code: def testCorrectnessExponentiatedQuadratic(self):
      base_kernel = exponentiated_quadratic.ExponentiatedQuadratic()
      sd_continuous = np.ones([1], dtype=np.float32)
  
      num_categories = [5, 3, 6, 4]
      x1_cat = np.stack(
          [np.random.randint(n, size=(10,)) for n in num_categories],
          axis=-1,
      )
      x2_cat = np.stack(
          [np.random.randint(n, size=(8,)) for n in num_categories],
          axis=-1,
      )
  
      x1_cont = np.ones([x1_cat.shape[0], 1]).astype(np.float32)
      x2_cont = np.ones([x2_cat.shape[0], 1]).astype(np.float32)
      x1 = fswc.ContinuousAndCategoricalValues(x1_cont, x1_cat)
      x2 = fswc.ContinuousAndCategoricalValues(x2_cont, x2_cat)
  
      ops = [
          tf.linalg.LinearOperatorDiag(
              np.random.uniform(
                  size=[num_categories[0]], high=10.0
              ).astype(np.float32)
          ),
          tf.linalg.LinearOperatorScaledIdentity(
              multiplier=np.float32(np.random.normal()),
              num_rows=num_categories[1],
          ),
          tf.linalg.LinearOperatorIdentity(
              num_rows=num_categories[2], dtype=np.float32
          ),
          tf.linalg.LinearOperatorDiag(
              np.random.uniform(
                  size=[num_categories[3]], high=10.0
              ).astype(np.float32)
          ),
      ]
      diagonal_kernel = fswec.FeatureScaledWithEmbeddedCategorical(
          base_kernel,
          categorical_embedding_operators=ops,
          continuous_scale_diag=sd_continuous,
          validate_args=True,
      )
  
      actual_kern_mat = self.evaluate(diagonal_kernel.matrix(x1, x2))
      expected_kern_mat = _naive_categorical_exponentiated_quadratic(
          x1_cat, x2_cat, [self.evaluate(op.diag_part()) for op in ops]
      )
      self.assertAllClose(actual_kern_mat, expected_kern_mat)
  
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 2 for calling the FeatureScaledWithCategoricalTest obj's underlying member method code with example usage: obj.testCorrectnessExponentiatedQuadratic() without return types