code: def testNormalCdfAndSurvivalFunction(self):
      # At integer values, the result should be the same as the standard normal.
      batch_shape = (3, 3)
      mu = rng.randn(*batch_shape)
      sigma = rng.rand(*batch_shape) + 1.0
      qdist = qd.QuantizedDistribution(
          distribution=normal.Normal(loc=mu, scale=sigma),
          validate_args=True,
      )
      sp_normal = stats.norm(mu, sigma)
  
      x = rng.randint(-5, 5, size=batch_shape).astype(np.float64)
  
      self.assertAllClose(sp_normal.cdf(x), self.evaluate(qdist.cdf(x)))
  
      self.assertAllClose(
          sp_normal.sf(x), self.evaluate(qdist.survival_function(x))
      )
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import quantized_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 7 for calling the QuantizedDistributionTest obj's underlying member method code with example usage: obj.testNormalCdfAndSurvivalFunction() without return types