code: def _construct_dummy_fold_strategy(
      fold_strategy_cls,
      num_jobs,
      num_folds_parallel,
      bagged_resources=None,
      model_base_resources=None,
      model_base_minimum_resources=None,
      model_base_default_resources=None,
  ):
      if bagged_resources is None:
          bagged_resources = {}
      if model_base_resources is None:
          model_base_resources = {}
      if model_base_minimum_resources is None:
          model_base_minimum_resources = {}
      dummy_model_base = DummyModel(
          minimum_resources=model_base_minimum_resources,
          default_resources=model_base_default_resources,
          hyperparameters={"ag_args_fit": model_base_resources},
      )
      dummy_bagged_ensemble_model = DummyBaggedModel(
          dummy_model_base,
          hyperparameters={"ag_args_fit": bagged_resources},
      )
      train_data, test_data = _prepare_data()
      args = dict(
          model_base=dummy_model_base,
          model_base_kwargs=dict(),
          bagged_ensemble_model=dummy_bagged_ensemble_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,
          **bagged_resources,  # 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.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 2 for calling a global function with example usage: _construct_dummy_fold_strategy(fold_strategy_cls, num_jobs, num_folds_parallel, bagged_resources, model_base_resources, model_base_minimum_resources, model_base_default_resources) and returns: fold_strategy_cls