code: def testInvalidScale(self):
      scale = [-0.01, 0.0, 2.0]
      with self.assertRaisesOpError(
          "Argument `scale` must be positive."
      ):
          dist = gev.GeneralizedExtremeValue(
              loc=0.0,
              scale=scale,
              concentration=1.0,
              validate_args=True,
          )
          self.evaluate(dist.mean())
  
      scale = tf.Variable([0.01])
      self.evaluate(scale.initializer)
      dist = gev.GeneralizedExtremeValue(
          loc=0.0, scale=scale, concentration=1.0, 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.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gev
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 3 for calling the _GEVTest obj's underlying member method code with example usage: obj.testInvalidScale() without return types