code: def random_cutout(
      images: TensorLike,
      mask_size: TensorLike,
      constant_values: Number = 0,
      seed: Number = None,
  ) -> tf.Tensor:
      """Apply [cutout](https://arxiv.org/abs/1708.04552) to images with random offset.
  
      This operation applies a `(mask_height x mask_width)` mask of zeros to
      a random location within `images`. The pixel values filled in will be of
      the value `constant_values`. The location where the mask will be applied is
      randomly chosen uniformly over the whole images.
  
      Args:
        images: A tensor of shape `(batch_size, height, width, channels)` (NHWC).
        mask_size: Specifies how big the zero mask that will be generated is that
          is applied to the images. The mask will be of size
          `(mask_height x mask_width)`. Note: mask_size should be divisible by 2.
        constant_values: What pixel value to fill in the images in the area that has
          the cutout mask applied to it.
        seed: A Python integer. Used in combination with `tf.random.set_seed` to
          create a reproducible sequence of tensors across multiple calls.
      Returns:
        A `Tensor` of the same shape and dtype as `images`.
      Raises:
        InvalidArgumentError: if `mask_size` can't be divisible by 2.
      """
      images = tf.convert_to_tensor(images)
      mask_size = tf.convert_to_tensor(mask_size)
  
      image_dynamic_shape = tf.shape(images)
      batch_size, image_height, image_width = (
          image_dynamic_shape[0],
          image_dynamic_shape[1],
          image_dynamic_shape[2],
      )
  
      mask_size, _ = _norm_params(mask_size, offset=None)
  
      cutout_center_height = _random_center(
          mask_size[0], image_height, batch_size, seed
      )
      cutout_center_width = _random_center(
          mask_size[1], image_width, batch_size, seed
      )
  
      offset = tf.transpose(
          [cutout_center_height, cutout_center_width], [1, 0]
      )
      return cutout(images, mask_size, offset, constant_values)
  
imports: import tensorflow
  from tensorflow_addons.utils.types import TensorLike, Number
label_desc: Use image node_id 3 for calling a global function with example usage: random_cutout(images, mask_size, constant_values, seed) and returns: cutout