code: def testGeneralizedNormalQuantile(self):
      batch_size = 50
      mu = self._rng.randn(batch_size)
      sigma = self._rng.rand(batch_size) + 1.0
      power = self._rng.rand(batch_size) + 1.0
      p = np.linspace(0.0, 1.0, batch_size).astype(np.float64)
      gnormal = generalized_normal.GeneralizedNormal(
          loc=self.make_input(mu),
          scale=self.make_input(sigma),
          power=self.make_input(power),
          validate_args=True,
      )
      quantile = gnormal.quantile(p)
      self.assertAllEqual(
          self.evaluate(gnormal.batch_shape_tensor()), quantile.shape
      )
      self.assertAllEqual(
          self.evaluate(gnormal.batch_shape_tensor()),
          self.evaluate(quantile).shape,
      )
      self.assertAllEqual(gnormal.batch_shape, quantile.shape)
      self.assertAllEqual(
          gnormal.batch_shape, self.evaluate(quantile).shape
      )
      expected_quantile = sp_stats.gennorm(
          power, loc=mu, scale=sigma
      ).ppf(p)
      self.assertAllClose(
          expected_quantile, self.evaluate(quantile), atol=0, rtol=1e-4
      )
  
imports: import math
  import numpy
  from scipy import stats
  from scipy.special import gamma
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import generalized_normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 10 for calling the _GeneralizedNormalTest obj's underlying member method code with example usage: obj.testGeneralizedNormalQuantile() without return types