch_size: Create batches of this size. A queue to hold a
    reasonable number of batches in memory is created.
    bucket_boundaries: int list, increasing non-negative numbers.
    If None, no bucket is performed.
    Returns:
    An input function that returns `(feature_batch, labels_batch)`
    tuples when called.'
    '''
 ... Write me a the Python code you requested?
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#Here is the Python code you requested.
    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