code: def testGammaGammaSampleMultidimensional(self):
      alpha_v = np.array(
          [np.arange(5, 25, 2, dtype=np.float32)]
      )  # 1 x 10
      alpha0_v = 5.0
      beta0_v = np.array(
          [np.arange(5, 15, 2, dtype=np.float32)]
      ).T  # 5 x 1
      n = 75000
  
      gg = gamma_gamma.GammaGamma(
          concentration=alpha_v,
          mixing_concentration=alpha0_v,
          mixing_rate=beta0_v,
          validate_args=True,
      )
      samples = gg.sample(n, seed=test_util.test_seed())
      sample_values = self.evaluate(samples)
      expected_shape = (n, beta0_v.shape[0], alpha_v.shape[-1])
      self.assertEqual(samples.shape, expected_shape)
      self.assertEqual(sample_values.shape, expected_shape)
      self.assertAllClose(
          self.evaluate(gg.variance()),
          sample_values.var(axis=0),
          rtol=0.6,
      )
      self.assertAllClose(
          self.evaluate(gg.mean()),
          sample_values.mean(axis=0),
          rtol=0.02,
      )
  
file_path: probability/tensorflow_probability/python/distributions/gamma_gamma_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gamma_gamma
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 13 for calling the GammaGammaTest obj's underlying member method code with example usage: obj.testGammaGammaSampleMultidimensional() without return types