code: def testNormalProbFiniteEvenForHugeValues(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),
          # validate_args=False to allow us to use float x, which is more likely
          # to result in rounding errors...and rounding errors caused the issue
          # tested in testLogisticLogProbThatUsedToUnderflowNoLongerDoes.
          validate_args=False,
      )
  
      x = tf.linspace(-10000.0, 10000.0, num=50)
  
      self.assertAllFinite(self.evaluate(qdist.log_prob(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 9 for calling the QuantizedDistributionTest obj's underlying member method code with example usage: obj.testNormalProbFiniteEvenForHugeValues() without return types