code: def testValidateArgs(self):
      x = fswc.ContinuousAndCategoricalValues(
          np.random.normal(size=(5, 3)), np.zeros(shape=(5, 0))
      )
      scale_diag_parameter = np.array([-1.0, 3.0, 4.0])
      with self.assertRaisesOpError(
          "`continuous_inverse_scale_diag` must be non-negative"
      ):
          k = fswec.FeatureScaledWithEmbeddedCategorical(
              exponentiated_quadratic.ExponentiatedQuadratic(),
              categorical_embedding_operators=[],
              continuous_inverse_scale_diag=scale_diag_parameter,
              validate_args=True,
          )
          self.evaluate(k.apply(x, x))
  
      with self.assertRaisesOpError(
          "`continuous_scale_diag` must be positive"
      ):
          k = fswec.FeatureScaledWithEmbeddedCategorical(
              exponentiated_quadratic.ExponentiatedQuadratic(),
              categorical_embedding_operators=[],
              continuous_scale_diag=scale_diag_parameter,
              validate_args=True,
          )
          self.evaluate(k.apply(x, x))
  
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.experimental.psd_kernels import feature_scaled_with_embedded_categorical
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  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 7 for calling the FeatureScaledWithCategoricalTest obj's underlying member method code with example usage: obj.testValidateArgs() without return types