code: def test_get_model_fcn(base_input_df):
      """
      Test if the fn is filtering the data
      Test if the learner is called with the filtered data
      """
  
      fake_prediction_column = [0.1, 0.2, 0.3]
      df_expected = pd.DataFrame(
          {
              "x1": [1.3, 1.8, -0.1],
              "x2": [10, 15, 6],
              "treatment": [
                  "A",
                  "A",
                  "A",
              ],
              "target": [1, 1, 0],
              "prediction": fake_prediction_column,
          }
      )
  
      def mock_learner(df):
          df["prediction"] = fake_prediction_column
  
          return (lambda x: x, df, dict())
  
      learner = MagicMock()
      learner.side_effect = mock_learner
  
      mock_fcn, mock_p_df, mock_logs = _get_model_fcn(
          base_input_df, "treatment", "A", learner
      )
  
      assert isinstance(mock_fcn, Callable)
      assert_frame_equal(mock_p_df, df_expected)
      assert isinstance(mock_logs, dict)
  
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 16 for calling a global function with example usage: test_get_model_fcn(base_input_df) and returns: function,  df,  dict