code: def testGammaCDF(self):
      batch_size = 6
      concentration = tf.constant([2.0] * batch_size)
      rate = tf.constant([3.0] * batch_size)
      concentration_v = 2.0
      rate_v = 3.0
      x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], 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,
      )
      cdf = gamma.cdf(x)
      self.assertEqual(cdf.shape, (6,))
      expected_cdf = sp_stats.gamma.cdf(
          x, concentration_v, scale=1 / rate_v
      )
      self.assertAllClose(cdf, expected_cdf)
      self.assertAllClose(gamma_lr.cdf(x), expected_cdf)
  
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 8 for calling the GammaTest obj's underlying member method code with example usage: obj.testGammaCDF() without return types