def test_sync_return_dict_without_response_model(benchmark, client: TestClient) -> None:
    status_code, body = _bench_get(benchmark, client, "/sync/dict-no-response-model")
    assert status_code == 200
    assert body == b'{"name":"foo","value":123}'


def test_sync_return_dict_with_response_model(benchmark, client: TestClient) -> None:
    status_code, body = _bench_get(benchmark, client, "/sync/dict-with-response-model")

# CPU WHEEL
test:linux_cpu_wheel_test_filters --test_tag_filters=-no_oss,-tf_tosa,-oss_excluded,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-no_oss_py38,-no_oss_py39,-no_oss_py310,-no_oss_py313
test:linux_cpu_wheel_test_filters --build_tag_filters=-no_oss,-tf_tosa,-oss_excluded,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-no_oss_py38,-no_oss_py39,-no_oss_py310,-no_oss_py313

* path operation: операция пути (optionally clarify as `обработчик пути`)
* path operation function: функция-обработчик пути (or `функция обработки пути`)
* proprietary: проприетарный
* benchmark: бенчмарк (add (`тест производительности`) if clarification is needed or use just `тест производительности`)
* ASGI server: ASGI-сервер
* In a hurry? : Нет времени?
* response status code: статус-код ответа

//tensorflow/core/tfrt
//tensorflow/python/kernel_tests/signal
//tensorflow/examples
//tensorflow/tools/android
//tensorflow/python/eager/benchmarks
EOF

  # grep patterns for files and targets which don't need to be in the pip
  # package, ever.
  cat > $BATS_TEST_TMPDIR/ignore_these_deps <<EOF
benchmark
_test$
_test.py$
_test_cpu$
_test_cpu.py$
_test_gpu$
_test_gpu.py$
_test_lib$

	return obj
}

// BenchmarkListObjects - Run ListObject Repeatedly and benchmark.
func BenchmarkListObjects(b *testing.B) {
	// Make a temporary directory to use as the obj.
	directory := b.TempDir()

	// Create the obj.
	obj := initFSObjectsB(directory, b)

	bucket := "ls-benchmark-bucket"
	// Create a bucket.
	err := obj.MakeBucket(b.Context(), bucket, MakeBucketOptions{})

# Benchmarks { #benchmarks }

Los benchmarks independientes de TechEmpower muestran aplicaciones de **FastAPI** ejecutándose bajo Uvicorn como <a href="https://www.techempower.com/benchmarks/#section=test&runid=7464e520-0dc2-473d-bd34-dbdfd7e85911&hw=ph&test=query&l=zijzen-7" class="external-link" target="_blank">uno de los frameworks de Python más rápidos disponibles</a>, solo por debajo de Starlette y Uvicorn en sí mismos (utilizados internamente por FastAPI).

# Benchmarks { #benchmarks }

Benchmarks independentes da TechEmpower mostram as aplicações **FastAPI** rodando com Uvicorn como <a href="https://www.techempower.com/benchmarks/#section=test&runid=7464e520-0dc2-473d-bd34-dbdfd7e85911&hw=ph&test=query&l=zijzen-7" class="external-link" target="_blank">um dos frameworks Python mais rápidos disponíveis</a>, somente atrás dos próprios Starlette e Uvicorn (utilizados internamente pelo FastAPI).

# Бенчмарки (тесты производительности) { #benchmarks }

Независимые бенчмарки TechEmpower показывают, что приложения **FastAPI** под управлением Uvicorn — <a href="https://www.techempower.com/benchmarks/#section=test&runid=7464e520-0dc2-473d-bd34-dbdfd7e85911&hw=ph&test=query&l=zijzen-7" class="external-link" target="_blank">одни из самых быстрых Python‑фреймворков</a>, уступающие только Starlette и самому Uvicorn (используются внутри FastAPI).

*   Python 3.3+ support via changes to python codebase and ability to specify
    python version via ./configure.

*   Some improvements to GPU performance and memory usage:
    [convnet benchmarks](https://github.com/soumith/convnet-benchmarks/issues/66)
    roughly equivalent with native cudnn v2 performance. Improvements mostly due
    to moving to 32-bit indices, faster shuffling kernels. More improvements to
    come in later releases.

### Migration