code: def test_simulate_t_learner_treatment_effect():
      df = pd.DataFrame(
          {
              "x1": [1.3, 1.0, 1.8, -0.1],
              "x2": [10, 4, 15, 6],
              "treatment": ["A", "B", "A", "control"],
              "target": [0, 0, 0, 1],
          }
      )
  
      treatments = ["A", "B"]
      control_name = "control"
      prediction_column = "prediction"
  
      control_learner = MagicMock()
      control_learner.side_effect = lambda _: pd.DataFrame(
          {"prediction": [1, 2, 3, 4]}
      )
  
      treatment_learner = MagicMock()
      treatment_learner.side_effect = lambda _: pd.DataFrame(
          {"prediction": [3, 2, 4, 4]}
      )
  
      learners = {
          "control": control_learner,
          "A": treatment_learner,
          "B": treatment_learner,
      }
  
      result = _simulate_t_learner_treatment_effect(
          df,
          learners,
          treatments,
          control_name,
          prediction_column,
      )
  
      print(result.suggested_treatment)
  
      expected = pd.DataFrame(
          {
              "x1": [1.3, 1.0, 1.8, -0.1],
              "x2": [10, 4, 15, 6],
              "treatment": ["A", "B", "A", "control"],
              "target": [0, 0, 0, 1],
              "treatment_A__prediction_on_treatment": [3, 2, 4, 4],
              "treatment_A__uplift": [2, 0, 1, 0],
              "treatment_B__prediction_on_treatment": [3, 2, 4, 4],
              "treatment_B__uplift": [2, 0, 1, 0],
              "uplift": [2, 0, 1, 0],
              "suggested_treatment": [
                  "treatment_A",
                  "control",
                  "treatment_A",
                  "control",
              ],
          }
      )
  
      assert isinstance(result, pd.DataFrame)
      assert_frame_equal(result, expected)
  
imports: from typing import Callable
  from unittest.mock import MagicMock, call, create_autospec, patch
  import numpy
  import pandas
  import pytest
  from pandas import DataFrame
  from pandas.testing import assert_frame_equal
  from fklearn.causal.cate_learning.meta_learners import TREATMENT_FEATURE, _append_treatment_feature, _create_treatment_flag, _filter_by_treatment, _fit_by_treatment, _get_learners, _get_model_fcn, _get_unique_treatments, _predict_by_treatment_flag, _simulate_t_learner_treatment_effect, _simulate_treatment_effect, causal_s_classification_learner, causal_t_classification_learner
  from fklearn.exceptions.exceptions import MissingControlError, MissingTreatmentError, MultipleTreatmentsError
  from fklearn.training.classification import logistic_classification_learner
  from fklearn.types import LearnerFnType
label_desc: Use image node_id 15 for calling a global function with example usage: test_simulate_t_learner_treatment_effect() without return types