class_docstr: The dataset splits provide train, validation and test data for a particular dataset. Calling
  any of the functions here, is a noop. Data is only loaded once a particular representation of
  the data is accessed.
class_docstr_tok: ["The","dataset","splits","provide","train",",","validation","and","test","data","for","a","particular","dataset",".","Calling","any","of","the","functions","here",",","is","a","noop",".","Data","is","only","loaded","once","a","particular","representation","of","the","data","is","accessed","."]
docstr: Returns the test data for the dataset.
docstr_tok: ["Returns","the","test","data","for","the","dataset","."]
code: def test(self, name: str = "") -> DatasetSplit:
      """
      Returns the test data for the dataset.
      """
      return DatasetSplit(
          self._metadata,
          self._directory,
          f"test_{name}" if name else "test",
          self._dataset_name,
          self._standardize,
          self._prediction_length,
      )
  
imports: from __future__ import annotations
  import json
  from dataclasses import dataclass
  from typing import Tuple, Optional, List
  from pathlib import Path
  from gluonts.dataset.common import FileDataset, MetaData
  from gluonts.dataset.field_names import FieldName
  import torch
  import numpy
  import numpy.ma
  import shutil
  from meta.data.dataset import TimeSeries, TimeSeriesDataset
  from meta.common import tensor_to_np
label_desc: Use image node_id 3 for calling the DatasetSplits obj's underlying member method code with example usage: obj.test(name) and returns: DatasetSplit