class_docstr: Augmenter reversing the time series.
  
  If `transform` is given a time series :math:`X={x_1, x_2, ... , x_n}`, then
  returns :math:`X_t={x_n, x_{n-1}, ..., x_2, x_1}`.
  Time series augmentation by reversing has been discussed e.g. in [1].
  
  Examples
  --------
  >>> X = pd.Series([1,2,3,4,5])
  >>> augmenter = ReverseAugmenter()
  >>> Xt = augmenter.fit_transform(X)
  >>> Xt
  0    5
  1    4
  2    3
  3    2
  4    1
  dtype: int64
  
  References and Footnotes
  ----------
  
      [1]: IWANA, Brian Kenji; UCHIDA, Seiichi. An empirical survey of data
      augmentation for time series classification with neural networks. Plos
      one, 2021, 16. Jg., Nr. 7, S. e0254841.
code: def __init__(self):
      super().__init__()
  
imports: import numpy
  import pandas
  from sklearn.utils import check_random_state
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 1 to create a new ReverseAugmenter object from inherited base classes: _AugmenterTags, BaseTransformer with example: obj = ReverseAugmenter()