code: def testVariableSkewness(self):
      loc = tf.constant(0.0, self.dtype)
      scale = tf.constant(1.0, self.dtype)
      skewness = tf.Variable(1.0, dtype=self.dtype)
      dist = two_piece_normal.TwoPieceNormal(
          loc=loc, scale=scale, skewness=skewness, validate_args=True
      )
  
      self.evaluate([v.initializer for v in dist.variables])
      self.assertIs(skewness, dist.skewness)
      self.assertEqual(0.0, self.evaluate(dist.mean()))
  
      with self.assertRaisesOpError(
          "Argument `skewness` must be positive."
      ):
          with tf.control_dependencies([skewness.assign(-1.0)]):
              self.evaluate(dist.mean())
  
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 24 for calling the _TwoPieceNormalTest obj's underlying member method code with example usage: obj.testVariableSkewness() without return types