As code duplication increments the chances of bugs, security issues, code desynchronization issues (when you update in one place but not in the others), etc.

And these models are all sharing a lot of the data and duplicating attribute names and types.

We could do better.

We can declare a `UserBase` model that serves as a base for our other models. And then we can make subclasses of that model that inherit its attributes (type declarations, validation, etc).

## Nested Models

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel

For example, we can define an `Image` model:

=== "Python 3.10+"

    ```Python hl_lines="7-9"

## Shortcut to remember the names

Let's see the previous example again:

```Python hl_lines="6"
{!../../../docs_src/response_status_code/tutorial001.py!}
```

`201` is the status code for "Created".

You will get completion in code you might even consider impossible before. As for example, the `price` key inside a JSON body (that could have been nested) that comes from a request.

No more typing the wrong key names, coming back and forth between docs, or scrolling up and down to find if you finally used `username` or `user_name`.

### Short

But don't worry, you can show it as you wish to your final users in the frontend.

And your database models can use any other names you want.

But for the login *path operation*, we need to use these names to be compatible with the spec (and be able to, for example, use the integrated API documentation system).

The most secure is the code flow, but is more complex to implement as it requires more steps. As it is more complex, many providers end up suggesting the implicit flow.

!!! note
    It's common that each authentication provider names their flows in a different way, to make it part of their brand.

    But in the end, they are implementing the same OAuth2 standard.

```

!!! tip
    Peewee creates several magic attributes.

    It will automatically add an `id` attribute as an integer to be the primary key.

    It will chose the name of the tables based on the class names.

    For the `Item`, it will create an attribute `owner_id` with the integer ID of the `User`. But we don't declare it anywhere.

## Create the Pydantic models

Now let's check the file `sql_app/schemas.py`.

```Python hl_lines="6"
{!../../../docs_src/path_operation_advanced_configuration/tutorial001.py!}
```

### Using the *path operation function* name as the operationId

If you want to use your APIs' function names as `operationId`s, you can iterate over all of them and override each *path operation's* `operation_id` using their `APIRoute.name`.

You should do it after adding all your *path operations*.

```Python hl_lines="2  12-21  24"

Most of the standard headers are separated by a "hyphen" character, also known as the "minus symbol" (`-`).

But a variable like `user-agent` is invalid in Python.

So, by default, `Header` will convert the parameter names characters from underscore (`_`) to hyphen (`-`) to extract and document the headers.

Also, HTTP headers are case-insensitive, so, you can declare them with standard Python style (also known as "snake_case").

It would depend mainly on the tool you use to **install** those requirements.

The most common way to do it is to have a file `requirements.txt` with the package names and their versions, one per line.

You would of course use the same ideas you read in [About FastAPI versions](versions.md){.internal-link target=_blank} to set the ranges of versions.