code: def testPeakLowInequalityAfterMutation(self):
      low = tf.Variable([1.0, 2.0, 3.0])
      peak = tf.Variable([2.0, 3.0, 4.0])
      high = [5.0, 5.0, 5.0]
      self.evaluate(low.initializer)
      self.evaluate(peak.initializer)
      dist = pert.PERT(low, peak, high, validate_args=True)
      with self.assertRaisesOpError(
          "`peak` must be greater than `low`."
      ):
          with tf.control_dependencies([peak.assign([0.0, 0.0, 0.0])]):
              self.evaluate(dist.sample(1, seed=test_util.test_seed()))
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import pert
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 13 for calling the PERTTest obj's underlying member method code with example usage: obj.testPeakLowInequalityAfterMutation() without return types