code: def testMode(self):
      alpha = np.array([1.1, 2, 3])
      expected_mode = (alpha - 1) / (np.sum(alpha) - 3)
      dist = dirichlet.Dirichlet(concentration=alpha)
      self.assertAllEqual([3], dist.mode().shape)
      self.assertAllClose(expected_mode, self.evaluate(dist.mode()))
  
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 17 for calling the DirichletTest obj's underlying member method code with example usage: obj.testMode() without return types