code: def testSampleHighConcentration(self):
      concentration = np.linspace(10.0, 20.0, 10)
      rate = np.float64(1.0)
      num_samples = int(1e5)
      self.assertLess(
          self.evaluate(
              st.min_num_samples_for_dkwm_cdf_test(
                  discrepancy=0.04,
                  false_fail_rate=1e-9,
                  false_pass_rate=1e-9,
              )
          ),
          num_samples,
      )
  
      d = exp_gamma_lib.ExpGamma(
          concentration=concentration, rate=rate, validate_args=True
      )
      samples = d.sample(num_samples, seed=test_util.test_seed())
      self.evaluate(
          st.assert_true_cdf_equal_by_dkwm(
              samples, d.cdf, false_fail_rate=1e-9
          )
      )
  
      self.assertAllMeansClose(
          self.evaluate(samples), d.mean(), axis=0, rtol=0.01
      )
      self.assertAllClose(
          self.evaluate(tf.math.reduce_variance(samples, axis=0)),
          d.variance(),
          rtol=0.05,
      )
  
imports: 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 exp_gamma
  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 test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 4 for calling the GammaSamplingTest obj's underlying member method code with example usage: obj.testSampleHighConcentration() without return types