code: def _testParamShapes(self, sample_shape, expected):
      param_shapes = normal.Normal.param_shapes(sample_shape)
      mu_shape, sigma_shape = param_shapes["loc"], param_shapes["scale"]
      self.assertAllEqual(expected, self.evaluate(mu_shape))
      self.assertAllEqual(expected, self.evaluate(sigma_shape))
      mu = tf.zeros(mu_shape)
      sigma = tf.ones(sigma_shape)
      self.assertAllEqual(
          expected,
          self.evaluate(
              tf.shape(
                  normal.Normal(mu, sigma, validate_args=True).sample(
                      seed=test_util.test_seed()
                  )
              )
          ),
      )
  
imports: import math
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 2 for calling the NormalTest obj's underlying member method code with example usage: obj._testParamShapes(sample_shape, expected) without return types