code: def test_UpliftTreeClassifierMultipleTreatments(
      generate_classification_data, evaluation_function
  ):
      df, x_names = generate_classification_data()
      UpliftTreeClassifierTesting(df, x_names, evaluation_function)
  
file_path: causalml/tests/test_uplift_trees.py
imports: import cProfile
  import numpy
  import pandas
  import pytest
  import pstats
  from joblib import parallel_backend
  from sklearn.model_selection import train_test_split
  from causalml.inference.tree import UpliftTreeClassifier, UpliftRandomForestClassifier
  from causalml.metrics import get_cumgain
  from causalml.dataset import make_uplift_classification
  from causalml.inference.tree import uplift_tree_string, uplift_tree_plot
  from .const import RANDOM_SEED, N_SAMPLE, CONTROL_NAME, TREATMENT_NAMES, CONVERSION
label_desc: Use image node_id 4 for calling a global function with example usage: test_UpliftTreeClassifierMultipleTreatments(generate_classification_data, evaluation_function) without return types