class_docstr: Augmenter adding Gaussian (i.e. white) noise to the time series.
  
  If `transform` is given time series :math:`X={x_1, x_2, ... , x_n}`, then
  returns :math:`X_t={x_1+e_1, x_2+e_2, ..., x_n+e_n}` where :math:`e_i` are
  i.i.d. random draws from a normal distribution with mean :math:`\mu` = 0
  and standard deviation :math:`\sigma` = ``scale``.
  Time series augmentation by adding Gaussian Noise has been discussed among
  others in [1] and [2].
  
  Parameters
  ----------
  scale: float, scale parameter (default=1.0)
          Specifies the standard deviation.
  random_state: None or int or ``np.random.RandomState`` instance, optional
          "If int or RandomState, use it for drawing the random variates.
          If None, rely on ``self.random_state``.
          Default is None." [3]
  
  References and Footnotes
  ----------
  
      [1]: WEN, Qingsong, et al. Time series data augmentation for deep
      learning: A survey. arXiv preprint arXiv:2002.12478, 2020.
      [2]: 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.
      [3]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.random_state.html # noqa
code: def _transform(self, X, y=None):
      from scipy.stats import norm
  
      if self.scale in self._allowed_statistics:
          scale = self.scale(X)
      elif isinstance(self.scale, (int, float)):
          scale = self.scale
      else:
          raise TypeError(
              "Type of parameter 'scale' must be a non-negative float value."
          )
      return X[0] + norm.rvs(
          0, scale, size=len(X), random_state=self.random_state
      )
  
imports: import numpy
  import pandas
  from sklearn.utils import check_random_state
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 2 for calling the WhiteNoiseAugmenter obj's underlying member method code with example usage: obj._transform(X, y) and returns: unknown