code: def testWeibullCDF(self):
      batch_size = 6
      concentration = np.array([3.0] * batch_size, dtype=self.dtype)
      scale = np.array([2.0] * batch_size, dtype=self.dtype)
      x = np.array([2.0, 3.0, 4.0, 5.0, 6.0, 7.0], dtype=self.dtype)
  
      dist = weibull.Weibull(
          concentration=self.make_input(concentration),
          scale=self.make_input(scale),
          validate_args=True,
      )
  
      log_cdf = dist.log_cdf(self.make_input(x))
      self.assertAllClose(
          self.evaluate(log_cdf),
          stats.weibull_min.logcdf(x, c=concentration, scale=scale),
      )
      self.assertEqual(
          self.evaluate(dist.batch_shape_tensor()), log_cdf.shape
      )
  
      cdf = dist.cdf(self.make_input(x))
      self.assertAllClose(
          self.evaluate(cdf),
          stats.weibull_min.cdf(x, c=concentration, scale=scale),
      )
      self.assertEqual(
          self.evaluate(dist.batch_shape_tensor()), cdf.shape
      )
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import weibull
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 6 for calling the _WeibullTest obj's underlying member method code with example usage: obj.testWeibullCDF() without return types