code: def test_iris_tree_to_filters(iris_dataset):
      model = DecisionTreeRegressor(max_depth=2)
      data = iris_dataset.features_columns.iloc[:, 2:]
      model.fit(data, iris_dataset.label_col)
      leaves = convert_tree_leaves_into_filters(
          model.tree_, list(data.columns)
      )
      leaf0_samples = leaves[0].filter(data)
      assert_that(len(leaf0_samples), equal_to(50))
      leaf1_samples = leaves[1].filter(data)
      assert_that(len(leaf1_samples), equal_to(54))
      leaf2_samples = leaves[2].filter(data)
      assert_that(len(leaf2_samples), equal_to(46))
  
      leaves_combined = pd.concat(
          [leaf0_samples, leaf1_samples, leaf2_samples]
      ).drop_duplicates()
      assert_that(
          len(leaves_combined), equal_to(len(data.drop_duplicates()))
      )
  
file_path: deepchecks/tests/utils/partition_tests.py
imports: import pandas
  from hamcrest import assert_that, equal_to
  from sklearn.tree import DecisionTreeRegressor
  from deepchecks.utils.performance.partition import DeepchecksFilter, convert_tree_leaves_into_filters, intersect_two_filters
label_desc: Use image node_id 1 for calling a global function with example usage: test_iris_tree_to_filters(iris_dataset) without return types