code: def test_normalized_hist_mean_cov():
      hist_list = [
          "date:country",
          "date:bankrupt",
          "date:num_employees",
          "date:A_score",
          "date:A_score:num_employees",
      ]
  
      pipeline = Pipeline(
          modules=[
              JsonReader(
                  file_path=resources.data("example_histogram.json"),
                  store_key="example_hist",
              ),
              HistSplitter(
                  read_key="example_hist",
                  store_key="output_hist",
                  features=hist_list,
              ),
              ApplyFunc(
                  apply_to_key="output_hist",
                  assign_to_key="output_hist",
                  apply_funcs=[
                      {"func": normalized_hist_mean_cov, "suffix": ""}
                  ],
              ),
          ]
      )
      datastore = pipeline.transform(datastore={})
  
      assert "output_hist" in datastore
      for f in [
          "A_score",
          "A_score:num_employees",
          "bankrupt",
          "country",
          "num_employees",
      ]:
          assert f in datastore["output_hist"]
  
      df = datastore["output_hist"]["A_score"]
  
      check = np.array(
          [
              [
                  0.22916667,
                  -0.01041667,
                  -0.0625,
                  -0.13541667,
                  -0.02083333,
              ],
              [
                  -0.01041667,
                  0.015625,
                  0.01041667,
                  -0.01354167,
                  -0.00208333,
              ],
              [-0.0625, 0.01041667, 0.12916667, -0.06458333, -0.0125],
              [
                  -0.13541667,
                  -0.01354167,
                  -0.06458333,
                  0.240625,
                  -0.02708333,
              ],
              [-0.02083333, -0.00208333, -0.0125, -0.02708333, 0.0625],
          ]
      )
  
      for hm, hc, hb in zip(
          df["histogram_mean"].values,
          df["histogram_cov"].values,
          df["histogram_binning"].values,
      ):
          np.testing.assert_array_almost_equal(
              hm, [0.3125, 0.03125, 0.1875, 0.40625, 0.0625]
          )
          np.testing.assert_array_almost_equal(
              hb, [1.5, 2.5, 3.5, 4.5, 5.5]
          )
          np.testing.assert_array_almost_equal(hc, check)
  
imports: import numpy
  import pandas
  import pytest
  from popmon import resources
  from popmon.analysis.apply_func import ApplyFunc
  from popmon.analysis.comparison import ExpandingNormHistComparer, ReferenceNormHistComparer, RollingNormHistComparer
  from popmon.analysis.functions import expand, expand_norm_hist_mean_cov, expanding_hist, normalized_hist_mean_cov, relative_chi_squared, roll, roll_norm_hist_mean_cov, rolling_hist
  from popmon.base import Pipeline
  from popmon.hist.hist_splitter import HistSplitter
  from popmon.io import JsonReader
label_desc: Use image node_id 5 for calling a global function with example usage: test_normalized_hist_mean_cov() without return types