code: def testIncorrectBoundsFailsVarAssignment(self):
      # low is var
      dist = truncated_normal.TruncatedNormal(
          loc=0.0,
          scale=0.1,
          low=tf.Variable(-1.0),
          high=-0.5,
          validate_args=True,
      )
      self.evaluate([v.initializer for v in dist.variables])
      self.evaluate(dist.mean())
      with tf.control_dependencies([dist.low.assign(-0.1)]):
          with self.assertRaisesOpError("`low >= high`"):
              self.evaluate(dist.mean())
  
      # high is var
      dist = truncated_normal.TruncatedNormal(
          loc=0.0,
          scale=0.1,
          low=-1.0,
          high=tf.Variable(-0.5),
          validate_args=True,
      )
      self.evaluate([v.initializer for v in dist.variables])
      self.evaluate(dist.mean())
      with tf.control_dependencies([dist.high.assign(-1.1)]):
          with self.assertRaisesOpError("`low >= high`"):
              self.evaluate(dist.mean())
  
      # both are vars
      dist = truncated_normal.TruncatedNormal(
          loc=0.0,
          scale=0.1,
          low=tf.Variable(-1.0),
          high=tf.Variable(-0.5),
          validate_args=True,
      )
      self.evaluate([v.initializer for v in dist.variables])
      self.evaluate(dist.mean())
      with tf.control_dependencies([dist.high.assign(-1.0)]):
          with self.assertRaisesOpError("`low >= high`"):
              self.evaluate(dist.mean())
  
imports: import itertools
  import unittest
  from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import truncated_normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 13 for calling the TruncatedNormalStandaloneTestCase obj's underlying member method code with example usage: obj.testIncorrectBoundsFailsVarAssignment() without return types