code: def testSampleXLA(self):
      self.skip_if_no_xla()
  
      @tf.function(jit_compile=True)
      def f(loc):
          return truncated_normal.TruncatedNormal(
              loc=loc, scale=1.0, low=-1.0, high=1.0
          ).sample([3], seed=test_util.test_seed())
  
      self.evaluate(f(tf.constant(0.2)))
  
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 20 for calling the TruncatedNormalStandaloneTestCase obj's underlying member method code with example usage: obj.testSampleXLA() and returns: truncated_normal