code: def test_causalml_smoke(self):
      from causalml.inference.tree import UpliftRandomForestClassifier
      from causalml.dataset import make_uplift_classification
      import causalml
  
      print("Causalml library smoke test")
      print("Version of causalml: {0}".format(causalml.__version__))
      n_samples = 500
      seed = 12345
  
      train, x_names = make_uplift_classification(
          n_samples=n_samples,
          treatment_name=["control", "treatment"],
          n_classification_features=10,
          n_classification_informative=10,
          random_seed=seed,
      )
      assert not train.empty
      assert x_names
  
      treatment_column = "treatment_group_key"
      response_column = "conversion"
      train[treatment_column] = train[treatment_column].astype(str)
      uplift_model = UpliftRandomForestClassifier(
          n_estimators=5,
          max_depth=8,
          evaluationFunction="KL",
          control_name="control",
          min_samples_leaf=10,
          min_samples_treatment=10,
          normalization=False,
          random_state=42,
      )
      uplift_model.fit(
          train[x_names].values,
          treatment=train[treatment_column].values,
          y=train[response_column].values,
      )
  
      assert uplift_model
  
imports: import sys
  import unittest
label_desc: Use image node_id 1 for calling the TestCausalmlIsWorking obj's underlying member method code with example usage: obj.test_causalml_smoke() without return types