class_docstr: An iterable type used for wrapping test data.
  
  Elements of a ``TestData`` object are pairs ``(input, label)``, where
  ``input`` is input data for models, while ``label`` is the future
  ground truth that models are supposed to predict.
  
  Parameters
  ----------
  dataset:
      Whole dataset used for testing.
  splitter:
      A specific splitter that knows how to slices training and
      test data.
  prediction_length
      Length of the prediction interval in test data.
  windows
      Indicates how many test windows to generate for each original
      dataset entry.
  distance
      This is rather the difference between the start of each test
      window generated, for each of the original dataset entries.
  max_history
      If given, all entries in the *test*-set have a max-length of
      `max_history`. This can be used to produce smaller file-sizes.
code: def label(self) -> "LabelDataset":
      return LabelDataset(self)
  
imports: from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Generator, Optional, Tuple
  import pandas
  from gluonts.dataset import Dataset, DataEntry
  from gluonts.dataset.field_names import FieldName
label_desc: Use image node_id 4 for calling the TestData obj's underlying member method code with example usage: obj.label() and returns: LabelDataset