code: def testUniformCDF(self):
      batch_size = 6
      a = tf.constant([1.0] * batch_size)
      b = tf.constant([11.0] * batch_size)
      a_v = 1.0
      b_v = 11.0
      x = np.array([-2.5, 2.5, 4.0, 0.0, 10.99, 12.0], dtype=np.float32)
  
      dist = uniform.Uniform(low=a, high=b, validate_args=False)
  
      def _expected_cdf():
          cdf = (x - a_v) / (b_v - a_v)
          cdf[x >= b_v] = 1
          cdf[x < a_v] = 0
          return cdf
  
      cdf = dist.cdf(x)
      self.assertAllClose(_expected_cdf(), self.evaluate(cdf))
  
      log_cdf = dist.log_cdf(x)
      self.assertAllClose(
          np.log(_expected_cdf()), self.evaluate(log_cdf)
      )
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  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 5 for calling the UniformTest obj's underlying member method code with example usage: obj.testUniformCDF() and returns: cdf