code: def testEmptyInputs(self):
      dim = 4
      n_pts = 3
      x_cont_empty = np.ones([3, 0], dtype=np.float32)
      x_cat_empty = np.ones([3, 0], dtype=np.int32)
      base_kernel = exponentiated_quadratic.ExponentiatedQuadratic()
  
      isd_categorical = [
          tf.linalg.LinearOperatorIdentity(5, dtype=np.float32)
      ] * dim
      kernel_empty_cont = fswec.FeatureScaledWithEmbeddedCategorical(
          base_kernel,
          categorical_embedding_operators=isd_categorical,
          continuous_inverse_scale_diag=np.ones([], dtype=np.float32),
          validate_args=True,
      )
      x_cat = np.ones((n_pts, dim)).astype(np.int32)
      x_empty_cont = fswc.ContinuousAndCategoricalValues(
          x_cont_empty, x_cat
      )
  
      # Distances between points are 0 so kernel matrix containes ones.
      self.assertAllClose(
          kernel_empty_cont.matrix(x_empty_cont, x_empty_cont),
          np.ones((n_pts, n_pts)),
      )
  
      x_cont = np.random.normal(size=(4, n_pts, dim)).astype(np.float32)
      isd_continuous = np.ones([dim], dtype=np.float32)
      kernel_empty_cat = fswec.FeatureScaledWithEmbeddedCategorical(
          base_kernel,
          continuous_inverse_scale_diag=isd_continuous,
          categorical_embedding_operators=[],
          validate_args=True,
      )
      x_empty_cat = fswc.ContinuousAndCategoricalValues(
          x_cont, x_cat_empty
      )
  
      # Without categorical data, the kernel matrix should be the same as the base
      # kernel matrix for continuous data.
      self.assertAllClose(
          kernel_empty_cat.matrix(x_empty_cat, x_empty_cat),
          base_kernel.matrix(x_cont, x_cont),
      )
  
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 8 for calling the FeatureScaledWithCategoricalTest obj's underlying member method code with example usage: obj.testEmptyInputs() without return types