code: def testBatchSamplesAreIndependent(self):
      num_samples = 1000
      d = johnson_su.JohnsonSU(
          loc=[0.0, 0.0], scale=1.0, skewness=0.0, tailweight=1.0
      )
      xs = d.sample(num_samples, seed=test_util.test_seed())
      cov = 1.0 / num_samples * tf.matmul(xs, xs, transpose_a=True)
      self.assertAllClose(cov / d.variance(), tf.eye(2), atol=0.4)
  
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 30 for calling the JohnsonSUTest obj's underlying member method code with example usage: obj.testBatchSamplesAreIndependent() without return types