code: def generate_classification_data_two_treatments():
      generated = False
  
      def _generate_data():
          if not generated:
              np.random.seed(RANDOM_SEED)
              data = make_uplift_classification(
                  n_samples=N_SAMPLE,
                  treatment_name=TREATMENT_NAMES[0:2],
                  y_name=CONVERSION,
                  random_seed=RANDOM_SEED,
                  delta_uplift_increase_dict=DELTA_UPLIFT_INCREASE_DICT,
                  n_uplift_increase_dict=N_UPLIFT_INCREASE_DICT,
              )
  
          return data
  
      yield _generate_data
  
file_path: causalml/tests/conftest.py
imports: import numpy
  import pytest
  from causalml.dataset import synthetic_data
  from causalml.dataset import make_uplift_classification
  from .const import RANDOM_SEED, N_SAMPLE, TREATMENT_NAMES, CONVERSION, DELTA_UPLIFT_INCREASE_DICT, N_UPLIFT_INCREASE_DICT
label_desc: Use image node_id 3 for calling a global function with example usage: generate_classification_data_two_treatments() and returns: data