The `__tablename__` attribute tells SQLAlchemy the name of the table to use in the database for each of these models.

### Create model attributes/columns

Now create all the model (class) attributes.

Each of these attributes represents a column in its corresponding database table.

We use `Column` from SQLAlchemy as the default value.

from sqlalchemy import Boolean, Column, ForeignKey, Integer, String
from sqlalchemy.orm import relationship

from .database import Base


class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    email = Column(String, unique=True, index=True)
    hashed_password = Column(String)
    is_active = Column(Boolean, default=True)

    items = relationship("Item", back_populates="owner")

## Execution of dependencies with `yield`

The sequence of execution is more or less like this diagram. Time flows from top to bottom. And each column is one of the parts interacting or executing code.

```mermaid
sequenceDiagram

participant client as Client
participant handler as Exception handler
participant dep as Dep with yield

from sqlalchemy import Boolean, Column, ForeignKey, Integer, String
from sqlalchemy.orm import relationship

from .database import Base


class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    email = Column(String, unique=True, index=True)
    hashed_password = Column(String)
    is_active = Column(Boolean, default=True)

    items = relationship("Item", back_populates="owner")

from sqlalchemy import Boolean, Column, ForeignKey, Integer, String
from sqlalchemy.orm import relationship

from .database import Base


class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    email = Column(String, unique=True, index=True)
    hashed_password = Column(String)
    is_active = Column(Boolean, default=True)

    items = relationship("Item", back_populates="owner")

database = databases.Database(DATABASE_URL)

metadata = sqlalchemy.MetaData()

notes = sqlalchemy.Table(
    "notes",
    metadata,
    sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True),
    sqlalchemy.Column("text", sqlalchemy.String),
    sqlalchemy.Column("completed", sqlalchemy.Boolean),
)


engine = sqlalchemy.create_engine(
    DATABASE_URL, connect_args={"check_same_thread": False}
)

# Tipos de dados extras

Até agora, você tem usado tipos de dados comuns, tais como:

* `int`
* `float`
* `str`
* `bool`

Mas você também pode usar tipos de dados mais complexos.

E você ainda terá os mesmos recursos que viu até agora:

* Ótimo suporte do editor.
* Conversão de dados das requisições recebidas.
* Conversão de dados para os dados da resposta.
* Validação de dados.
* Anotação e documentação automáticas.

    {!> ../../../docs_src/sql_databases/sql_app_py310/schemas.py!}
    ```

#### 🇸🇲 👗 & Pydantic 👗

👀 👈 🇸🇲 *🏷* 🔬 🔢 ⚙️ `=`, & 🚶‍♀️ 🆎 🔢 `Column`, 💖:

```Python
name = Column(String)
```

⏪ Pydantic *🏷* 📣 🆎 ⚙️ `:`, 🆕 🆎 ✍ ❕/🆎 🔑:

```Python
name: str
```

✔️ ⚫️ 🤯, 👆 🚫 🤚 😕 🕐❔ ⚙️ `=` & `:` ⏮️ 👫.

        {
            "detail": [
                {
                    "loc": ["body", 1],
                    "msg": "Expecting property name enclosed in double quotes: line 1 column 2 (char 1)",
                    "type": "value_error.jsondecode",
                    "ctx": {
                        "msg": "Expecting property name enclosed in double quotes",

        {
            "detail": [
                {
                    "loc": ["body", 1],
                    "msg": "Expecting property name enclosed in double quotes: line 1 column 2 (char 1)",
                    "type": "value_error.jsondecode",
                    "ctx": {
                        "msg": "Expecting property name enclosed in double quotes",