code: def testLogPdfOnBoundaryIsFiniteWhenAlphaIsOne(self):
      # Test concentration = 1. for each dimension.
      concentration = 3 * np.ones((10, 10)).astype(np.float32)
      concentration[range(10), range(10)] = 1.0
      x = 1 / 9.0 * np.ones((10, 10)).astype(np.float32)
      x[range(10), range(10)] = 0.0
      dist = dirichlet_lib.Dirichlet(concentration)
      log_prob = self.evaluate(dist.log_prob(x))
      self.assertAllEqual(
          np.ones_like(log_prob, dtype=np.bool_), np.isfinite(log_prob)
      )
  
      # Test when concentration[k] = 1., and x is zero at various dimensions.
      dist = dirichlet_lib.Dirichlet(10 * [1.0])
      log_prob = self.evaluate(dist.log_prob(x))
      self.assertAllEqual(
          np.ones_like(log_prob, dtype=np.bool_), np.isfinite(log_prob)
      )
  
imports: import importlib
  import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.distributions import dirichlet
  from tensorflow.python.ops.distributions import kullback_leibler
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 5 for calling the DirichletTest obj's underlying member method code with example usage: obj.testLogPdfOnBoundaryIsFiniteWhenAlphaIsOne() without return types