class_docstr: Tests correctness of the Frechet bijector.
code: def testVariablesScaleAndconcentration(self):
      x = tf.Variable(1.0)
      y = tf.Variable(1.0)
      b = frechet_cdf.FrechetCDF(
          loc=0.0, scale=x, concentration=y, validate_args=True
      )
      self.evaluate(x.initializer)
      self.evaluate(y.initializer)
      self.assertIs(x, b.scale)
      self.assertIs(y, b.concentration)
      self.assertEqual((), self.evaluate(b.forward(3.0)).shape)
      with self.assertRaisesOpError(
          "Argument `scale` must be positive."
      ):
          with tf.control_dependencies([x.assign(-1.0)]):
              self.evaluate(b.forward(3.0))
      with self.assertRaisesOpError(
          "Argument `concentration` must be positive."
      ):
          with tf.control_dependencies([x.assign(1), y.assign(-1.0)]):
              self.evaluate(b.forward(3.0))
  
file_path: probability/tensorflow_probability/python/bijectors/frechet_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 frechet_cdf
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 4 for calling the FrechetCDFTest obj's underlying member method code with example usage: obj.testVariablesScaleAndconcentration() without return types