code: def testJohnsonSULogCDF(self):
      batch_size = 50
      skewness = self._rng.randn(batch_size)
      tailweight = self._rng.rand(batch_size) + 1.0
      mu = self._rng.randn(batch_size)
      sigma = self._rng.rand(batch_size) + 1.0
      x = np.linspace(-100.0, 10.0, batch_size).astype(np.float64)
  
      dist = johnson_su.JohnsonSU(
          skewness=skewness,
          tailweight=tailweight,
          loc=mu,
          scale=sigma,
          validate_args=True,
      )
  
      cdf = dist.log_cdf(x)
      self.assertAllEqual(
          self.evaluate(dist.batch_shape_tensor()), cdf.shape
      )
      self.assertAllEqual(
          self.evaluate(dist.batch_shape_tensor()),
          self.evaluate(cdf).shape,
      )
      self.assertAllEqual(dist.batch_shape, cdf.shape)
      self.assertAllEqual(dist.batch_shape, self.evaluate(cdf).shape)
  
      expected_cdf = sp_stats.johnsonsu.logcdf(
          x, skewness, tailweight, mu, sigma
      )
      self.assertAllClose(
          expected_cdf, self.evaluate(cdf), atol=0, rtol=1e-3
      )
  
imports: import math
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import johnson_su
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 11 for calling the JohnsonSUTest obj's underlying member method code with example usage: obj.testJohnsonSULogCDF() without return types