from enum import Enum

from fastapi import FastAPI


class ModelName(str, Enum):
    alexnet = "alexnet"
    resnet = "resnet"
    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":

    assert response.json() == {"model_name": "lenet", "message": "LeCNN all the images"}


def test_get_enums_resnet():
    response = client.get("/models/resnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "resnet", "message": "Have some residuals"}


def test_get_enums_invalid():
    response = client.get("/models/foo")
    assert response.status_code == 422

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

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

/// info | 정보

<a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">열거형(또는 enums)</a>은 파이썬 버전 3.4 이후로 사용 가능합니다.

///

/// tip | 팁

혹시 궁금하다면, "AlexNet", "ResNet", 그리고 "LeNet"은 그저 기계 학습 <abbr title="기술적으로 정확히는 딥 러닝 모델 구조">모델</abbr>들의 이름입니다.

///

### *경로 매개변수* 선언

생성한 열거형 클래스(`ModelName`)를 사용하는 타입 어노테이션으로 *경로 매개변수*를 만듭니다:

/// info | 说明

Python 3.4 及之后版本支持<a href="https://docs.python.org/zh-cn/3/library/enum.html" class="external-link" target="_blank">枚举（即 enums）</a>。

///

/// tip | 提示

**AlexNet**、**ResNet**、**LeNet** 是机器学习<abbr title="技术上来说是深度学习模型架构">模型</abbr>。

///

### 声明*路径参数*

使用 Enum 类（`ModelName`）创建使用类型注解的*路径参数*：

{* ../../docs_src/path_params/tutorial005.py hl[16] *}

### 查看文档

    assertEquals("9.999 \u03bcs", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance(1234567);
    assertEquals("1.235 ms", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance(5000000000L);
    assertEquals("5.000 s", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance((long) (1.5 * 60 * 1000000000L));

    var_to_data_type_map_.swap(result.second);
  } else {
    reader_.reset(new TensorSliceReader(filename));
    if (!reader_->status().ok()) {
      tsl::Set_TF_Status_from_Status(status, reader_->status());
      return;
    }
    var_to_shape_map_.reset(
        new TensorSliceReader::VarToShapeMap(reader_->GetVariableToShapeMap()));
    var_to_data_type_map_.reset(new TensorSliceReader::VarToDataTypeMap(

	)

	if config.Colorful {
		infoStr = Green + "%s\n" + Reset + Green + "[info] " + Reset
		warnStr = BlueBold + "%s\n" + Reset + Magenta + "[warn] " + Reset
		errStr = Magenta + "%s\n" + Reset + Red + "[error] " + Reset
		traceStr = Green + "%s\n" + Reset + Yellow + "[%.3fms] " + BlueBold + "[rows:%v]" + Reset + " %s"
		traceWarnStr = Green + "%s " + Yellow + "%s\n" + Reset + RedBold + "[%.3fms] " + Yellow + "[rows:%v]" + Magenta + " %s" + Reset

    assertTrue(reader.markSupported());

    assertEquals(string, readFully(reader));
    assertFullyRead(reader);

    // reset and read again
    reader.reset();
    assertEquals(string, readFully(reader));
    assertFullyRead(reader);

    // reset, skip, mark, then read the rest
    reader.reset();
    assertEquals(5, reader.skip(5));
    reader.mark(Integer.MAX_VALUE);
    assertEquals(string.substring(5), readFully(reader));

        buf.reset();
        ns.encode(buf); // should not throw

        // After encoding, check how many bytes were used
        int bytesUsed = buf.getIndex();
        // Should be 2 bytes for the short value (alignment may add padding)
        assertTrue(bytesUsed >= 2, "Should use at least 2 bytes for short");

        // Reset index to read back
        buf.reset();