code: def test_column_partition_numerical_with_dominant():
      # Arrange
      df = pd.DataFrame(
          data={
              "col": [
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  1,
                  5,
                  5,
                  8,
                  9,
                  10,
                  11,
              ]
          }
      )
      dataset = Dataset(df, cat_features=[])
      # Act
      filters = partition_column(dataset, "col", 5)
      filter_results = [
          {"count": v.filter(dataset.data).size, "label": v.label}
          for v in filters
      ]
      # Assert
      h.assert_that(
          filter_results,
          h.has_items(
              h.has_entries({"count": 16, "label": "[1 - 5)"}),
              h.has_entries({"count": 3, "label": "[5 - 8.9)"}),
              h.has_entries({"count": 3, "label": "[8.9 - 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 2 for calling a global function with example usage: test_column_partition_numerical_with_dominant() without return types