This decoupling of parts, and being a "microframework" that could be extended to cover exactly what is needed was a key feature that I wanted to keep.

Given the simplicity of Flask, it seemed like a good match for building APIs. The next thing to find was a "Django REST Framework" for Flask.

!!! check "Inspired **FastAPI** to"

<div class="termy">

```console
$ pip install "fastapi[all]"

---> 100%
```

</div>

...that also includes `uvicorn`, that you can use as the server that runs your code.

!!! note
    You can also install it part by part.

    This is what you would probably do once you want to deploy your application to production:

    ```
    pip install fastapi
    ```

#### JVM platform

This is a platform that builds on the core and software platforms to add support for developing software that runs on the JVM.
This includes software that is implemented using Java, Kotlin or some other JVM language.

This platform provides specific support for Java, Groovy and Scala, and includes the foojay toolchain plugin. 

#### Extensibility platform

  - type: textarea
    id: current-behavior
    attributes:
      label: Current Behavior
      description: Tell us what happens
    validations:
      required: true
  - type: textarea
    id: expected-behavior
    attributes:
      label: Expected Behavior
      description: Tell us what should happen
    validations:
      required: true
  - type: textarea
    id: context
    attributes:

import java.lang.annotation.Target;

/**
 * Signifies that a test should not be run under Android. This annotation is respected only by our
 * Google-internal Android suite generators. Note that those generators also suppress any test
 * annotated with LargeTest.
 *
 * <p>Why use a custom annotation instead of {@code android.test.suitebuilder.annotation.Suppress}?
 * I'm not completely sure that this is the right choice, but it has various advantages:
 *

  HttpUrl baseUrl = server.url("/v1/chat/");

  // Exercise your application code, which should make those HTTP requests.
  // Responses are returned in the same order that they are enqueued.
  Chat chat = new Chat(baseUrl);

  chat.loadMore();
  assertEquals("hello, world!", chat.messages());

  chat.loadMore();
  chat.loadMore();
  assertEquals(""
      + "hello, world!\n"
      + "sup, bra?\n"

available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.

The risk of loading untrusted checkpoints depends on the code or graph that you
are working with. When loading untrusted checkpoints, the values of the traced
variables from your model are also going to be untrusted. That means that if

#### JVM platform

This is a platform that builds on the core and software platforms to add support for developing software that runs on the JVM.
This includes software that is implemented using Java, Kotlin or some other JVM language.

This platform provides specific support for Java, Groovy and Scala, and includes the foojay toolchain plugin. 

#### Extensibility platform

  - type: textarea
    id: context
    attributes:
      label: Context
      description: |
        How has this issue affected you? What are you trying to accomplish? What other alternatives have you considered?
        Providing context helps us come up with a solution that is most useful in the real world
    validations:

```

The `@app.get("/")` tells **FastAPI** that the function right below is in charge of handling requests that go to:

* the path `/`
* using a <abbr title="an HTTP GET method"><code>get</code> operation</abbr>

!!! info "`@decorator` Info"
    That `@something` syntax in Python is called a "decorator".

    You put it on top of a function. Like a pretty decorative hat (I guess that's where the term came from).