code: def testSample(self):
      alpha = [1.0, 2]
      dist = dirichlet.Dirichlet(alpha)
      n = tf.constant(100000)
      samples = dist.sample(n, seed=test_util.test_seed())
      sample_values = self.evaluate(samples)
      self.assertEqual(sample_values.shape, (100000, 2))
      self.assertTrue(np.all(sample_values > 0.0))
      self.assertLess(
          sp_stats.kstest(
              # Beta is a univariate distribution.
              sample_values[:, 0],
              sp_stats.beta(a=1.0, b=2.0).cdf,
          )[0],
          0.01,
      )
  
imports: import numpy
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 21 for calling the DirichletTest obj's underlying member method code with example usage: obj.testSample() without return types