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 tensorflow as tf
  
  from keras_cv.layers import preprocessing
  
  NUM_CLASSES = 10
  
  
  class RandomCutoutTest(tf.test.TestCase):
      def test_aggressive_shear_fills_at_least_some_pixels(self):
          img_shape = (50, 50, 3)
          xs = tf.stack(
              [2 * tf.ones(img_shape), tf.ones(img_shape)],
              axis=0,
          )
          xs = tf.cast(xs, tf.float32)
  
          fill_value = 0.0
          layer = preprocessing.RandomShear(
              x=(3, 3), seed=0, fill_mode="constant", fill_value=fill_value
          )
          xs = layer(xs)
  
          # Some pixels should be replaced with fill value
          self.assertTrue(tf.math.reduce_any(xs[0] == fill_value))
          self.assertTrue(tf.math.reduce_any(xs[0] == 2.0))
          self.assertTrue(tf.math.reduce_any(xs[1] == fill_value))
          self.assertTrue(tf.math.reduce_any(xs[1] == 1.0))
  
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
  
  from keras_cv.layers import preprocessing
  
  
  class RandomCutoutTest(tf.test.TestCase):
      def test_aggressive_shear_fills_at_least_some_pixels(self):
          img_shape = (50, 50, 3)
          xs = tf.stack(
              [2 * tf.ones(img_shape), tf.ones(img_shape)],
              axis=0,
          )
          xs = tf.cast(xs, tf.float32)
  
          fill_value = 0.0
          layer = preprocessing.RandomShear(
              x=(3, 3), seed=0, fill_mode="constant", fill_value=fill_value
          )
          xs = layer(xs)
  
          # Some pixels should be replaced with fill value
          self.assertTrue(tf.math.reduce_any(xs[0] == fill_value))
          self.assertTrue(tf.math.reduce_any(xs[0] == 2.0))
          self.assertTrue(tf.math.reduce_any(xs[1] == fill_value))
          self.assertTrue(tf.math.reduce_any(xs[1] == 1.0))
  
subject: Remove accidental num classes