code: def testWeibullEntropy(self):
      concentration = np.array([7.8], dtype=self.dtype)
      scale = np.array([1.1], dtype=self.dtype)
  
      dist = weibull.Weibull(
          concentration=self.make_input(concentration),
          scale=self.make_input(scale),
          validate_args=True,
      )
  
      entropy = dist.entropy()
      expected_entropy = (
          np.euler_gamma * (1.0 - 1.0 / concentration)
          + np.log(scale)
          - np.log(concentration)
          + 1.0
      )
  
      self.assertAllClose(
          self.evaluate(entropy), expected_entropy, atol=1e-5, rtol=1e-5
      )  # relaxed tol for fp32 in JAX
      self.assertEqual(
          self.evaluate(dist.batch_shape_tensor()), entropy.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 4 for calling the _WeibullTest obj's underlying member method code with example usage: obj.testWeibullEntropy() without return types