old_contents: """
  A set of abstract classes defining interfaces.
  """
  
  class AugmentMixin(object):
      """Mixin class for augmentation of data in noxer."""
  
      def fit_transform(self, X, Y, **fit_params):
          """Fit to data, then transform it.
  
          Fits transformer to X and Y with optional parameters fit_params
          and returns a transformed version of X and Y.
  
          Parameters
          ----------
          X : array of shape [n_samples, ...]
              Training set.
  
          Y : array of shape [n_samples, ...]
              Target values.
  
          Returns
          -------
          X_new : array of shape [n_samples, ...]
              Transformed array.
  
          Y_new : array of shape [n_samples, ...]
              Transformed outputs.
  
          """
          return self.fit(X, Y, **fit_params).transform(X, Y)
new_contents: """
  A set of abstract classes defining interfaces.
  """
  
  class AugmentMixin(object):
      """Mixin class for augmentation of data in noxer."""
  
      def fit(self, X, Y, **fit_params):
          """Fit to the data, base method.
  
          Fits transformer to X and Y with optional parameters fit_params
          and returns self.
  
          Parameters
          ----------
          X : array of shape [n_samples, ...]
              Training set.
  
          Y : array of shape [n_samples, ...]
              Target values.
  
          Returns
          -------
          self
  
          """
          return self
  
      def fit_transform(self, X, Y, **fit_params):
          """Fit to data, then transform it.
  
          Fits transformer to X and Y with optional parameters fit_params
          and returns a transformed version of X and Y.
  
          Parameters
          ----------
          X : array of shape [n_samples, ...]
              Training set.
  
          Y : array of shape [n_samples, ...]
              Target values.
  
          Returns
          -------
          X_new : array of shape [n_samples, ...]
              Transformed array.
  
          Y_new : array of shape [n_samples, ...]
              Transformed outputs.
  
          """
          return self.fit(X, Y, **fit_params).transform(X, Y)
subject: Extend base augment