code: def test__simulate_treatment_effect(mock_predict_by_treatment_flag):
      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],
          }
      )
  
      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": [
                  0.3,
                  0.3,
                  0.0,
                  1.0,
              ],
              "treatment_A__prediction_on_control": [
                  0.2,
                  0.5,
                  0.3,
                  0.0,
              ],
              "treatment_A__uplift": [0.1, -0.2, -0.3, 1.0],
              "treatment_B__prediction_on_treatment": [
                  0.6,
                  0.7,
                  0.0,
                  1.0,
              ],
              "treatment_B__prediction_on_control": [
                  1.0,
                  0.5,
                  1.0,
                  1.0,
              ],
              "treatment_B__uplift": [-0.4, 0.2, -1.0, 0.0],
              "uplift": [0.1, 0.2, -0.3, 1.0],
              "suggested_treatment": [
                  "treatment_A",
                  "treatment_B",
                  "control",
                  "treatment_A",
              ],
          }
      )
  
      treatments = ["A", "B"]
      control_name = "control"
  
      # This test will score the model for all treatments available and for all treatment-control pairs. In this test,
      # since we have control and treatment for treatments A and B, we expect to have 4 model outputs - two for each
      # treatment. The output of the following data will be used to calculate the uplift.
  
      mock_predict_by_treatment_flag.side_effect = [
          [0.3, 0.3, 0.0, 1.0],
          # treatment = A, apply treatment = 1
          [0.2, 0.5, 0.3, 0.0],
          # treatment = A, apply treatment = 0
          [0.6, 0.7, 0.0, 1.0],
          # treatment = B, apply treatment = 1
          [1.0, 0.5, 1.0, 1.0]
          # treatment = B, apply treatment = 0
      ]
  
      learners = {
          "A": logistic_classification_learner,
          "B": logistic_classification_learner,
      }
  
      results = _simulate_treatment_effect(
          df,
          treatments=treatments,
          control_name=control_name,
          learners=learners,
          prediction_column="prediction",
      )
  
      assert_frame_equal(results, 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 13 for calling a global function with example usage: test__simulate_treatment_effect(mock_predict_by_treatment_flag) without return types