def run_model_on_random_input(model):
    batch_size = 1
    height = 100
    width = 200
    inputs = {'image': tf.random.uniform(shape=(batch_size, height, width, 3), minval=0, maxval=256, dtype='int32'), 'horz_split_points_probs': tf.random.uniform(shape=(batch_size, height), dtype='float32'), 'vert_split_points_probs': tf.random.uniform(shape=(batch_size, width), dtype='float32'), 'horz_split_points_binary': tf.random.uniform(shape=(batch_size, height), minval=0, maxval=2, dtype='int32'), 'vert_split_points_binary': tf.random.uniform(shape=(batch_size, width), minval=0, maxval=2, dtype='int32')}
    model(inputs)
def run_model_on_random_input(model):
    batch_size = 1
    height = 100
    width = 200
    inputs = {'image': tf.random.uniform(shape=(batch_size, height, width, 3), minval=0, maxval=256, dtype='int32'), 'horz_split_points_probs': tf.random.uniform(shape=(batch_size, height), dtype='float32'), 'vert_split_points_probs': tf.random.uniform(shape=(batch_size, width), dtype='float32'), 'horz_split_points_binary': tf.random.uniform(shape=(batch_size, height), minval=0, maxval=2, dtype='int32'), 'vert_split_points_binary': tf.random.uniform(shape=(batch_size, width), minval=0, maxval=2, dtype='int32')}
    model(inputs)