code: def model(self, v=None):
          "Returns the model of node v"
          if v is None:
              v = self.estopping
          hist = self.hist
          trace = self.trace(v)
          ins = None
          if self._base._probability_calibration is not None:
              node = hist[-1]
              node.normalize()
              X = np.array([x.full_array() for x in node.hy]).T
              y = np.array(self._base._y_klass.full_array())
              mask = np.ones(X.shape[0], dtype=np.bool)
              mask[np.array(self._base._mask_ts.index)] = False
              ins = self._base._probability_calibration().fit(X[mask], y[mask])
          if self._classifier:
              nclasses = self._labels.shape[0]
          else:
              nclasses = None
          m = Model(trace, hist, nvar=self._base._nvar,
                    classifier=self._classifier, labels=self._labels,
                    probability_calibration=ins, nclasses=nclasses)
          return f model(self, v=None):
          "Returns the model of node v"
          if v is None:
              v = self.estopping
          hist = self.hist
          trace = self.trace(v)
          ins = None
          if self._base._probability_calibration is not None:
              node = hist[-1]
              node.normalize()
              X = np.array([x.full_array() for x in node.hy]).T
              y = np.array(self._base._y_klass.full_array())
              mask = np.ones(X.shape[0], dtype=np.bool)
              mask[np.array(self._base._mask_ts.index)] = False
              ins = self._base._probability_calibration().fit(X[mask], y[mask])
          if self._classifier:
              nclasses = self._labels.shape[0]
          else:
              nclasses = None
          m = Model(trace, hist, nvar=self._base._nvar,
                    classifier=self._classifier, labels=self._labels,
                    probability_calibration=ins, nclasses=nclasses)
          return m
docs: Returns the model of node v