code: def test_hist_splitter_filter():
      """Test of hist_splitter option filter_empty_split_hists
  
      One of the split histograms of type date:A_score:num_employees is empty and only contains a NaN.
      In this test, those empty split-histograms are *not* removed, leading to split-histograms of
      inconsistent types.
      """
  
      hist_list = ["date:A_score:num_employees"]
      features = ["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,
                  filter_empty_split_hists=False,
              ),
          ]
      )
      datastore = pipeline.transform(datastore={})
  
      assert "output_hist" in datastore and isinstance(
          datastore["output_hist"], dict
      )
      assert len(datastore["output_hist"].keys()) == len(features)
      for f in features:
          assert f in datastore["output_hist"]
      for f in features:
          assert isinstance(datastore["output_hist"][f], pd.DataFrame)
  
      for f in features:
          df = datastore["output_hist"][f]
          split_list = df.reset_index().to_dict("records")
          hlist = [s["histogram"] for s in split_list]
          check = check_similar_hists(hlist)
          assert check is False
  
file_path: popmon/tests/popmon/hist/test_hist_splitter.py
imports: import pandas
  import pytest
  from popmon import resources
  from popmon.analysis.hist_numpy import assert_similar_hists, check_similar_hists
  from popmon.base import Pipeline
  from popmon.hist.hist_splitter import HistSplitter
  from popmon.io import JsonReader
label_desc: Use image node_id 2 for calling a global function with example usage: test_hist_splitter_filter() without return types