code: def causal_s_classification_learner(
      df: pd.DataFrame,
      treatment_col: str,
      control_name: str,
      prediction_column: str,
      learner: Callable,
      learner_transformers: List[LearnerFnType] = None,
  ) -> LearnerReturnType:
      """
      Fits a Causal S-Learner classifier. The S-learner is a meta-learner which
      learns the Conditional Average Treatment Effect (CATE) through the creation
      of an auxiliary binary feature T that indicates if the samples is in the
      treatment (T = 1) or in the control (T = 0) group. Then, this feature can
      then be used to perform inference by artificially simulating the conversion
      of a new sample for both scenarios, i.e., with T = 0 and T = 1. The CATE τ
      is defined as τ(xi) = M(X=xi, T=1) - M(X=xi, T=0), being M a Machine Learning
      Model.
  
      **References:**
  
      [1] https://matheusfacure.github.io/python-causality-handbook/21-Meta-Learners.html
  
      [2] https://causalml.readthedocs.io/en/latest/methodology.html
  
      Parameters
      ----------
      df : pd.DataFrame
          A Pandas' DataFrame with features and target columns.
          The model will be trained to predict the target column
          from the features.
      treatment_col: str
          The name of the column in `df` which contains the names of
          the treatments or control to which each data sample was subjected.
      control_name: str
          The name of the control group.
      prediction_column : str
          The name of the column with the predictions from the provided learner.
      learner: Callable
          A fklearn classification learner function.
      learner_transformers: list
          A list of fklearn transformer functions to be applied after the learner and before estimating the CATE.
          This parameter may be useful, for example, to estimate the CATE with calibrated classifiers.
      """
  
      learner = copy.deepcopy(learner)
      features = _get_learner_features(learner)
      features_with_treatment = _append_treatment_feature(
          features, TREATMENT_FEATURE
      )
      unique_treatments = _get_unique_treatments(
          df, treatment_col, control_name
      )
  
      if learner_transformers is not None:
          learner_transformers = copy.deepcopy(learner_transformers)
          learner_pipe = build_pipeline(
              *[
                  learner(features=features_with_treatment),
              ]
              + learner_transformers
          )
      else:
          learner_pipe = learner(features=features_with_treatment)
  
      fitted_learners, learners_logs = _fit_by_treatment(
          df=df,
          learner=learner_pipe,
          treatment_col=treatment_col,
          control_name=control_name,
          treatments=unique_treatments,
      )
  
      def p(new_df: pd.DataFrame) -> pd.DataFrame:
          scored_df = _simulate_treatment_effect(
              df=new_df,
              treatments=unique_treatments,
              learners=fitted_learners,
              control_name=control_name,
              prediction_column=prediction_column,
          )
          return scored_df
  
      p.__doc__ = learner_pred_fn_docstring(
          "causal_s_classification_learner"
      )
      log = {
          "causal_s_classification_learner": {
              **learners_logs,
              "causal_features": features_with_treatment,
          }
      }
  
      return p, p(df), log
  
imports: import copy
  import inspect
  from typing import Callable, Dict, List, Tuple
  import numpy
  import pandas
  from toolz import curry
  from fklearn.common_docstrings import learner_pred_fn_docstring, learner_return_docstring
  from fklearn.exceptions.exceptions import MissingControlError, MissingTreatmentError, MultipleTreatmentsError
  from fklearn.training.pipeline import build_pipeline
  from fklearn.types import LearnerFnType, LearnerReturnType, PredictFnType
label_desc: Use image node_id 9 for calling a global function with example usage: causal_s_classification_learner(df, treatment_col, control_name, prediction_column, learner, learner_transformers) and returns: p,  p,  log, scored_df