code: def causal_t_classification_learner(
      df: pd.DataFrame,
      treatment_col: str,
      control_name: str,
      prediction_column: str,
      learner: LearnerFnType,
      treatment_learner: LearnerFnType = None,
      learner_transformers: List[LearnerFnType] = None,
  ) -> LearnerReturnType:
      """
      Fits a Causal T-Learner classifier. The T-Learner is a meta-learner which learns the
      Conditional Average Treatment Effect (CATE) through the use of one Machine Learning
      model for each treatment and for the control group. Each model is fitted in a subset of
      the data, according to the treatment: the CATE $\tau$ is defined as
      $\tau(x_{i}) = M_{1}(X=x_{i}, T=1) - M_{0}(X=x_{i}, T=0)$, being $M_{1}$ a model fitted
      with treatment data and $M_{0}$ a model fitted with control data. Notice that $M_{0}$
      and $M_{1}$ are traditional Machine Learning models such as a LightGBM Classifier and
      that $x_{i}$ is the feature set of sample $i$.
  
      **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 and 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: LearnerFnType
          A fklearn classification learner function.
      treatment_learner: LearnerFnType
          An optional fklearn classification learner function.
      learner_transformers: List[LearnerFnType]
          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.
      """
  
      control_learner = copy.deepcopy(learner)
  
      if treatment_learner is None:
          treatment_learner = copy.deepcopy(learner)
  
      # pipeline
      if learner_transformers is not None:
          learner_transformers = copy.deepcopy(learner_transformers)
          control_learner_pipe = build_pipeline(
              *[control_learner] + learner_transformers
          )
  
          treatment_learner_pipe = build_pipeline(
              *[treatment_learner] + learner_transformers
          )
      else:
          control_learner_pipe = copy.deepcopy(control_learner)
          treatment_learner_pipe = copy.deepcopy(treatment_learner)
  
      # learners
      unique_treatments = _get_unique_treatments(
          df, treatment_col, control_name
      )
  
      learners, learners_logs = _get_learners(
          df=df,
          control_learner=control_learner_pipe,
          treatment_learner=treatment_learner_pipe,
          unique_treatments=unique_treatments,
          control_name=control_name,
          treatment_col=treatment_col,
      )
  
      def p(new_df: pd.DataFrame) -> pd.DataFrame:
          return _simulate_t_learner_treatment_effect(
              new_df,
              learners,
              unique_treatments,
              control_name,
              prediction_column,
          )
  
      p.__doc__ = learner_pred_fn_docstring(
          "causal_t_classification_learner"
      )
  
      log = {"causal_t_classification_learner": {**learners_logs}}
  
      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 13 for calling a global function with example usage: causal_t_classification_learner(df, treatment_col, control_name, prediction_column, learner, treatment_learner, learner_transformers) and returns: p,  p,  log, _simulate_t_learner_treatment_effect