code: def testEmptyInputs(self):
      dim = 4
      n_pts = 3
      isd_empty = np.ones([]).astype(np.float32)
      x_cont_empty = np.ones([3, 0]).astype(np.float32)
      x_cat_empty = np.ones([3, 0]).astype(np.int32)
      base_kernel = exponentiated_quadratic.ExponentiatedQuadratic()
  
      isd_categorical = np.array([dim]).astype(np.float32)
      kernel_empty_cont = fswc.FeatureScaledWithCategorical(
          base_kernel,
          inverse_scale_diag=fswc.ContinuousAndCategoricalValues(
              isd_empty, isd_categorical
          ),
          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 = fswc.FeatureScaledWithCategorical(
          base_kernel,
          inverse_scale_diag=fswc.ContinuousAndCategoricalValues(
              isd_continuous, isd_empty
          ),
          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),
      )
  
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 6 for calling the FeatureScaledWithCategoricalTest obj's underlying member method code with example usage: obj.testEmptyInputs() without return types