def robotsrulesetOfDomain(self, domain):
		'''检查domain是否在数据库中,
			否 --> (False, None)
			是 --> (True, 数据库中存储的robots.txt内容)
		'''
		exist = False
		cur = self.conn.cursor()
		# 是否存在
		cur.execute("SELECT 1 FROM Domains WHERE domain=?", (domain,))
		if cur.fetchone() :
			exist = True
		# 存在的话,结果是什么
		cur.execute("SELECT rules FROM Domains,Rulesets "
			"WHERE domain=? AND Domains.did=Rulesets.did"
			,(domain,) )
		ruleset = cur.fetchone()
		return (exist, ruleset)
def add_interim_values(module, input, output):
    """The forward hook used to save interim tensors, detached
    from the graph. Used to calculate the multipliers
    """
    try:
        del module.x
    except AttributeError:
        pass
    try:
        del module.y
    except AttributeError:
        pass
    module_type = module.__class__.__name__
    if module_type in op_handler:
        func_name = op_handler[module_type].__name__
        # First, check for cases where we don't need to save the x and y tensors
        if func_name == 'passthrough':
            pass
        else:
            # check only the 0th input varies
            for i in range(len(input)):
                if i != 0 and type(output) is tuple:
                    assert input[i] == output[i], "Only the 0th input may vary!"
            # if a new method is added, it must be added here too. This ensures tensors
            # are only saved if necessary
            if func_name in ['maxpool', 'nonlinear_1d']:
                # only save tensors if necessary
                if type(input) is tuple:
                    setattr(module, 'x', torch.nn.Parameter(input[0].detach()))
                else:
                    setattr(module, 'x', torch.nn.Parameter(input.detach()))
                if type(output) is tuple:
                    setattr(module, 'y', torch.nn.Parameter(output[0].detach()))
                else:
                    setattr(module, 'y', torch.nn.Parameter(output.detach()))
            if module_type in failure_case_modules:
                input[0].register_hook(deeplift_tensor_grad)