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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def boost(self, dtrain, grad, hess):
 """ Boost the booster for one iteration, with customized gradient statistics. Parameters dtrain : DMatrix The training DMatrix. grad : list The first order of gradient. hess : list The second order of gradient. """

        if len(grad) != len(hess):
            raise ValueError('grad / hess length mismatch: {} / {}'.format(len(grad), len(hess)))
        if not isinstance(dtrain, DMatrix):
            raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
        self._validate_features(dtrain)

        _check_call(_LIB.XGBoosterBoostOneIter(self.handle, dtrain.handle,
                                               c_array(ctypes.c_float, grad),
                                               c_array(ctypes.c_float, hess),
                                               len(grad)))