text: # Copyright 2018 The TensorFlow Probability 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
  #
  #     http://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.
  # ============================================================================
  """Tests for Gradient."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  # Dependency imports
  import numpy as np
  
  import tensorflow.compat.v2 as tf
  import tensorflow_probability as tfp
  
  from tensorflow.python.framework import test_util  # pylint: disable=g-direct-tensorflow-import
  
  
  @test_util.run_all_in_graph_and_eager_modes
  class GradientTest(tf.test.TestCase):
  
    def test_non_list(self):
      f = lambda x: x**2 / 2
      g = lambda x: x
      x = np.concatenate([np.linspace(-100, 100, int(1e1)), [0]], axis=0)
      y, dydx = self.evaluate(tfp.math.value_and_gradient(f, x))
      self.assertAllClose(f(x), y, atol=1e-6, rtol=1e-6)
      self.assertAllClose(g(x), dydx, atol=1e-6, rtol=1e-6)
  
    def test_list(self):
      f = lambda x, y: x * y
      g = lambda x, y: [y, x]
      args = [np.linspace(0, 100, int(1e1)),
              np.linspace(-100, 0, int(1e1))]
      y, dydx = self.evaluate(tfp.math.value_and_gradient(f, args))
      self.assertAllClose(f(*args), y, atol=1e-6, rtol=1e-6)
      self.assertAllClose(g(*args), dydx, atol=1e-6, rtol=1e-6)
  
    def test_output_gradients(self):
      jacobian = np.float32([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]])
      f = lambda x: tf.squeeze(tf.matmul(jacobian, x[:, tf.newaxis]))
      x = np.ones([3], dtype=np.float32)
      output_gradients = np.float32([1., 2., 3.])
      y, dydx = self.evaluate(
          tfp.math.value_and_gradient(f, x, output_gradients=output_gradients))
      self.assertAllClose(f(x), y, atol=1e-6, rtol=1e-6)
      self.assertAllClose(
          np.dot(output_gradients, jacobian), dydx, atol=1e-6, rtol=1e-6)
  
  
  if __name__ == '__main__':
    tf.test.main()
  