Aber all diese Funktionalität der Verwendung von asynchronem Code mit `async` und `await` wird oft als Verwendung von „Coroutinen“ zusammengefasst. Es ist vergleichbar mit dem Hauptmerkmal von Go, den „Goroutinen“.

## Fazit { #conclusion }

Sehen wir uns den gleichen Satz von oben noch mal an:

import dataclasses
import inspect
import sys
from collections.abc import Coroutine, Mapping, Sequence
from contextlib import AsyncExitStack, contextmanager
from copy import copy, deepcopy
from dataclasses import dataclass
from typing import (
    Annotated,
    Any,
    Callable,
    ForwardRef,
    Optional,
    Union,
    cast,
)

import anyio
from fastapi import params
from fastapi._compat import (
    ModelField,

- kube-apiserver: fixed a bug that could cause a goroutine leak if the apiserver encountered an encoding error serving a watch to a websocket watcher ([#84693](https://github.com/kubernetes/kubernetes/pull/84693), [@tedyu](https://github.com/tedyu))

		n = tw
	}
	t.mu.Lock()
	defer t.mu.Unlock()

	t.objAPI = objAPI
	t.updateWorkers(n)
}

// PendingTasks returns the number of ILM transition tasks waiting for a worker
// goroutine.
func (t *transitionState) PendingTasks() int {
	return len(t.transitionCh)
}

// ActiveTasks returns the number of active (ongoing) ILM transition tasks.
func (t *transitionState) ActiveTasks() int64 {

import email.message
import functools
import inspect
import json
from collections.abc import (
    AsyncIterator,
    Awaitable,
    Collection,
    Coroutine,
    Mapping,
    Sequence,
)
from contextlib import AsyncExitStack, asynccontextmanager
from enum import Enum, IntEnum
from typing import (
    Annotated,
    Any,
    Callable,
    Optional,
    Union,
)

from annotated_doc import Doc

const csvSplitSize = 128 << 10

// startReaders will read the header if needed and spin up a parser
// and a number of workers based on GOMAXPROCS.
// If an error is returned no goroutines have been started and r.err will have been set.
func (r *Reader) startReaders(newReader func(io.Reader) *csv.Reader) error {
	if r.args.FileHeaderInfo != none {
		// Read column names
		// Get one line.

	if err != nil {
		rebalanceLogIf(ctx, fmt.Errorf("invalid workers value err: %v, defaulting to %d", err, len(pool.sets)))
		workerSize = len(pool.sets)
	}

	// Each decom worker needs one List() goroutine/worker
	// add that many extra workers.
	workerSize += len(pool.sets)

	wk, err := workers.New(workerSize)
	if err != nil {
		return err
	}

	for setIdx, set := range pool.sets {

But all this functionality of using asynchronous code with `async` and `await` is many times summarized as using "coroutines". It is comparable to the main key feature of Go, the "Goroutines".

## Conclusion { #conclusion }

Let's see the same phrase from above:

> Modern versions of Python have support for **"asynchronous code"** using something called **"coroutines"**, with **`async` and `await`** syntax.

* the `APIRouter`
* the `requirements.txt`
* the Bearer Token
* the breaking change
* the bug
* the button
* the callable
* the code
* the commit
* the context manager
* the coroutine
* the database session
* the disk
* the domain
* the engine
* the fake X
* the HTTP GET method
* the item
* the library
* the lifespan
* the lock
* the middleware

		err = mg.bucketLoader(GlobalContext, metricValues, mg.cache, mg.buckets)
	} else {
		err = mg.loader(GlobalContext, metricValues, mg.cache)
	}

	// There is no way to handle errors here, so we panic the current goroutine
	// and the Metrics API handler returns a 500 HTTP status code. This should
	// normally not happen, and usually indicates a bug.
	logger.CriticalIf(GlobalContext, errors.Wrap(err, "failed to get metrics"))