code: def testReproduceVmap1(self, dtype):
      # Regression test for b/145554459
      loc = tf.constant(-200.0, dtype=dtype)
      scale = tf.constant(2.188274e01, dtype=dtype)
      high = tf.constant(113.33857, dtype=dtype)
      low = tf.constant(102.94414, dtype=dtype)
      # Not validating args b/c the assertions confuse pfor.
      dist = truncated_normal.TruncatedNormal(
          loc, scale, low, high, validate_args=False
      )
      sample = tf.constant(
          [102.950745, 103.87256, 107.78299], dtype=dtype
      )
      batch_lp = dist.log_prob(sample)
      pfor_lp = tf.vectorized_map(dist.log_prob, sample)
      batch_lp_, pfor_lp_ = self.evaluate((batch_lp, pfor_lp))
      self.assertAllClose(batch_lp_, pfor_lp_, atol=2e-6)
  
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 1 for calling the TruncatedNormalTestGraphMode obj's underlying member method code with example usage: obj.testReproduceVmap1(dtype) without return types