code: def testGammaFromMeanVariance(self):
      concentration = 2.0
      rate = np.array([0.5, 1.0, 2.0], dtype=np.float32)
      scale = 1.0 / rate
      x = np.array([0.1, 7.0, 4.0], dtype=np.float32)
      mean = sp_stats.gamma.mean(a=concentration, scale=scale)
      var = sp_stats.gamma.var(a=concentration, scale=scale)
      gamma_mean_var = gamma_lib.Gamma.experimental_from_mean_variance(
          mean, variance=var, validate_args=True
      )
      expected_log_pdf = sp_stats.gamma.logpdf(
          x, a=concentration, scale=scale
      )
      log_pdf = gamma_mean_var.log_prob(x)
      self.assertAllClose(expected_log_pdf, self.evaluate(log_pdf))
  
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 36 for calling the GammaTest obj's underlying member method code with example usage: obj.testGammaFromMeanVariance() without return types