code: def testNormalProbWithCutoffs(self):
      # At integer values, the result should be the same as the standard normal.
      qdist = qd.QuantizedDistribution(
          distribution=normal.Normal(loc=0.0, scale=1.0),
          low=-2.0,
          high=2.0,
          validate_args=True,
      )
      sm_normal = stats.norm(0.0, 1.0)
      # These cutoffs create partitions of the real line, and indices:
      # (-inf, -2](-2, -1](-1, 0](0, 1](1, inf)
      #        -2      -1      0     1     2
      # Test interval (-inf, -2], <--> index -2.
      self.assertAllClose(
          sm_normal.cdf(-2), self.evaluate(qdist.prob(-2.0)), atol=0
      )
      # Test interval (-2, -1], <--> index -1.
      self.assertAllClose(
          sm_normal.cdf(-1) - sm_normal.cdf(-2),
          self.evaluate(qdist.prob(-1.0)),
          atol=0,
      )
      # Test interval (-1, 0], <--> index 0.
      self.assertAllClose(
          sm_normal.cdf(0) - sm_normal.cdf(-1),
          self.evaluate(qdist.prob(0.0)),
          atol=0,
      )
      # Test interval (1, inf), <--> index 2.
      self.assertAllClose(
          1.0 - sm_normal.cdf(1), self.evaluate(qdist.prob(2.0)), atol=0
      )
  
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 11 for calling the QuantizedDistributionTest obj's underlying member method code with example usage: obj.testNormalProbWithCutoffs() without return types