code: def fit(self, X, y=None):
          '''
          Specify the data to which kernel values should be computed.
  
          Parameters
          ----------
          X : list of arrays or :class:`skl_groups.features.Features`
              The bags to compute "to".
          '''
          self.features_ = as_features(X, stack=True, bare=True)
          # TODO: could precompute things like squared norms if kernel == "rbf".
          # Probably should add support to sklearn instead of hacking it here.
          return self fit(self, X, y=None):
          '''
          Specify the data to which kernel values should be computed.
  
          Parameters
          ----------
          X : list of arrays or :class:`skl_groups.features.Features`
              The bags to compute "to".
          '''
          self.features_ = as_features(X, stack=True, bare=True)
          # TODO: could precompute things like squared norms if kernel == "rbf".
          # Probably should add support to sklearn instead of hacking it here.
          return self
docs: Specify the data to which kernel values should be computed.
  
          Parameters
          ----------
          X : list of arrays or :class:`skl_groups.features.Features`
              The bags to compute "to".