code: def _construct_dummy_fold_strategy(
      fold_strategy_cls,
      num_jobs,
      num_folds_parallel,
      resource_granted,
      model_base,
      bagged_model,
  ):
      train_data, test_data = _prepare_data()
      args = dict(
          model_base=model_base,
          model_base_kwargs=dict(),
          bagged_ensemble_model=bagged_model,
          X=train_data,
          y=test_data,
          X_pseudo=None,
          y_pseudo=None,
          sample_weight=None,
          time_start=time.time(),
          time_limit=0,
          models=[],
          oof_pred_proba=np.array([]),
          oof_pred_model_repeats=np.array([]),
          save_folds=True,
          time_limit_fold_ratio=1,
          **resource_granted,  # These will be processed at abstract model level in a real tabular predictor
      )
      if fold_strategy_cls == ParallelLocalFoldFittingStrategy:
          args["num_jobs"] = num_jobs
          args["num_folds_parallel"] = num_folds_parallel
      return fold_strategy_cls(**args)
  
imports: import time
  import numpy
  import pandas
  import pytest
  from autogluon.core.hpo.executors import CustomHpoExecutor, RayHpoExecutor
  from autogluon.core.models.ensemble.bagged_ensemble_model import BaggedEnsembleModel
  from autogluon.core.models.ensemble.fold_fitting_strategy import ParallelLocalFoldFittingStrategy, SequentialLocalFoldFittingStrategy
  from autogluon.tabular.models import AbstractModel
label_desc: Use image node_id 3 for calling a global function with example usage: _construct_dummy_fold_strategy(fold_strategy_cls, num_jobs, num_folds_parallel, resource_granted, model_base, bagged_model) and returns: fold_strategy_cls