code: def test_match_optimizer(generate_unmatched_data):
      df, features = generate_unmatched_data()
  
      optimizer = MatchOptimizer(
          treatment_col=TREATMENT_COL,
          ps_col=SCORE_COL,
          matching_covariates=[SCORE_COL],
          min_users_per_group=100,
          smd_cols=[SCORE_COL],
          dev_cols_transformations={SCORE_COL: np.mean},
      )
  
      matched = optimizer.search_best_match(df)
  
      assert sum(matched[TREATMENT_COL] == 0) == sum(
          matched[TREATMENT_COL] != 0
      )
  
file_path: causalml/tests/test_match.py
imports: import numpy
  import pandas
  import pytest
  from causalml.match import NearestNeighborMatch, MatchOptimizer
  from causalml.propensity import ElasticNetPropensityModel
  from .const import RANDOM_SEED, TREATMENT_COL, SCORE_COL, GROUP_COL
label_desc: Use image node_id 3 for calling a global function with example usage: test_match_optimizer(generate_unmatched_data) without return types