code: def testPdfXProper(self):
      a = [[1.0, 2, 3]]
      b = [[2.0, 4, 3]]
      dist = beta.Beta(a, b, 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("Sample must be non-negative."):
          self.evaluate(dist.prob([-1.0, 0.1, 0.5]))
      with self.assertRaisesOpError(
          "Sample must be less than or equal to `1`."
      ):
          self.evaluate(dist.prob([0.1, 0.2, 1.2]))
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 6 for calling the BetaTest obj's underlying member method code with example usage: obj.testPdfXProper() without return types