code: def testQuantileFiniteGradientAtDifficultPoints(self, skewness):
      def quantile(loc, scale, skewness, probs):
          dist = two_piece_normal.TwoPieceNormal(
              loc, scale=scale, skewness=skewness, validate_args=True
          )
          return dist.quantile(probs)
  
      x = -17.0 if self.dtype == np.float32 else -33.0
      loc = tf.constant(0.0, self.dtype)
      scale = tf.constant(1.0, self.dtype)
      probs = tf.constant(
          [
              np.exp(x),
              np.exp(-2.0),
              1.0 - np.exp(-2.0),
              1.0 - np.exp(x),
          ],
          dtype=self.dtype,
      )
  
      value, grads = gradient.value_and_gradient(
          quantile,
          [loc, scale, tf.constant(skewness, self.dtype), probs],
      )
      self.assertAllFinite(value)
      self.assertAllFinite(grads[0])  # d/d loc
      self.assertAllFinite(grads[1])  # d/d scale
      self.assertAllFinite(grads[2])  # d/d skewness
      self.assertAllFinite(grads[3])
  
imports: import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import two_piece_normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 18 for calling the _TwoPieceNormalTest obj's underlying member method code with example usage: obj.testQuantileFiniteGradientAtDifficultPoints(skewness) and returns: dist