human
Complete the below
def _get_inputs(input_queue, num_classes):

    ''''Dequeue batch and construct inputs to object detection model.
    Args:
    input_queue: BatchQueue object holding enqueued tensor_dicts.
    num_classes: Number of classes.
    Returns:
    images: a list of 3-D float tensor of images.
    locations_list: a list of tensors of shape [num_boxes, 4]
    containing the corners of the groundtruth boxes.
    classes_list: a list of padded one-hot tensors containing target classes.
    masks_list: a list of 3-D float tensors of shape [num_boxes, image_height,
    image_width] containing instance masks for objects if present in the
    input_queue. Else returns None.'
    '''

Can you provide a the Python program you requested?
assistant
#Here is the Python program you requested.
    read_data_list = input_queue.dequeue()
    label_id_offset = 1
    def extract_images_and_targets(read_data):
        image = read_data[fields.InputDataFields.image]
        location_gt = read_data[fields.InputDataFields.groundtruth_boxes]
        classes_gt = tf.cast(read_data[fields.InputDataFields.groundtruth_classes], tf.int32)
        classes_gt -= label_id_offset
        classes_gt = util_ops.padded_one_hot_encoding(indices=classes_gt, depth=num_classes, left_pad=0)
        masks_gt = read_data.get(fields.InputDataFields.groundtruth_instance_masks)
        return (image, location_gt, classes_gt, masks_gt)
    return zip(*map(extract_images_and_targets, read_data_list))