code: def testGammaQuantile(self):
      batch_size = 6
      concentration = np.linspace(1, 10.0, batch_size).astype(
          np.float32
      )[..., np.newaxis]
      rate = np.linspace(3.0, 7.0, batch_size).astype(np.float32)[
          ..., np.newaxis
      ]
      x = np.array(
          [0.1, 0.2, 0.3, 0.9, 0.8, 0.5, 0.7], dtype=np.float32
      )
  
      gamma = gamma_lib.Gamma(
          concentration=concentration, rate=rate, validate_args=True
      )
      gamma_lr = gamma_lib.Gamma(
          concentration=concentration,
          log_rate=tf.math.log(rate),
          validate_args=True,
      )
      quantile = gamma.quantile(x)
      self.assertEqual(quantile.shape, (6, 7))
      expected_quantile = sp_stats.gamma.ppf(
          x, concentration, scale=1 / rate
      )
      self.assertAllClose(quantile, expected_quantile)
      self.assertAllClose(gamma_lr.quantile(x), expected_quantile)
  
imports: import functools
  from absl.testing import parameterized
  import numpy
  from scipy import misc
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import implementation_selection
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 9 for calling the GammaTest obj's underlying member method code with example usage: obj.testGammaQuantile() without return types