Inference for AttentionOCR model

Hi there!
I am trying to understand Attention OCR repo and its inference.
I have seen its input/output details -

it says, it takes (32) as string. I don't get this. What does it mean?
In demo_inference.py, there is run method-

def run(checkpoint, batch_size, dataset_name, image_path_pattern): images_placeholder, endpoints = create_model(batch_size,dataset_name) images_data = load_images(image_path_pattern, batch_size,dataset_name) session_creator = monitored_session.ChiefSessionCreator(checkpoint_filename_with_path=checkpoint) with monitored_session.MonitoredSession(session_creator=session_creator) as sess: predictions = sess.run(endpoints.predicted_text,feed_dict={images_placeholder: images_data}) return [pr_bytes.decode('utf-8') for pr_bytes in predictions.tolist()]

Here, it takes images_data which --

as batch_size is 1.

Please help me to know this better.
@leandroschelb

The issue points to demo_inference.py, including run(), create_model(), and load_images(), plus linked input/output details. Start by reading that inference path and determine what the stated (32) string and batch_size=1 represent; done means documenting the input shape and its relationship to images_data clearly.