code: def load_predictor_prediction(cost_forecast_loc_pred):
      this_directory = os.path.abspath(os.path.dirname(__file__))
  
      # validate the trained model
      if cost_forecast_loc_pred is None:
          try:
              cost_predictor = joblib.load(
                  os.path.join(
                      this_directory,
                      "saved_models",
                      "bps_prediction.joblib",
                  )
              )
          except KeyError:
              cost_predictor = joblib.load(
                  os.path.join(
                      this_directory,
                      "saved_models",
                      "bps_prediction_old.joblib",
                  )
              )
  
          return cost_predictor
  
imports: import os
  import sys
  import time
  import numbers
  import numpy
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.utils import check_array
  import joblib
  from joblib import Parallel, delayed, effective_n_jobs
  from joblib.externals.loky.backend.context import set_start_method
  from pyod.models.sklearn_base import _pprint
  from pyod.utils.utility import check_parameter
  from suod.models.parallel_processes import cost_forecast_meta
  from suod.models.parallel_processes import balanced_scheduling
  from suod.models.parallel_processes import _parallel_fit
  from suod.models.parallel_processes import _parallel_predict
  from suod.models.parallel_processes import _parallel_predict_proba
  from suod.models.parallel_processes import _parallel_decision_function
  from suod.models.parallel_processes import _partition_estimators
  from suod.models.parallel_processes import _parallel_approx_estimators
  from ..utils.utility import _unfold_parallel, build_codes, _get_sklearn_version
  import warnings
  from collections import defaultdict
  from inspect import signature
label_desc: Use image node_id 2 for calling a global function with example usage: load_predictor_prediction(cost_forecast_loc_pred) and returns: cost_predictor