code: def testPdfXProper(self):
      alpha = [[1.0, 2, 3]]
      dist = dirichlet.Dirichlet(alpha, validate_args=True)
      self.evaluate(dist.prob([0.1, 0.3, 0.6]))
      self.evaluate(dist.prob([0.2, 0.3, 0.5]))
      # Either condition can trigger.
      with self.assertRaisesOpError("Samples must be non-negative"):
          self.evaluate(dist.prob([-1.0, 1.5, 0.5]))
      with self.assertRaisesOpError(
          "Sample last-dimension must sum to `1`"
      ):
          self.evaluate(dist.prob([0.1, 0.2, 0.8]))
  
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 4 for calling the DirichletTest obj's underlying member method code with example usage: obj.testPdfXProper() without return types