code: def testZipfSampleMultidimensionalMean(self):
      power_v = np.array([np.arange(5, 15, dtype=np.float32)])  # 1 x 10
      dist = zipf.Zipf(power=power_v, validate_args=True)
      n = int(100e3)
      samples = dist.sample(n, seed=test_util.test_seed())
      sample_values = self.evaluate(samples)
      self.assertEqual(
          (
              n,
              1,
              10,
          ),
          samples.shape,
      )
      self.assertEqual(
          (
              n,
              1,
              10,
          ),
          sample_values.shape,
      )
  
      # stats.zipf wants float64 params.
      stats_mean = np.vectorize(stats.zipf.mean)(
          power_v.astype(np.float64)
      )
      self.assertAllClose(
          sample_values.mean(axis=0), stats_mean, rtol=0.01
      )
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import zipf
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 27 for calling the ZipfTest obj's underlying member method code with example usage: obj.testZipfSampleMultidimensionalMean() without return types