code: def partial_fit(self, X, y=None, **fit_kwargs):
          
          estimator = getattr(self, "estimator_", None)
          if estimator is None:
              estimator = sklearn.base.clone(self.estimator)
          return self._fit_for_estimator(estimator, X, y, **fit_kwargs)
docs: Fit the underlying estimator.
  
          If this estimator has not been previously fit, this is identical to
          :meth:`Incremental.fit`. If it has been previously fit,
          ``self.estimator_`` is used as the starting point.
  
          Parameters
          ----------
          X, y : array-like
          **kwargs
              Additional fit-kwargs for the underlying estimator.
  
          Returns
          -------
          self : object