code: def testCdfMultidimensional(self):
      probs = np.array(
          [0.0, 0.1, 0.3, 0.7, 0.9, 1.0], dtype=np.float32
      )[..., np.newaxis]
      logits = np.log(probs) - np.log1p(-probs)
      dist = continuous_bernoulli.ContinuousBernoulli(logits=logits)
      x = np.array(
          [-1.0, 0.0, 0.1, 0.3, 0.7, 0.9, 1.0, 2.0], dtype=np.float32
      )
      self.assertAllClose(self.evaluate(dist.cdf(x)), cdf(x, probs))
  
imports: import numpy
  from scipy import special
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import continuous_bernoulli
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 28 for calling the ContinuousBernoulliTest obj's underlying member method code with example usage: obj.testCdfMultidimensional() without return types