class_docstr: Scale samples using statistics that are robust to outliers.
  
  This Scaler removes the median and scales the data according to
  the quantile range (defaults to IQR: Interquartile Range).
  The IQR is the range between the 1st quartile (25th quantile)
  and the 3rd quartile (75th quantile).
  
  Centering and scaling happen independently on each sample by
  computing the relevant statistics on the timestamps. Median and
  interquartile range are used to transform the data.
  
  Standardization of a dataset is a common requirement for many
  machine learning estimators. Typically this is done by removing the mean
  and scaling to unit variance. However, outliers can often influence the
  sample mean / variance in a negative way. In such cases, the median and
  the interquartile range often give better results.
  
  Parameters
  ----------
  with_centering : bool (default = True)
      If True, center the data before scaling.
  
  with_scaling : bool (default = True)
      If True, scale the data to interquartile range.
  
  quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0
      Default: (25.0, 75.0) = (1st quantile, 3rd quantile) = IQR
  
  Examples
  --------
  >>> from pyts.preprocessing import RobustScaler
  >>> X = [[1, -2,  4],
  ...      [-2,  1,  1],
  ...      [2,  3, -2]]
  >>> scaler = RobustScaler()
  >>> scaler.transform(X)
  array([[ 0. , -1. ,  1. ],
         [-2. ,  0. ,  0. ],
         [ 0. ,  0.4, -1.6]])
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code: def __init__(
      self,
      with_centering=True,
      with_scaling=True,
      quantile_range=(25.0, 75.0),
  ):
      self.with_centering = with_centering
      self.with_scaling = with_scaling
      self.quantile_range = quantile_range
  
imports: from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import MaxAbsScaler
  from sklearn.preprocessing import RobustScaler
  from sklearn.utils.validation import check_array
  from ..base import UnivariateTransformerMixin
label_desc: Use image node_id 1 to create a new RobustScaler object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = RobustScaler(with_centering, with_scaling, quantile_range)