code: def transform(self, X):
          
          if self.with_centering:
              check_is_fitted(self, "center_")
          if self.with_scaling:
              check_is_fitted(self, "scale_")
          X = self._check_array(X, self.copy)
  
          # if sparse.issparse(X):
          #     if self.with_scaling:
          #         inplace_column_scale(X, 1.0 / self.scale_)
          # else:
          if self.with_centering:
              X -= self.center_
          if self.with_scaling:
              X /= self.scale_
          return X
docs: Center and scale the data.
  
          Can be called on sparse input, provided that ``RobustScaler`` has been
          fitted to dense input and ``with_centering=False``.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}
              The data used to scale along the specified axis.
  
          This implementation was copied and modified from Scikit-Learn.
  
          See License information here:
          https://github.com/scikit-learn/scikit-learn/blob/master/README.rst