run: bash scripts/test.sh
        env:
          COVERAGE_FILE: coverage/.coverage.${{ runner.os }}-py${{ matrix.python-version }}
          CONTEXT: ${{ runner.os }}-py${{ matrix.python-version }}
      - name: Store coverage files
        uses: actions/upload-artifact@v3
        with:
          name: coverage
          path: coverage

  coverage-combine:
    needs: [test]
    runs-on: ubuntu-latest
    steps:

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

!!! danger
    Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.

    If you don't know, you will learn what a "password hash" is in the [security chapters](security/simple-oauth2.md#password-hashing){.internal-link target=_blank}.

然后再用它生成一个只含已设置（在请求中所发送）数据，且省略了默认值的 `dict`：

```Python hl_lines="34"
{!../../../docs_src/body_updates/tutorial002.py!}
```

### 使用 Pydantic 的 `update` 参数

接下来，用 `.copy()` 为已有模型创建调用 `update` 参数的副本，该参数为包含更新数据的 `dict`。

例如，`stored_item_model.copy(update=update_data)`：

```Python hl_lines="35"
{!../../../docs_src/body_updates/tutorial002.py!}
```

### 更新部分数据小结

简而言之，更新部分数据应：

* 使用 `PATCH` 而不是 `PUT` （可选，也可以用 `PUT`）；
* 提取存储的数据；

Doing that in separated functions that don't share logic or variables together is more difficult as you would need to store values in global variables or similar tricks.

Because of that, it's now recommended to instead use the `lifespan` as explained above.

## Technical Details

Just a technical detail for the curious nerds. 🤓

```

We will use this model in our *path operation function*, so, we don't include in it the `hashed_password`.

### `UserInDB` model

Now, let's create a `UserInDB` model.

This will have the data that is actually stored in the database.

We don't create it as a subclass of Pydantic's `BaseModel` but as a subclass of our own `User`, because it will have all the attributes in `User` plus a couple more:

```Python hl_lines="31-33"

        * So, the user will have to log in again at some point later.
        * And if the token is stolen, the risk is less. It is not like a permanent key that will work forever (in most of the cases).
* The frontend stores that token temporarily somewhere.
* The user clicks in the frontend to go to another section of the frontend web app.
* The frontend needs to fetch some more data from the API.

    ```

### Использование параметра `update` в Pydantic

Теперь можно создать копию существующей модели, используя `.copy()`, и передать параметр `update` с `dict`, содержащим данные для обновления.

Например, `stored_item_model.copy(update=update_data)`:

=== "Python 3.10+"

    ```Python hl_lines="33"
    {!> ../../../docs_src/body_updates/tutorial002_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="35"

    * Query parameters.
    * Cookies, etc.

Plus all the security features from Starlette (including **session cookies**).

All built as reusable tools and components that are easy to integrate with your systems, data stores, relational and NoSQL databases, etc.

### Dependency Injection

* (Optionally) use `PATCH` instead of `PUT`.
* Retrieve the stored data.
* Put that data in a Pydantic model.
* Generate a `dict` without default values from the input model (using `exclude_unset`).
    * This way you can update only the values actually set by the user, instead of overriding values already stored with default values in your model.

    Die Beispiele hier verwenden `.copy()` für die Kompatibilität mit Pydantic v1, Sie sollten jedoch stattdessen `.model_copy()` verwenden, wenn Sie Pydantic v2 verwenden können.

Wie in `stored_item_model.model_copy(update=update_data)`:

=== "Python 3.10+"

    ```Python hl_lines="33"
    {!> ../../../docs_src/body_updates/tutorial002_py310.py!}
    ```

=== "Python 3.9+"