computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted
models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions

现在试着发送一个无效的 `item`，例如：

```JSON
{
  "title": "towel",
  "size": "XL"
}

```

收到的响应包含 `body` 信息，并说明数据是无效的：

```JSON hl_lines="12-15"
{
  "detail": [
    {
      "loc": [
        "body",
        "size"
      ],
      "msg": "value is not a valid integer",
      "type": "type_error.integer"
    }
  ],
  "body": {
    "title": "towel",
    "size": "XL"
  }
}

    data = [{"x": 1, "y": 2}, {"x": 3, "y": 4}]
    response = client.post("/tuple-of-models/", json=data)
    assert response.status_code == 200, response.text
    assert response.json() == data


def test_tuple_with_model_invalid():
    data = [{"x": 1, "y": 2}, {"x": 3, "y": 4}, {"x": 5, "y": 6}]
    response = client.post("/tuple-of-models/", json=data)
    assert response.status_code == 422, response.text

            </goals>
            <configuration>
              <version>1.1.0</version>
              <models>
                <model>src/main/mdo/remote-resources.mdo</model>
              </models>
            </configuration>
          </execution>
          <execution>
            <id>supplemental-models</id>
            <goals>
              <goal>xpp3-writer</goal>
              <goal>java</goal>

client = TestClient(app)


def test_get_enums_alexnet():
    response = client.get("/models/alexnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "alexnet", "message": "Deep Learning FTW!"}


def test_get_enums_lenet():
    response = client.get("/models/lenet")
    assert response.status_code == 200

            <configuration>
              <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
              <version>2.0.0</version>
              <models>
                <model>src/main/mdo/plugin.mdo</model>
              </models>
              <templates>
                <template>model.vm</template>
              </templates>
              <params>

{* ../../docs_src/first_steps/tutorial001_py39.py hl[8] *}

You can return a `dict`, `list`, singular values as `str`, `int`, etc.

You can also return Pydantic models (you'll see more about that later).

There are many other objects and models that will be automatically converted to JSON (including ORMs, etc). Try using your favorite ones, it's highly probable that they are already supported.

0F73          ; mapped                 ; 0F71 0F72     # 2.0  TIBETAN VOWEL SIGN II
0F74          ; valid                                  # 2.0  TIBETAN VOWEL SIGN U
0F75          ; mapped                 ; 0F71 0F74     # 2.0  TIBETAN VOWEL SIGN UU
0F76          ; mapped                 ; 0FB2 0F80     # 2.0  TIBETAN VOWEL SIGN VOCALIC R

    - tutorial/query-param-models.md
    - tutorial/body-multiple-params.md
    - tutorial/body-fields.md
    - tutorial/body-nested-models.md
    - tutorial/schema-extra-example.md
    - tutorial/extra-data-types.md
    - tutorial/cookie-params.md
    - tutorial/header-params.md
    - tutorial/cookie-param-models.md
    - tutorial/header-param-models.md
    - tutorial/response-model.md

///

If `bump-pydantic` doesn't work for your use case, you can use the support for both Pydantic v1 and v2 models in the same app to do the migration to Pydantic v2 gradually.

You could fist upgrade Pydantic to use the latest version 2, and change the imports to use `pydantic.v1` for all your models.