class_docstr: Test by comparing TruncatedNormals with wide bounds and unbounded Normal.
    
class_docstr_tok: ["Test","by","comparing","TruncatedNormals","with","wide","bounds","and","unbounded","Normal","."]
code: def testCDF(self, loc, scale):
      truncated_dist, normal_dist = self.constructDists(loc, scale)
      low = self.evaluate(truncated_dist.low)
      high = self.evaluate(truncated_dist.high)
      test_x = list(np.float32(np.random.uniform(low, high, 10)))
      test_x += [low, high, low + EPSILON, high - EPSILON]
      tr_cdf = self.evaluate(truncated_dist.cdf(test_x))
      n_cdf = self.evaluate(normal_dist.cdf(test_x))
      self.assertAllClose(tr_cdf, n_cdf, rtol=1e-4, atol=1e-4)
  
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 5 for calling the TruncatedNormalTestCompareWithNormal obj's underlying member method code with example usage: obj.testCDF(loc, scale) without return types