code: def testReproducibleGraph(self):
      pspherical = power_spherical.PowerSpherical(
          mean_direction=tf.math.l2_normalize(
              np.array([1.0, 2.0], dtype=self.dtype)
          ),
          concentration=self.dtype(1.2),
      )
      seed = test_util.test_seed()
      s1 = self.evaluate(pspherical.sample(50, seed=seed))
      if tf.executing_eagerly():
          tf.random.set_seed(seed)
      s2 = self.evaluate(pspherical.sample(50, seed=seed))
      self.assertAllEqual(s1, s2)
  
imports: import numpy
  from scipy import special
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import power_spherical
  from tensorflow_probability.python.distributions import spherical_uniform
  from tensorflow_probability.python.distributions import von_mises_fisher
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 1 for calling the _PowerSphericalTest obj's underlying member method code with example usage: obj.testReproducibleGraph() without return types