code: def testDtypeAndShapeInheritedFromBaseDist(self):
      batch_shape = (2, 3)
      qdist = qd.QuantizedDistribution(
          distribution=normal.Normal(
              loc=tf.zeros(batch_shape), scale=tf.ones(batch_shape)
          ),
          low=1.0,
          high=10.0,
          validate_args=True,
      )
  
      self.assertEqual(batch_shape, qdist.batch_shape)
      self.assertAllEqual(
          batch_shape, self.evaluate(qdist.batch_shape_tensor())
      )
      self.assertEqual((), qdist.event_shape)
      self.assertAllEqual((), self.evaluate(qdist.event_shape_tensor()))
  
      samps = qdist.sample(10, seed=test_util.test_seed())
      self.assertEqual((10,) + batch_shape, samps.shape)
      self.assertAllEqual(
          (10,) + batch_shape, self.evaluate(samps).shape
      )
  
      y = rng.randint(0, 5, size=batch_shape).astype(np.float32)
      self.assertEqual(batch_shape, qdist.prob(y).shape)
  
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 19 for calling the QuantizedDistributionTest obj's underlying member method code with example usage: obj.testDtypeAndShapeInheritedFromBaseDist() without return types