code: def testValidateArgs(self):
      with self.assertRaisesOpError("must be positive"):
          k = gamma_exponential.GammaExponential(
              -1.0, 1.0, power=0.5, validate_args=True
          )
          self.evaluate(k.apply([1.0], [1.0]))
  
      with self.assertRaisesOpError("must be positive"):
          gamma_exponential.GammaExponential(
              2.0, -2.0, power=0.5, validate_args=True
          )
          self.evaluate(k.apply([1.0], [1.0]))
  
      with self.assertRaisesOpError("must be positive"):
          gamma_exponential.GammaExponential(
              2.0, 2.0, power=-1.0, validate_args=True
          )
          self.evaluate(k.apply([1.0], [1.0]))
  
      with self.assertRaisesOpError("must be <= 1"):
          gamma_exponential.GammaExponential(
              2.0, 2.0, power=1.1, validate_args=True
          )
          self.evaluate(k.apply([1.0], [1.0]))
  
      # But `None`'s are ok
      k = gamma_exponential.GammaExponential(
          None, None, power=None, validate_args=True
      )
      self.evaluate(k.apply([1.0], [1.0]))
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/gamma_exponential_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
  from tensorflow_probability.python.math.psd_kernels import gamma_exponential
label_desc: Use image node_id 6 for calling the GammaExponentialTest obj's underlying member method code with example usage: obj.testValidateArgs() without return types