from collections.abc import AsyncIterable, Iterable

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None


items = [
    Item(name="Plumbus", description="A multi-purpose household device."),
    Item(name="Portal Gun", description="A portal opening device."),
    Item(name="Meeseeks Box", description="A box that summons a Meeseeks."),
]

from collections.abc import Callable
from typing import Annotated, Any

from annotated_doc import Doc
from starlette.background import BackgroundTasks as StarletteBackgroundTasks
from typing_extensions import ParamSpec

P = ParamSpec("P")


class BackgroundTasks(StarletteBackgroundTasks):
    """
    A collection of background tasks that will be called after a response has been
    sent to the client.

    Read more about it in the

    /** Buffer size used for copying. */
    protected static final int DEFAULT_BUF_SIZE = 4096;

    // ////////////////////////////////////////////////////////////////
    // from InputStream to OutputStream
    //
    /**
     * Copies from an input stream to an output stream.
     * <p>
     * Neither the input stream nor the output stream is closed.
     * </p>
     *

        }
    }

    /**
     * Reads text from the given {@link Reader}.
     * <p>
     * The {@link Reader} is not closed by this method.
     * </p>
     *
     * @param reader
     *            the character input stream to read from (must not be {@literal null})
     * @return the text read from the reader
     */
    public static String readText(final Reader reader) {

     * @return a stream of substrings resulting from splitting the input string, or an empty stream if the input is {@code null}
     */
    public static StreamOf<String> split(final String value, final String regex) {
        return stream(value == null ? null : value.split(regex));
    }

    /**
     * Creates a stream of map entries from the given map.
     *
     * @param <K> the type of keys in the map

## Description from docstring { #description-from-docstring }

As descriptions tend to be long and cover multiple lines, you can declare the *path operation* description in the function <dfn title="a multi-line string as the first expression inside a function (not assigned to any variable) used for documentation">docstring</dfn> and **FastAPI** will read it from there.

 * callable block} that may capture objects for later closing. To start a pipeline from a {@link
 * ListenableFuture} that doesn't create resources that should be closed later, you can use {@link
 * #from(ListenableFuture)} instead.
 *
 * <h3>Derived steps</h3>
 *
 * A {@code ClosingFuture} step can be derived from one or more input {@code ClosingFuture} steps in

labels: 'comp:lite'

---

**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installed from (source or binary):
- TensorFlow version (or github SHA if from source):


**Provide the text output from tflite_convert**

```
# Copy and paste here
```

**Standalone code to reproduce the issue** 

### Create an `Enum` class { #create-an-enum-class }

Import `Enum` and create a sub-class that inherits from `str` and from `Enum`.

By inheriting from `str` the API docs will be able to know that the values must be of type `string` and will be able to render correctly.

Then create class attributes with fixed values, which will be the available valid values:

                missingFiles.add("- The file '" + filename + "' from " + jsonFile.getName() + " was not found in the directory.");
            }
        }

        return missingFiles;
    }

    /**
     * Finds and collects all anchors from all .adoc files in a directory.
     */