code: def _predict_by_treatment_flag(
      df: pd.DataFrame,
      learner_fcn: PredictFnType,
      is_treatment: bool,
      prediction_column: str,
  ) -> np.ndarray:
      treatment_flag = (
          np.ones(df.shape[0])
          if is_treatment
          else np.zeros(df.shape[0])
      )
      df[TREATMENT_FEATURE] = treatment_flag
      prediction_df = learner_fcn(df)
      df.drop(columns=[TREATMENT_FEATURE], inplace=True)
  
      return prediction_df[prediction_column].values
  
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 7 for calling a global function with example usage: _predict_by_treatment_flag(df, learner_fcn, is_treatment, prediction_column) and returns: prediction_df