code: def dummy_results():
      """Results that are dummy."""
      results = RAMResults()
      results.cv = PresplitFilesCV()
      results.save_predictions(
          strategy_name="alg1",
          dataset_name="dataset1",
          index=np.array([1, 2, 3, 4]),
          y_true=np.array([1, 1, 1, 1]),
          y_pred=np.array([1, 1, 1, 1]),
          y_proba=None,
          cv_fold=0,
          train_or_test="test",
          fit_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          fit_estimator_end_time=pd.to_datetime(1605268801, unit="ms"),
          predict_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          predict_estimator_end_time=pd.to_datetime(
              1605268801, unit="ms"
          ),
      )
      results.save_predictions(
          strategy_name="alg1",
          dataset_name="dataset2",
          index=np.array([1, 2, 3, 4]),
          y_true=np.array([0, 0, 0, 0]),
          y_pred=np.array([0, 0, 0, 0]),
          y_proba=None,
          cv_fold=0,
          train_or_test="test",
          fit_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          fit_estimator_end_time=pd.to_datetime(1605268801, unit="ms"),
          predict_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          predict_estimator_end_time=pd.to_datetime(
              1605268801, unit="ms"
          ),
      )
  
      results.save_predictions(
          strategy_name="alg2",
          dataset_name="dataset1",
          index=np.array([1, 2, 3, 4]),
          y_true=np.array([1, 1, 1, 1]),
          y_pred=np.array([0, 0, 0, 0]),
          y_proba=None,
          cv_fold=0,
          train_or_test="test",
          fit_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          fit_estimator_end_time=pd.to_datetime(1605268801, unit="ms"),
          predict_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          predict_estimator_end_time=pd.to_datetime(
              1605268801, unit="ms"
          ),
      )
      results.save_predictions(
          strategy_name="alg2",
          dataset_name="dataset2",
          index=np.array([1, 2, 3, 4]),
          y_true=np.array([0, 0, 0, 0]),
          y_pred=np.array([1, 1, 1, 1]),
          y_proba=None,
          cv_fold=0,
          train_or_test="test",
          fit_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          fit_estimator_end_time=pd.to_datetime(1605268801, unit="ms"),
          predict_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          predict_estimator_end_time=pd.to_datetime(
              1605268801, unit="ms"
          ),
      )
  
      results.save_predictions(
          strategy_name="alg3",
          dataset_name="dataset1",
          index=np.array([1, 2, 3, 4]),
          y_true=np.array([1, 1, 1, 1]),
          y_pred=np.array([1, 1, 0, 1]),
          y_proba=None,
          cv_fold=0,
          train_or_test="test",
          fit_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          fit_estimator_end_time=pd.to_datetime(1605268801, unit="ms"),
          predict_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          predict_estimator_end_time=pd.to_datetime(
              1605268801, unit="ms"
          ),
      )
      results.save_predictions(
          strategy_name="alg3",
          dataset_name="dataset2",
          index=np.array([1, 2, 3, 4]),
          y_true=np.array([0, 0, 0, 0]),
          y_pred=np.array([0, 0, 1, 0]),
          y_proba=None,
          cv_fold=0,
          train_or_test="test",
          fit_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          fit_estimator_end_time=pd.to_datetime(1605268801, unit="ms"),
          predict_estimator_start_time=pd.to_datetime(
              1605268800, unit="ms"
          ),
          predict_estimator_end_time=pd.to_datetime(
              1605268801, unit="ms"
          ),
      )
      return results
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.metrics import accuracy_score
  from sktime.benchmarking.evaluation import Evaluator
  from sktime.benchmarking.metrics import PairwiseMetric
  from sktime.benchmarking.results import RAMResults
  from sktime.series_as_features.model_selection import PresplitFilesCV
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 1 for calling a global function with example usage: dummy_results() and returns: results