code: def testValidateArgs(self):
      x = fswc.ContinuousAndCategoricalValues(
          np.random.normal(size=(5, 3)),
          np.random.randint(10, size=(5, 1)),
      )
      scale_diag_parameter = fswc.ContinuousAndCategoricalValues(
          [-1.0, 3.0, 4.0], [2.0]
      )
      with self.assertRaisesOpError(
          "`inverse_scale_diag` must be non-negative"
      ):
          k = fswc.FeatureScaledWithCategorical(
              exponentiated_quadratic.ExponentiatedQuadratic(),
              inverse_scale_diag=scale_diag_parameter,
              validate_args=True,
          )
          self.evaluate(k.apply(x, x))
  
      with self.assertRaisesOpError("`scale_diag` must be positive"):
          k = fswc.FeatureScaledWithCategorical(
              exponentiated_quadratic.ExponentiatedQuadratic(),
              scale_diag=scale_diag_parameter,
              validate_args=True,
          )
          self.evaluate(k.apply(x, x))
  
file_path: probability/tensorflow_probability/python/experimental/psd_kernels/feature_scaled_with_categorical_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.psd_kernels import feature_scaled_with_categorical
  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 feature_scaled
  from tensorflow_probability.python.math.psd_kernels import matern
label_desc: Use image node_id 5 for calling the FeatureScaledWithCategoricalTest obj's underlying member method code with example usage: obj.testValidateArgs() without return types