code: def setUp(self):
      super(BernoulliNormalCDFTest, self).setUp()
      self.dtype = np.float32
      self.model = family.BernoulliNormalCDF()
  
      def normal_cdf(r):
          r = tf.convert_to_tensor(value=r, name="r")
          n = normal.Normal(
              loc=tf.zeros([], r.dtype), scale=tf.ones([], r.dtype)
          )
          return n.cdf(r)
  
      self.expected = family.CustomExponentialFamily(
          lambda mean: bernoulli.Bernoulli(probs=mean), normal_cdf
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import binomial
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import negative_binomial
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.glm import family
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 1 for calling the BernoulliNormalCDFTest obj's underlying member method code with example usage: obj.setUp() and returns: n