code: def test_clip_by_value_preserve_grad(self, x, lo, hi, expected_y):
      expected_dydx = np.ones_like(x)
      x = tf.convert_to_tensor(x, dtype=self.dtype)
      y, dydx = gradient.value_and_gradient(
          lambda x_: numeric.clip_by_value_preserve_gradient(
              x_, lo, hi
          ),
          x,
      )
      y_, dydx_ = self.evaluate([y, dydx])
      self.assertAllClose(expected_y, y_)
      self.assertAllClose(expected_dydx, dydx_)
  
file_path: probability/tensorflow_probability/python/math/numeric_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math import numeric
label_desc: Use image node_id 1 for calling the ClipByValuePreserveGrad32 obj's underlying member method code with example usage: obj.test_clip_by_value_preserve_grad(x, lo, hi, expected_y) without return types