code: def testWeibullShape(self):
      concentration = np.array([1.0] * 5, dtype=self.dtype)
      scale = np.array([2.0] * 5, dtype=self.dtype)
      dist = weibull.Weibull(
          concentration=self.make_input(concentration),
          scale=self.make_input(scale),
          validate_args=True,
      )
      if self.use_static_shape:
          self.assertEqual(
              (5,), self.evaluate(dist.batch_shape_tensor())
          )
          self.assertEqual(tf.TensorShape([5]), dist.batch_shape)
          self.assertAllEqual(
              [], self.evaluate(dist.event_shape_tensor())
          )
          self.assertEqual(tf.TensorShape([]), dist.event_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 1 for calling the _WeibullTest obj's underlying member method code with example usage: obj.testWeibullShape() without return types