class_docstr: Single result class for consistency of return object when loading results.
class_docstr_tok: ["Single","result","class","for","consistency","of","return","object","when","loading","results","."]
code: def __init__(
      self,
      strategy_name,
      dataset_name,
      index,
      y_true,
      y_pred,
      fit_estimator_start_time,
      fit_estimator_end_time,
      predict_estimator_start_time,
      predict_estimator_end_time,
      y_proba=None,
  ):
      # check input format
      if not all(
          isinstance(array, np.ndarray) for array in [y_true, y_pred]
      ):
          raise ValueError(
              f"Prediction results have to stored as numpy arrays, "
              f"but found: {[type(array) for array in [y_true, y_pred]]}"
          )
      if not all(
          isinstance(name, str)
          for name in [strategy_name, dataset_name]
      ):
          raise ValueError(
              f"Names must be strings, but found: "
              f"{[type(name) for name in [strategy_name, dataset_name]]}"
          )
  
      self.strategy_name = strategy_name
      self.dataset_name = dataset_name
      self.index = index
      self.y_true = y_true
      self.y_pred = y_pred
      self.y_proba = y_proba
      self.fit_estimator_start_time = fit_estimator_start_time
      self.fit_estimator_end_time = fit_estimator_end_time
      self.predict_estimator_start_time = predict_estimator_start_time
      self.predict_estimator_end_time = predict_estimator_end_time
  
imports: import os
  from abc import ABC, abstractmethod
  from warnings import warn
  import numpy
  from joblib import dump, load
label_desc: Use image node_id 1 to create a new _PredictionsWrapper object  with example: obj = _PredictionsWrapper(strategy_name, dataset_name, index, y_true, y_pred, fit_estimator_start_time, fit_estimator_end_time, predict_estimator_start_time, predict_estimator_end_time, y_proba)