class_docstr: A dataset split provides all the representations for a particular split
  (train/val/test) of a dataset.
docstr: Returns the NumPy arrays that are used to perform evaluation.
docstr_tok: ["Returns","the","NumPy","arrays","that","are","used","to","perform","evaluation","."]
code: def evaluation(self) -> EvaluationDataset:
      """
      Returns the NumPy arrays that are used to perform evaluation.
      """
      if self._split == "train":
          raise ValueError(
              "training data does not provide an evaluation dataset"
          )
  
      base = self._directory / "numpy" / self._split
      return EvaluationDataset(
          np.load(base / "future_data.npy"),
          ma.MaskedArray(
              np.load(base / "past_data.npy"),
              mask=np.load(base / "past_mask.npy"),
          ),
      )
  
imports: from __future__ import annotations
  import json
  import shutil
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import cast, Dict, Tuple, Union
  import numpy
  import pandas
  from gluonts.dataset.common import Dataset, FileDataset, MetaData
  from numpy import ma
  from tsbench.constants import DEFAULT_DATA_CATCH22_PATH, DEFAULT_DATA_PATH, DEFAULT_DATA_STATS_PATH
label_desc: Use image node_id 2 for calling the DatasetSplit obj's underlying member method code with example usage: obj.evaluation() and returns: EvaluationDataset