code: def cutout(big_image, pad_size, num_frames, replace=0) -> tf.Tensor:
      """Apply cutout (https://arxiv.org/abs/1708.04552) to image.
  
      This operation applies a (2*pad_size x 2*pad_size) mask of zeros to
      a random location within `img`. The pixel values filled in will be of the
      value `replace`. The located where the mask will be applied is randomly
      chosen uniformly over the whole image.
  
      Args:
        big_image: An image Tensor of type uint8. Shape is [t * h, w, c]
        pad_size: Specifies how big the zero mask that will be generated is that is
          applied to the image. The mask will be of size (2*pad_size x 2*pad_size).
        num_frames: Specifies the t dimension in the input shape.
        replace: What pixel value to fill in the image in the area that has the
          cutout mask applied to it.
  
      Returns:
        An image Tensor that is of type uint8.
      """
      big_image_shape = _get_shape(big_image)
      image = tf.reshape(
          big_image,
          [
              num_frames,
              big_image_shape[0] // num_frames,
              big_image_shape[1],
              big_image_shape[2],
          ],
      )
      image_height = tf.shape(image)[1]
      image_width = tf.shape(image)[2]
  
      # Sample the center location in the image where the zero mask will be applied.
      cutout_center_height = tf.random.uniform(
          shape=[], minval=0, maxval=image_height, dtype=tf.int32
      )
  
      cutout_center_width = tf.random.uniform(
          shape=[], minval=0, maxval=image_width, dtype=tf.int32
      )
  
      lower_pad = tf.maximum(0, cutout_center_height - pad_size)
      upper_pad = tf.maximum(
          0, image_height - cutout_center_height - pad_size
      )
      left_pad = tf.maximum(0, cutout_center_width - pad_size)
      right_pad = tf.maximum(
          0, image_width - cutout_center_width - pad_size
      )
  
      cutout_shape = [
          image_height - (lower_pad + upper_pad),
          image_width - (left_pad + right_pad),
      ]
      padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]]
      mask = tf.pad(
          tf.zeros(cutout_shape, dtype=image.dtype),
          padding_dims,
          constant_values=1,
      )
      mask = tf.expand_dims(mask, -1)
      mask = tf.expand_dims(mask, 0)
      mask = tf.tile(mask, [num_frames, 1, 1, 3])
      image = tf.where(
          tf.equal(mask, 0),
          tf.ones_like(image, dtype=image.dtype) * replace,
          image,
      )
  
      big_image = tf.reshape(
          image, [num_frames * image_height, image_width, 3]
      )
      return big_image
  
imports: import functools
  import math
  from typing import Optional
  from absl import logging
  from dmvr import builders
  from dmvr import processors
  import simclr.tf2.data_util
  import tensorflow
  from official.vision.image_classification import augment
label_desc: Use image node_id 14 for calling a global function with example usage: cutout(big_image, pad_size, num_frames, replace) and returns: big_image