code: def testQuantile(self):
      dist = self.make_two_piece_normals()
  
      x = np.array([[0.000001], [0.5], [0.999999]], dtype=self.dtype)
  
      quantile = self.evaluate(dist.quantile(x))
      # The following values were calculated using the R package 'gamlss.dist'.
      # Package version: 5.3-2. Distribution: SN2.
      expected_quantile = np.array(
          [
              [-61.040950, -44.53424, -32.24254],
              [-0.7025392, 3.0000000, 6.6688350],
              [38.1495200, 50.534240, 66.876050],
          ],
          dtype=self.dtype,
      )
  
      self.assertAllEqual(quantile.shape, expected_quantile.shape)
      self.assertAllClose(
          a=quantile,
          b=expected_quantile,
          rtol=1e-03 if self.dtype == np.float32 else 1e-06,
      )
  
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 13 for calling the _TwoPieceNormalTest obj's underlying member method code with example usage: obj.testQuantile() without return types