These functions are there (instead of just using the classes directly) so that your editor doesn't mark errors about their types.

That way you can use your normal editor and coding tools without having to add custom configurations to disregard those errors.

Later you will see how **powerful** it can be.

/// tip

The fact that this is **standard Python** means that you will still get the **best possible developer experience** in your editor, with the tools you use to analyze and refactor your code, etc. ✨

And also that your code will be very compatible with many other Python tools and libraries. 🚀

///

## Type hints in **FastAPI** { #type-hints-in-fastapi }

# https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/configuring-issue-templates-for-your-repository#creating-issue-forms
# https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema
name: Bugs
description: The go command, standard library, or anything else
title: "import/path: issue title"

body:
  - type: markdown
    attributes:

  /**
   * Returns the minimum of the two values. If the values compare as 0, the first is returned.
   *
   * <p>The recommended solution for finding the {@code minimum} of some values depends on the type
   * of your data and the number of elements you have. Read more in the Guava User Guide article on
   * <a href="https://github.com/google/guava/wiki/CollectionUtilitiesExplained#comparators">{@code
   * Comparators}</a>.
   *

## Use it { #use-it }

Now you can declare your dependency using this class.

{* ../../docs_src/dependencies/tutorial002_an_py310.py hl[19] *}

**FastAPI** calls the `CommonQueryParams` class. This creates an "instance" of that class and the instance will be passed as the parameter `commons` to your function.

## Type annotation vs `Depends` { #type-annotation-vs-depends }

Also, **automatically generated clients** will have simpler interfaces, so that the developers communicating with your API can have a much better time working with your API. 😎

///

All the fields in `HeroPublic` are the same as in `HeroBase`, with `id` declared as `int` (not `None`):

* `id`
* `name`
* `age`

/// tip

**GraphQL** solves some very specific use cases.

It has **advantages** and **disadvantages** when compared to common **web APIs**.

Make sure you evaluate if the **benefits** for your use case compensate the **drawbacks**. 🤓

///

## GraphQL Libraries { #graphql-libraries }

Here are some of the **GraphQL** libraries that have **ASGI** support. You could use them with **FastAPI**:

       * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).
     * If error still persists then, apparently your CPU model does not support AVX instruction sets.
       * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).
   

### Sub-application { #sub-application }

Then, create your sub-application, and its *path operations*.

This sub-application is just another standard FastAPI application, but this is the one that will be "mounted":

{* ../../docs_src/sub_applications/tutorial001_py39.py hl[11, 14:16] *}

### Mount the sub-application { #mount-the-sub-application }

In your top-level application, `app`, mount the sub-application, `subapi`.

 *
 * <p>{@code SettableFuture} is the recommended {@code ListenableFuture} implementation when your
 * task cannot be implemented with {@link ListeningExecutorService}, the various {@link Futures}
 * utility methods, or {@link ListenableFutureTask}. Those APIs have less opportunity for developer
 * error. If your needs are more complex than {@code SettableFuture} supports, use {@link