code: def ones_or_zeros_model(df):
      def p(new_df: pd.DataFrame) -> pd.DataFrame:
          pred = new_df[TREATMENT_FEATURE].values
  
          col_dict = {"prediction": pred[:]}
  
          return new_df.assign(**col_dict)
  
      return p(df)
  
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 10 for calling a global function with example usage: ones_or_zeros_model(df) and returns: p, new_df