code: def _kstest(self, concentration, rate, samples):
      # Uses the Kolmogorov-Smirnov test for goodness of fit.
      oracle_dist = exp_gamma_lib.ExpGamma(
          concentration=concentration, rate=rate
      )
      ks, _ = sp_stats.kstest(
          samples, lambda x: self.evaluate(oracle_dist.cdf(x))
      )
      # Return True when the test passes.
      return ks < 0.02
  
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 18 for calling the ExpGammaTest obj's underlying member method code with example usage: obj._kstest(concentration, rate, samples) and returns: unknown