false

-   type: input id: Cuda attributes: label: CUDA/cuDNN version description:
    placeholder: validations: required: false

-   type: input id: Gpu attributes: label: GPU model and memory description: if
    compiling from source placeholder: validations: required: false

-   type: textarea id: what-happened attributes: label: Current Behaviour?

Although any other parameter declared normally (for example, the body with a Pydantic model) would still be validated, converted, annotated, etc.

But there are specific cases where it's useful to get the `Request` object.

## Use the `Request` object directly { #use-the-request-object-directly }

    price: float = Field(..., gt=0)


app = FastAPI()


@app.post("/items/")
async def create_item(item: Item, project_id: Annotated[int, Query(...)]): ...
```

## Return Type or Response Model

When possible, include a return type. It will be used to validate, filter, document, and serialize the response.

```python
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

///

## 导入 Pydantic 的 `BaseModel` { #import-pydantics-basemodel }

从 `pydantic` 中导入 `BaseModel`：

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## 创建数据模型 { #create-your-data-model }

把数据模型声明为继承 `BaseModel` 的类。

使用 Python 标准类型声明所有属性：

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}

与声明查询参数一样，包含默认值的模型属性是可选的，否则就是必选的。把默认值设为 `None` 可使其变为可选。

	}
	return err
}

func (stmt *Statement) clone() *Statement {
	newStmt := &Statement{
		TableExpr:            stmt.TableExpr,
		Table:                stmt.Table,
		Model:                stmt.Model,
		Unscoped:             stmt.Unscoped,
		Dest:                 stmt.Dest,
		ReflectValue:         stmt.ReflectValue,
		Clauses:              map[string]clause.Clause{},

이를 통해 **여러 위치**에서 **모델을 재사용할** 수 있고 모든 매개변수에 대한 유효성 검사 및 메타데이터를 한 번에 선언할 수도 있습니다. 😎

/// note | 참고

이 기능은 FastAPI 버전 `0.115.0` 이후부터 지원됩니다. 🤓

///

## Pydantic 모델을 사용한 헤더 매개변수 { #header-parameters-with-a-pydantic-model }

**Pydantic 모델**에 필요한 **헤더 매개변수**를 선언한 다음, 해당 매개변수를 `Header`로 선언합니다:

{* ../../docs_src/header_param_models/tutorial001_an_py310.py hl[9:14,18] *}

import org.elasticsearch.gradle.internal.conventions.VersionPropertiesLoader
import org.apache.tools.ant.taskdefs.condition.Os
import org.gradle.plugins.ide.eclipse.model.AccessRule
import org.gradle.plugins.ide.eclipse.model.SourceFolder
import org.gradle.plugins.ide.eclipse.model.ProjectDependency

plugins {
  id 'java-gradle-plugin'
  id 'groovy-gradle-plugin'
  id 'groovy'
  id 'elasticsearch.build-tools'
  id 'elasticsearch.eclipse'

/**
 * <p>
 * A {@link Builder} encapsulates a strategy for building a set of Maven projects. The default strategy in Maven builds
 * the projects serially, but a {@link Builder} can employ any type of concurrency model to build the projects.
 */
@Named("singlethreaded")
@Singleton
public class SingleThreadedBuilder implements Builder {
    private final LifecycleModuleBuilder lifecycleModuleBuilder;

    @Inject

### Check the password { #check-the-password }

At this point we have the user data from our database, but we haven't checked the password.

Let's put that data in the Pydantic `UserInDB` model first.

You should never save plaintext passwords, so, we'll use the (fake) password hashing system.

If the passwords don't match, we return the same error.

#### Password hashing { #password-hashing }

                && options.model().map(v -> !v).orElse(false)
                && options.plugins().map(v -> !v).orElse(false)
                && options.modelVersion().isEmpty();

        if (noOptionsSpecified || allOptionsDisabled) {
            return true;
        }

        // Check if --model is explicitly set (and not part of "all disabled" scenario)
        if (options.model().isPresent()) {