class_docstr: A dataset that corresponds to applying a list of transformations to each
  element in the base_dataset. This only supports SimpleTransformations,
  which do the same thing at prediction and training time.
  
  Parameters
  ----------
  base_dataset
      Dataset to transform
  transformations
      List of transformations to apply
class_docstr_tok: ["A","dataset","that","corresponds","to","applying","a","list","of","transformations","to","each","element","in","the","base_dataset",".","This","only","supports","SimpleTransformations",",","which","do","the","same","thing","at","prediction","and","training","time",".","Parameters","--","--","--","--","--","base_dataset","Dataset","to","transform","transformations","List","of","transformations","to","apply"]
code: def __init__(
      self,
      base_dataset: Dataset,
      transformation: Transformation,
      is_train=True,
  ) -> None:
      self.base_dataset = base_dataset
      self.transformation = transformation
      self.is_train = is_train
  
imports: import abc
  from dataclasses import dataclass
  from typing import Callable, Iterable, Iterator, List
  from gluonts.core.component import equals, validated
  from gluonts.dataset.common import DataEntry, Dataset
  from gluonts.env import env
label_desc: Use image node_id 1 to create a new TransformedDataset object from inherited base classes: Dataset with example: obj = TransformedDataset(base_dataset, transformation, is_train)