val call =
      client.newCall(
        Request(
          url = server.url("/foo"),
          body =
            object : RequestBody() {
              override fun contentType(): MediaType = "text/plain; charset=utf-8".toMediaType()

              override fun writeTo(sink: BufferedSink) {
                sink.write(postBytes)
              }
            },
        ),
      )

```

### `HTMLResponse`

Takes some text or bytes and returns an HTML response, as you read above.

### `PlainTextResponse`

Takes some text or bytes and returns an plain text response.

```Python hl_lines="2  7  9"
{!../../../docs_src/custom_response/tutorial005.py!}
```

### `JSONResponse`

Takes some data and returns an `application/json` encoded response.

instead of being called directly (as it would block the server).

If you are coming from another async framework that does not work in the way described above and you are used to defining trivial compute-only *path operation functions* with plain `def` for a tiny performance gain (about 100 nanoseconds), please note that in **FastAPI** the effect would be quite opposite. In these cases, it's better to use `async def` unless your *path operation functions* use code that performs blocking <abbr...

=== "Python 3.8+"

    ```Python hl_lines="1  20"
    {!> ../../../docs_src/extra_models/tutorial004.py!}
    ```

## Response with arbitrary `dict`

You can also declare a response using a plain arbitrary `dict`, declaring just the type of the keys and values, without using a Pydantic model.

This is useful if you don't know the valid field/attribute names (that would be needed for a Pydantic model) beforehand.

/plugins/kapt3/kapt3-compiler/tests-gen/ "Kotlin Compiler Core" "Kotlin JVM"
/plugins/kapt4/ "Kotlin JVM"
/plugins/kotlinx-serialization/ Sergey.Shanshin Leonid.Startsev
/plugins/js-plain-objects/ Artem.Kobzar
/libraries/tools/js-plain-objects Artem.Kobzar
/plugins/lombok/ "Kotlin Compiler Core"
/plugins/noarg/ "Kotlin Compiler Core"
/plugins/parcelize/ "Kotlin Compiler Core"

    subgraph Client Node
        Client
        CZ["Ztunnel"]
    end
    subgraph Server Node
        Server
        SZ["Ztunnel"]
    end
    Client--Plain-->CZ
    CZ-."HBONE (target)".->Server
    CZ--"HBONE (actual)"-->SZ
    SZ--Plain-->Server
```

### Pooling

User connections can be multiplexed over shared HBONE connections.
This is done through standard HTTP/2 pooling.

{!../../../docs_src/path_operation_advanced_configuration/tutorial006.py!}
```

In this example, we didn't declare any Pydantic model. In fact, the request body is not even <abbr title="converted from some plain format, like bytes, into Python objects">parsed</abbr> as JSON, it is read directly as `bytes`, and the function `magic_data_reader()` would be in charge of parsing it in some way.

```Python hl_lines="20-37  39-40"
{!../../../docs_src/path_operation_advanced_configuration/tutorial006.py!}
```

👉 🖼, 👥 🚫 📣 🙆 Pydantic 🏷. 👐, 📨 💪 🚫 <abbr title="converted from some plain format, like bytes, into Python objects">🎻</abbr> 🎻, ⚫️ ✍ 🔗 `bytes`, &amp; 🔢 `magic_data_reader()` 🔜 🈚 🎻 ⚫️ 🌌.

👐, 👥 💪 📣 📈 🔗 📨 💪.

### 🛃 🗄 🎚 🆎

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

So, a request with a content type of `text/plain` containing JSON data would be accepted and the JSON data would be extracted.