{* ../../docs_src/body_nested_models/tutorial007.py hl[9,14,20,23,27] *}

/// info | 정보

`Offer`가 선택사항 `Image` 리스트를 차례로 갖는 `Item` 리스트를 어떻게 가지고 있는지 주목하세요

///

## 순수 리스트의 본문

예상되는 JSON 본문의 최상위 값이 JSON `array`(파이썬 `list`)면, Pydantic 모델에서와 마찬가지로 함수의 매개변수에서 타입을 선언할 수 있습니다:

```Python
images: List[Image]
```

이를 아래처럼:

{* ../../docs_src/body_nested_models/tutorial008.py hl[15] *}

## 어디서나 편집기 지원

om/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance) | [amd64](https://console.cloud.google.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance-amd64), [arm64](https://console.cloud.google.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance-arm64), [ppc64le](https://console.cloud.google.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance-ppc64le), [s390x](https://console.cloud.google.com/artifa...

        "rock",
        "metal",
        "bar"
    ],
    "images": [
        {
            "url": "http://example.com/baz.jpg",
            "name": "The Foo live"
        },
        {
            "url": "http://example.com/dave.jpg",
            "name": "The Baz"
        }
    ]
}
```

/// info

请注意 `images` 键现在具有一组 image 对象是如何发生的。

///

## 深度嵌套模型

你可以定义任意深度的嵌套模型：

 */
package org.codelibs.fess.thumbnail.impl;

import java.awt.Image;
import java.awt.Rectangle;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.util.Iterator;
import java.util.Map;

import javax.imageio.ImageIO;
import javax.imageio.ImageReadParam;
import javax.imageio.ImageReader;
import javax.imageio.stream.ImageInputStream;

import org.apache.logging.log4j.LogManager;

ogle.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance) | [amd64](https://console.cloud.google.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance-amd64), [arm64](https://console.cloud.google.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance-arm64), [ppc64le](https://console.cloud.google.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance-ppc64le), [s390x](https://console.cloud.google.com/...

ogle.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance) | [amd64](https://console.cloud.google.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance-amd64), [arm64](https://console.cloud.google.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance-arm64), [ppc64le](https://console.cloud.google.com/artifacts/docker/k8s-artifacts-prod/southamerica-east1/images/conformance-ppc64le), [s390x](https://console.cloud.google.com/...

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Image(BaseModel):
    url: HttpUrl
    name: str


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: set[str] = set()
    images: Union[list[Image], None] = None


@app.put("/items/{item_id}")

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Image(BaseModel):
    url: HttpUrl
    name: str


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: set[str] = set()
    images: list[Image] | None = None


class Offer(BaseModel):
    name: str
    description: str | None = None

## Qué es una Imagen de Contenedor { #what-is-a-container-image }

Un **contenedor** se ejecuta desde una **imagen de contenedor**.

[registry.k8s.io/kube-apiserver:v1.27.11](https://console.cloud.google.com/gcr/images/k8s-artifacts-prod/us/kube-apiserver) | [amd64](...