code: def test_drop_duplicates_numeric_edge_cases(generator_helper):
      df = pd.DataFrame(
          {
              "A": [0, 1, 2, 3, 4],
              "B": ["0", "1", "2", "3", "4"],
              "C": [np.nan, 1, 2, 3, 4],
              "D": [np.nan, 1, 2, 3, 4],
              "E": [4, 3, 2, 1, 4],
              "F": [4, 3, 2, 1, 0],
              "G": [4, 3, 2, 1, 4],
              "H": [4.0, 3.0, 2.0, 1.0, 4.0],
              "I": [4.2, 3.6, -4.3, 1.7, 4.2],
              "J": [4.2, 3.6, -4.3, 1.7, 4.2],
          }
      )
  
      feature_generator = DropDuplicatesFeatureGenerator()
      feature_metadata_in = FeatureMetadata.from_df(df)
  
      # Drop D because C and D are identical and both numerical, and C comes earlier
      # Drop G and H because E and G are identical and both numerical, and G comes earlier
      # Drop J because I and J are identical and both numerical, and I comes earlier
      expected_dropped_1 = ["D", "G", "H", "J"]
      actual_dropped_1 = feature_generator._drop_duplicate_features(
          X=df, feature_metadata_in=feature_metadata_in
      )
      assert expected_dropped_1 == actual_dropped_1
  
      og_feature_metadata_in = FeatureMetadata.from_df(df).to_dict(
          inverse=True
      )
  
      expected_og_feature_metadata_in = {
          ("float", ()): ["C", "D", "H", "I", "J"],
          ("int", ()): ["A", "E", "F", "G"],
          ("object", ()): ["B"],
      }
  
      assert expected_og_feature_metadata_in == og_feature_metadata_in
  
      expected_feature_metadata_in_full = {
          ("float", ()): ["C", "I"],
          ("int", ()): ["A", "E", "F"],
          ("object", ()): ["B"],
      }
  
      expected_feature_metadata_full = {
          ("float", ()): ["C", "I"],
          ("int", ()): ["A", "E", "F"],
          ("object", ()): ["B"],
      }
  
      # When
      output_data = generator_helper.fit_transform_assert(
          input_data=df,
          generator=feature_generator,
          expected_feature_metadata_in_full=expected_feature_metadata_in_full,
          expected_feature_metadata_full=expected_feature_metadata_full,
      )
  
      for removed_feature in expected_dropped_1:
          assert removed_feature not in output_data.columns
  
file_path: autogluon/features/tests/features/generators/test_drop_duplicates.py
imports: import numpy
  import pandas
  from autogluon.common import FeatureMetadata
  from autogluon.features.generators import DropDuplicatesFeatureGenerator
label_desc: Use image node_id 3 for calling a global function with example usage: test_drop_duplicates_numeric_edge_cases(generator_helper) without return types