code: def make_data():
      data = {
          "age": [20, 23, 26, 54, 100],
          "lymph node size": [2, 1, 3, 4, 1],
          "lymph node spread": [
              "distant",
              "none",
              "distant",
              "close",
              "none",
          ],
          "metastasis": ["yes", "no", "yes", "yes", "no"],
      }
  
      def _make_data(
          with_ordinal=True, with_nominal=True, with_continuous=True
      ):
          data_s = {}
          if with_continuous:
              data_s["age"] = data["age"]
  
          if with_ordinal:
              data_s["lymph node size"] = pd.Categorical(
                  data["lymph node size"],
                  categories=[1, 2, 3, 4],
                  ordered=True,
              )
              data_s["lymph node spread"] = pd.Categorical(
                  data["lymph node spread"],
                  categories=["none", "close", "distant"],
                  ordered=True,
              )
          if with_nominal:
              data_s["metastasis"] = pd.Categorical(
                  data["metastasis"],
                  categories=["no", "yes"],
                  ordered=False,
              )
          expected = _get_expected_matrix(
              with_ordinal=with_ordinal,
              with_nominal=with_nominal,
              with_continuous=with_continuous,
          )
  
          return pd.DataFrame(data_s), expected
  
      return _make_data
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal
  import pandas
  import pytest
  from sklearn.base import clone
  from sklearn.metrics.pairwise import pairwise_kernels
  from sksurv.kernels import ClinicalKernelTransform, clinical_kernel
label_desc: Use image node_id 2 for calling a global function with example usage: make_data() and returns: _make_data, pd,  expected