code: def testInvalidScale(self):
      scale = self.make_input(
          np.array([-0.01, 0.0, 2.0], dtype=self.dtype)
      )
      with self.assertRaisesOpError(
          "Argument `scale` must be positive."
      ):
          dist = weibull.Weibull(
              concentration=1.0, scale=scale, validate_args=True
          )
          self.evaluate(dist.mean())
  
      scale = tf.Variable([0.01])
      self.evaluate(scale.initializer)
      dist = weibull.Weibull(
          concentration=1.0, scale=scale, validate_args=True
      )
      self.assertIs(scale, dist.scale)
      self.evaluate(dist.mean())
      with tf.control_dependencies([scale.assign([-0.01])]):
          with self.assertRaisesOpError(
              "Argument `scale` must be positive."
          ):
              self.evaluate(dist.mean())
  
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 2 for calling the _WeibullTest obj's underlying member method code with example usage: obj.testInvalidScale() without return types