code: def testJohnsonSUMean(self):
      skewness = [1.0]
      tailweight = [2.0]
      # Mu will be broadcast to [7, 7, 7].
      mu = [7.0]
      sigma = [11.0, 12.0, 13.0]
  
      dist = johnson_su.JohnsonSU(
          skewness=skewness,
          tailweight=tailweight,
          loc=mu,
          scale=sigma,
          validate_args=True,
      )
      self.assertAllEqual((3,), dist.mean().shape)
      # sp_stats doesn't work with array tailweight
      expected_mean = sp_stats.johnsonsu.mean(
          skewness, tailweight[0], mu, sigma
      )
      self.assertAllClose(expected_mean, self.evaluate(dist.mean()))
  
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 14 for calling the JohnsonSUTest obj's underlying member method code with example usage: obj.testJohnsonSUMean() without return types