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:

But if we use the same model for another *path operation*, we could be sending our user's passwords to every client.

!!! danger
    Never store the plain password of a user or send it in a response like this, unless you know all the caveats and you know what you are doing.

## Add an output model

It's relatively tightly coupled with relational databases (like MySQL or PostgreSQL), so, having a NoSQL database (like Couchbase, MongoDB, Cassandra, etc) as the main store engine is not very easy.

* 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；

The fast food store has 8 processors (cashiers/cooks). While the concurrent burgers store might have had only 2 (one cashier and one cook).

But still, the final experience is not the best. 😞

---

This would be the parallel equivalent story for burgers. 🍔

For a more "real life" example of this, imagine a bank.

```Python
commons: Annotated[dict, Depends(common_parameters)]
```

But because we are using `Annotated`, we can store that `Annotated` value in a variable and use it in multiple places:

=== "Python 3.10+"

    ```Python hl_lines="12  16  21"
    {!> ../../../docs_src/dependencies/tutorial001_02_an_py310.py!}
    ```

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. 🤓

        bool,
        Doc(
            """
            Exclude from the output any fields that start with the name `_sa`.

            This is mainly a hack for compatibility with SQLAlchemy objects, they
            store internal SQLAlchemy-specific state in attributes named with `_sa`,
            and those objects can't (and shouldn't be) serialized to JSON.
            """
        ),
    ] = True,
) -> Any:
    """

```

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"