old_contents: # Copyright 2022 The KerasCV Authors
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import kears_cv
  import tensorflow as tf
  
  from keras_cv.utils import preprocessing
  
  
  @tf.keras.utils.register_keras_serializable(package="keras_cv")
  class RandomJpegQuality(keras_cv.layers.BaseImageAugmentationLayer):
      """Applies Random Jpeg compression artifacts to an image.
  
      Performs the jpeg compression algorithm on the image.  This layer can used in order
      to ensure your model is robust to artifacts introduced by JPEG compresion.
  
      Args:
          factor: 2 element tuple or 2 element list.  During augmentation, a random number
          is drawn from the factor distribution.  This value is passed to
          `tf.image.adjust_jpeg_quality()`.
          seed: Integer. Used to create a random seed.
  
      Usage:
      ```python
      layer = keras_cv.RandomJpegQuality(factor=(75, 100)))
      (images, labels), _ = tf.keras.datasets.cifar10.load_data()
      augmented_images = layer(images)
      ```
      """
  
      def __init__(self, factor, seed=None, **kwargs):
          super().__init__(**kwargs)
          if isinstance(factor, float, int):
              raise ValueError(
                  "RandomJpegQuality() expects factor to be a 2 element "
                  "tuple, list or a `keras_cv.FactorSampler`. "
                  "RandomJpegQuality() received `factor={factor}`."
              )
          self.factor = preprocessing.parse_factor(
              factor, min_value=0, max_value=100, param_name="factor", seed=self.seed
          )
  
      def get_random_transformation(self, image=None, label=None, bounding_box=None):
          return self.factor(dtype=tf.int32)
  
      def augment_image(self, image, transformation=None):
          jpeg_quality = transformation
          return tf.image.adjust_jpeg_quality(image, jpeg_quality)
  
      def get_config(self):
          config = super().get_config()
          config.update({"factor": self.factor})
          return config
  
new_contents: # Copyright 2022 The KerasCV Authors
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import tensorflow as tf
  
  import keras_cv
  from keras_cv.utils import preprocessing
  
  
  @tf.keras.utils.register_keras_serializable(package="keras_cv")
  class RandomJpegQuality(keras_cv.layers.BaseImageAugmentationLayer):
      """Applies Random Jpeg compression artifacts to an image.
  
      Performs the jpeg compression algorithm on the image.  This layer can used in order
      to ensure your model is robust to artifacts introduced by JPEG compresion.
  
      Args:
          factor: 2 element tuple or 2 element list.  During augmentation, a random number
          is drawn from the factor distribution.  This value is passed to
          `tf.image.adjust_jpeg_quality()`.
          seed: Integer. Used to create a random seed.
  
      Usage:
      ```python
      layer = keras_cv.RandomJpegQuality(factor=(75, 100)))
      (images, labels), _ = tf.keras.datasets.cifar10.load_data()
      augmented_images = layer(images)
      ```
      """
  
      def __init__(self, factor, seed=None, **kwargs):
          super().__init__(**kwargs)
          if isinstance(factor, float, int):
              raise ValueError(
                  "RandomJpegQuality() expects factor to be a 2 element "
                  "tuple, list or a `keras_cv.FactorSampler`. "
                  "RandomJpegQuality() received `factor={factor}`."
              )
          self.factor = preprocessing.parse_factor(
              factor, min_value=0, max_value=100, param_name="factor", seed=self.seed
          )
  
      def get_random_transformation(self, image=None, label=None, bounding_box=None):
          return self.factor(dtype=tf.int32)
  
      def augment_image(self, image, transformation=None):
          jpeg_quality = transformation
          return tf.image.adjust_jpeg_quality(image, jpeg_quality)
  
      def get_config(self):
          config = super().get_config()
          config.update({"factor": self.factor})
          return config
  
subject: Update lint