These types that have internal types are called "**generic**" types. And it's possible to declare them, even with their internal types.

To declare those types and the internal types, you can use the standard Python module `typing`. It exists specifically to support these type hints.

#### Newer versions of Python { #newer-versions-of-python }

</div>

## Forbid Extra Form Fields { #forbid-extra-form-fields }

In some special use cases (probably not very common), you might want to **restrict** the form fields to only those declared in the Pydantic model. And **forbid** any **extra** fields.

/// note

This is supported since FastAPI version `0.114.0`. 🤓

///

You can use Pydantic's model configuration to `forbid` any `extra` fields:

work) run the object code and to modify the work, including scripts to
control those activities.  However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work.  For example, Corresponding Source

in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that
have different security properties and provide different levels of confidence
when dealing with untrusted data. Based on the security history of these

 * {@link ValueGraph}, and {@link Network}):
 *
 * {@snippet :
 * someGraphAlgorithm(startNode, graph);
 * }
 *
 * This works because those types each implement {@code PredecessorsFunction}. It will also work
 * with any other implementation of this interface.
 *
 * <p>If you have your own graph implementation based around a custom node type {@code MyNode},

This is very useful when you need to:

* Have shared logic (the same code logic again and again).
* Share database connections.
* Enforce security, authentication, role requirements, etc.
* And many other things...

All these, while minimizing code repetition.

## First Steps { #first-steps }

 * {@link ValueGraph}, and {@link Network}):
 *
 * {@snippet :
 * someGraphAlgorithm(startNode, graph);
 * }
 *
 * This works because those types each implement {@code PredecessorsFunction}. It will also work
 * with any other implementation of this interface.
 *
 * <p>If you have your own graph implementation based around a custom node type {@code MyNode},

 * {@link ValueGraph}, and {@link Network}):
 *
 * {@snippet :
 * someGraphAlgorithm(startNode, graph);
 * }
 *
 * This works because those types each implement {@code SuccessorsFunction}. It will also work with
 * any other implementation of this interface.
 *
 * <p>If you have your own graph implementation based around a custom node type {@code MyNode},

 * file system (those projects are usually about to be built) or from the local repository
 * (they are usually downloaded during dependency collection). Those projects are loaded
 * from a Project Object Model (POM).</p>
 *
 * <p><dfn>Project Object Model</dfn> or <dfn>POM</dfn> refers to the information describing
 * all the information needed to build or consume a project. Those are usually loaded from

If you added the return type annotation, tools and editors would complain with a (correct) error telling you that your function is returning a type (e.g. a dict) that is different from what you declared (e.g. a Pydantic model).

In those cases, you can use the *path operation decorator* parameter `response_model` instead of the return type.

You can use the `response_model` parameter in any of the *path operations*:

* `@app.get()`
* `@app.post()`