code: def test_column_partition_numerical(diabetes):
      # Arrange
      train, _ = diabetes
      # Act
      filters = partition_column(train, "age", 5)
      filter_results = [
          {"count": v.filter(train.data).size, "label": v.label}
          for v in filters
      ]
      # Assert
      h.assert_that(
          filter_results,
          h.has_items(
              h.has_entries({"count": 583, "label": "[-0.11 - -0.05)"}),
              h.has_entries(
                  {"count": 682, "label": "[-0.05 - -5.51E-3)"}
              ),
              h.has_entries(
                  {"count": 605, "label": "[-5.51E-3 - 0.02)"}
              ),
              h.has_entries({"count": 726, "label": "[0.02 - 0.05)"}),
              h.has_entries({"count": 660, "label": "[0.05 - 0.11]"}),
          ),
      )
  
imports: import hamcrest
  import pandas
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.utils.performance.partition import partition_column
label_desc: Use image node_id 1 for calling a global function with example usage: test_column_partition_numerical(diabetes) without return types