def update(self, doc, process=None):
        if process is None:
            pk = 'id'
            id = doc.id
            doc.synced = False
            set = doc
        elif process == UPDATE_PROCESS:
            if self.db.docs.find({'sid': doc.sid}).count() == 0:
                return self.insert(doc, synced=True)
            pk = 'sid'
            id = doc.sid
            doc.synced = not doc.merged() # must be commited if was merged
            doc.merged(False)
            set = {'tid': doc.tid, 'data': doc.data, 'user': doc.user,
                   'public': doc.public, 'synced': doc.synced}
        elif process == COMMIT_PROCESS:
            pk = 'id'
            id = doc.id
            doc.synced = True
            set = {'sid': doc.sid, 'tid': doc.tid, 'synced': doc.synced}
        else:
            raise DatabaseException('Incorrect update process')

        try:
            rs = self.db.docs.update({pk: id}, {'$set': set}, safe=True)
            assert rs['n'] == 1
        except Exception as e:
            raise DatabaseException(e)
        return doc
def nonlinear_1d(module, grad_input, grad_output):
    delta_out = module.y[: int(module.y.shape[0] / 2)] - module.y[int(module.y.shape[0] / 2):]

    delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
    dup0 = [2] + [1 for i in delta_in.shape[1:]]
    # handles numerical instabilities where delta_in is very small by
    # just taking the gradient in those cases
    grads = [None for _ in grad_input]
    grads[0] = torch.where(torch.abs(delta_in.repeat(dup0)) < 1e-6, grad_input[0],
                           grad_output[0] * (delta_out / delta_in).repeat(dup0))
    return tuple(grads)