code: def testLogNormalFromMeanVariance(self):
      loc = np.array([[[-3.0], [2.0]]], dtype=np.float32)
      scale = np.array([[[0.1]], [[1.0]]], dtype=np.float32)
      x = np.array([0.1, 7.0, 4.0], dtype=np.float32)
      mean = sp_stats.lognorm.mean(s=scale, scale=np.exp(loc))
      var = sp_stats.lognorm.var(s=scale, scale=np.exp(loc))
      lognormal_mean_var = (
          lognormal.LogNormal.experimental_from_mean_variance(
              mean, variance=var, validate_args=True
          )
      )
      expected_log_pdf = sp_stats.lognorm.logpdf(
          x, s=scale, scale=np.exp(loc)
      )
      log_pdf = lognormal_mean_var.log_prob(x)
      self.assertAllClose(
          expected_log_pdf, self.evaluate(log_pdf), rtol=2e-5
      )
      self.assertAllClose(
          mean, self.evaluate(lognormal_mean_var.mean())
      )
      self.assertAllClose(
          var, self.evaluate(lognormal_mean_var.variance())
      )
  
file_path: probability/tensorflow_probability/python/distributions/lognormal_test.py
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 9 for calling the LogNormalTest obj's underlying member method code with example usage: obj.testLogNormalFromMeanVariance() without return types