class_docstr: Applies RandomErasing to a video.
  
  
  Reference: https://arxiv.org/abs/1708.04896
class_docstr_tok: ["Applies","RandomErasing","to","a","video",".","Reference",":","https",":","\/\/arxiv.org\/abs\/1708.04896"]
docstr: Applies RandomErasing to video.
  
  Args:
    video (tf.Tensor): Of shape [temporal, height, width, 3] representing a
    video.
  
  Returns:
    tf.Tensor: The augmented version of video.
docstr_tok: ["Applies","RandomErasing","to","video",".","Args",":","video","(","tf.Tensor",")",":","Of","shape","[","temporal",",","height",",","width",",","3","]","representing","a","video",".","Returns",":","tf.Tensor",":","The","augmented","version","of","video","."]
code: def distort(self, video: tf.Tensor) -> tf.Tensor:
      """Applies RandomErasing to video.
  
      Args:
        video (tf.Tensor): Of shape [temporal, height, width, 3] representing a
        video.
  
      Returns:
        tf.Tensor: The augmented version of video.
      """
      uniform_random = tf.random.uniform(
          shape=[], minval=0.0, maxval=1.0
      )
      mirror_cond = tf.less(uniform_random, self._probability)
      video = tf.cond(
          mirror_cond, lambda: self._erase(video), lambda: video
      )
      return video
  
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 2 for calling the RandomErasing obj's underlying member method code with example usage: obj.distort(video) and returns: video