code: def test_project_lambda_smoke_positives():
      # This is a repeat of the _negatives method but with
      # the '+' indices larger
      eqo = EqualizedOdds()
  
      events = ["label=False", "label=True"]
      signs = ["+", "-"]
      labels = ["a", "b"]
      midx = pd.MultiIndex.from_product(
          [signs, events, labels], names=[_SIGN, _EVENT, _GROUP_ID]
      )
  
      df = pd.DataFrame()
      # Note that the '-' indices are now smaller
      df = 0 + pd.Series(
          [200, 300, 100, 600, 4, 5, 6, 7], index=midx, dtype=np.float64
      )
  
      ls = eqo.project_lambda(df)
  
      expected = pd.DataFrame()
      expected = 0 + pd.Series(
          [196, 295, 94, 593, 0, 0, 0, 0], index=midx, dtype=np.float64
      )
      assert expected.equals(ls)
  
file_path: fairlearn/test/unit/reductions/moments/test_moments_equalized_odds.py
imports: import numpy
  import pandas
  from fairlearn.reductions import EqualizedOdds
  from fairlearn.reductions._moments.moment import _EVENT, _GROUP_ID, _SIGN
  from .data_generator import simple_binary_threshold_data
label_desc: Use image node_id 3 for calling a global function with example usage: test_project_lambda_smoke_positives() without return types