```Python hl_lines="2  15  26"
{!../../docs_src/sql_databases/sql_app/models.py!}
```

🕐❔ 🔐 🔢 `items` `User`, `my_user.items`, ⚫️ 🔜 ✔️ 📇 `Item` 🇸🇲 🏷 (⚪️➡️ `items` 🏓) 👈 ✔️ 💱 🔑 ☝ 👉 ⏺ `users` 🏓.

🕐❔ 👆 🔐 `my_user.items`, 🇸🇲 🔜 🤙 🚶 & ☕ 🏬 ⚪️➡️ 💽 `items` 🏓 & 🔗 👫 📥.

	defer store.unlock()

	// Load existing policy mapping
	var mp MappedPolicy
	if !isGroup {
		if userType == stsUser {
			stsMap := xsync.NewMapOf[string, MappedPolicy]()

			// Attempt to load parent user mapping for STS accounts
			store.loadMappedPolicy(context.TODO(), name, stsUser, false, stsMap)

			mp, _ = stsMap.Load(name)
		} else {
			mp, _ = cache.iamUserPolicyMap.Load(name)
		}

这将或多或少会成为一种“神奇”属性，其中表示该表与其他相关的表中的值。

```Python hl_lines="2  15  26"
{!../../docs_src/sql_databases/sql_app/models.py!}
```

当访问 user 中的属性`items`时，如 中`my_user.items`，它将有一个`Item`SQLAlchemy 模型列表（来自`items`表），这些模型具有指向`users`表中此记录的外键。

当您访问`my_user.items`时，SQLAlchemy 实际上会从`items`表中的获取一批记录并在此处填充进去。

同样，当访问 Item中的属性`owner`时，它将包含表中的`User`SQLAlchemy 模型`users`。使用`owner_id`属性/列及其外键来了解要从`users`表中获取哪条记录。

## 创建 Pydantic 模型

	var basePrefix string
	switch userType {
	case svcUser:
		basePrefix = iamConfigServiceAccountsPrefix
	case stsUser:
		basePrefix = iamConfigSTSPrefix
	default:
		basePrefix = iamConfigUsersPrefix
	}

	ctx, cancel := context.WithCancel(ctx)
	defer cancel()

		var oldDN string
		if isGroup {
			oldDN = r.Group
		} else {
			oldDN = r.User
		}
		if oldDN != dn {
			sys.store.PolicyDBUpdate(ctx, oldDN, isGroup, stsUser, r.Policies, isAttach)
		}
	}

	userType := stsUser
	updatedAt, addedOrRemoved, effectivePolicies, err = sys.store.PolicyDBUpdate(
		ctx, dn, isGroup, userType, r.Policies, isAttach)
	if err != nil {
		return
	}

func (ies *IAMEtcdStore) loadUsers(ctx context.Context, userType IAMUserType, m map[string]UserIdentity) error {
	var basePrefix string
	switch userType {
	case svcUser:
		basePrefix = iamConfigServiceAccountsPrefix
	case stsUser:
		basePrefix = iamConfigSTSPrefix
	default:
		basePrefix = iamConfigUsersPrefix
	}

	cctx, cancel := context.WithTimeout(ctx, defaultContextTimeout)
	defer cancel()

def main(user_id: str):
    return user_id


# Un modèle Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date
```
Qui peuvent ensuite être utilisés comme cela:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

که سپس میتوان به این شکل از آن استفاده کرد:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

def main(user_id: str):
    return user_id


# Модель Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Это можно использовать так:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

👈 💪 ⤴️ ⚙️ 💖:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```