class_docstr: Tests correctness of the weibull bijector.
class_docstr_tok: ["Tests","correctness","of","the","weibull","bijector","."]
code: def testVariableAssertsConcentration(self):
      concentration = tf.Variable(1.0)
      scale = tf.Variable(1.0)
      b = weibull_cdf.WeibullCDF(
          concentration=concentration, scale=scale, validate_args=True
      )
      self.evaluate([concentration.initializer, scale.initializer])
      with self.assertRaisesOpError(
          "Argument `concentration` must be positive."
      ):
          with tf.control_dependencies([concentration.assign(-1.0)]):
              self.evaluate(b.inverse(0.5))
  
file_path: probability/tensorflow_probability/python/bijectors/weibull_cdf_test.py
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import weibull_cdf
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 7 for calling the WeibullCDFBijectorTest obj's underlying member method code with example usage: obj.testVariableAssertsConcentration() without return types