code: def testBetaFromMeanVariance(self):
      concentration1 = 2.0
      concentration0 = np.repeat(5.0, 3).astype(np.float32)
      x = np.array([0.1, 0.4, 0.75], dtype=np.float32)
      mu = sp_stats.beta.mean(a=concentration1, b=concentration0)
      var = sp_stats.beta.var(a=concentration1, b=concentration0)
      beta_mean_var = beta.Beta.experimental_from_mean_variance(
          mu, variance=var, validate_args=True
      )
      expected_log_pdf = sp_stats.beta.logpdf(
          x, a=concentration1, b=concentration0
      )
      log_pdf = beta_mean_var.log_prob(x)
      self.assertAllClose(expected_log_pdf, self.evaluate(log_pdf))
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 41 for calling the BetaTest obj's underlying member method code with example usage: obj.testBetaFromMeanVariance() without return types