code: def testCorrectnessExponentiatedQuadraticFullMatrix(self):
      base_kernel = matern.MaternFiveHalves()
      sd_continuous = np.ones([1], dtype=np.float32)
  
      num_categories = [6, 4]
      x1_cat = np.stack(
          [np.random.randint(n, size=(2,)) for n in num_categories],
          axis=-1,
      )
      x2_cat = np.stack(
          [np.random.randint(n, size=(5,)) for n in num_categories],
          axis=-1,
      )
  
      x1_cont = np.ones(x1_cat.shape[:-1] + (1,)).astype(np.float32)
      x2_cont = np.ones(x2_cat.shape[:-1] + (1,)).astype(np.float32)
      x1 = fswc.ContinuousAndCategoricalValues(x1_cont, x1_cat)
      x2 = fswc.ContinuousAndCategoricalValues(x2_cont, x2_cat)
      ops = [
          tf.linalg.LinearOperatorFullMatrix(
              np.random.uniform(
                  size=[num_categories[0], 3], high=10.0
              ).astype(np.float32)
          ),
          tf.linalg.LinearOperatorScaledIdentity(
              multiplier=np.float32(np.random.normal()),
              num_rows=num_categories[1],
          ),
      ]
      embedding_kernel = fswec.FeatureScaledWithEmbeddedCategorical(
          base_kernel,
          categorical_embedding_operators=ops,
          continuous_scale_diag=sd_continuous,
          validate_args=True,
      )
  
      actual_kern_mat = self.evaluate(embedding_kernel.matrix(x1, x2))
  
      x1_one_hot = [
          _make_one_hot(x1_cat[:, 0], num_categories[0]),
          _make_one_hot(x1_cat[:, 1], num_categories[1]),
      ]
      x2_one_hot = [
          _make_one_hot(x2_cat[:, 0], num_categories[0]),
          _make_one_hot(x2_cat[:, 1], num_categories[1]),
      ]
      x1_embeddings = tf.concat(
          [
              tf.matmul(x1_one_hot[0], ops[0].to_dense()),
              tf.matmul(x1_one_hot[1], ops[1].to_dense()),
          ],
          axis=-1,
      )
      x2_embeddings = tf.concat(
          [
              tf.matmul(x2_one_hot[0], ops[0].to_dense()),
              tf.matmul(x2_one_hot[1], ops[1].to_dense()),
          ],
          axis=-1,
      )
      expected_kern_mat = base_kernel.matrix(
          x1_embeddings, x2_embeddings
      )
      self.assertAllClose(
          actual_kern_mat, expected_kern_mat, rtol=1e-5, atol=1e-5
      )
  
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 3 for calling the FeatureScaledWithCategoricalTest obj's underlying member method code with example usage: obj.testCorrectnessExponentiatedQuadraticFullMatrix() without return types