code: def testExponentialCDF(self):
      batch_size = 6
      lam = tf.constant([2.0] * batch_size)
      lam_v = 2.0
      x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
  
      dist = exponential.Exponential(rate=lam, validate_args=True)
  
      cdf = dist.cdf(x)
      self.assertEqual(cdf.shape, (6,))
  
      expected_cdf = sp_stats.expon.cdf(x, scale=1 / lam_v)
      self.assertAllClose(self.evaluate(cdf), expected_cdf)
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 3 for calling the ExponentialTest obj's underlying member method code with example usage: obj.testExponentialCDF() without return types