```Python
from typing import List, Dict
from datetime import date

from pydantic import BaseModel

# Değişkeni str olarak belirt
# ve o fonksiyon için harika bir editör desteği al
def main(user_id: str):
    return user_id


# Pydantic modeli
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Sonrasında bu şekilde kullanabilirsin

```Python

        )
    try:
        data = dict(obj)
    except Exception as e:
        errors: list[Exception] = []
        errors.append(e)
        try:
            data = vars(obj)
        except Exception as e:
            errors.append(e)
            raise ValueError(errors) from e
    return jsonable_encoder(
        data,
        include=include,
        exclude=exclude,

e.g.:
To download `2021/taxes.csv` archived in `financial.zip` and stored under a bucket named `company-data`, you can issue a GET request using the following path 'company-data/financial.zip/2021/taxes.csv`

## Contents properties

        return eventSpyDispatcher;
    }

    @Override
    public Map<String, Object> getData() {
        if (data == null) {
            data = new HashMap<>();
        }

        return data;
    }

{* ../../docs_src/request_form_models/tutorial001_an_py39.py hl[9:11,15] *}

**FastAPI** **extraerá** los datos de **cada campo** de los **form data** en el request y te dará el modelo de Pydantic que definiste.

## Revisa la Documentación { #check-the-docs }

Puedes verificarlo en la interfaz de documentación en `/docs`:

<div class="screenshot">

	}

	region := globalSite.Region()
	date := UTCNow()
	scope := getScope(date, region)
	credential := fmt.Sprintf("%s/%s", accessKeyID, scope)

	// Set URL query.
	query := req.URL.Query()
	query.Set("X-Amz-Algorithm", signV4Algorithm)
	query.Set("X-Amz-Date", date.Format(iso8601Format))
	query.Set("X-Amz-Expires", strconv.FormatInt(expires, 10))

	return strings.HasPrefix(r.URL.Path, kmsPathPrefix)
}

// Supported Amz date headers.
var amzDateHeaders = []string{
	// Do not change this order, x-amz-date value should be
	// validated first.
	"x-amz-date",
	"date",
}

// parseAmzDateHeader - parses supported amz date headers, in
// supported amz date formats.
func parseAmzDateHeader(req *http.Request) (time.Time, APIErrorCode) {

    {
        setLastUpdatedTimestamp( new java.util.Date() );
    }

    public void setLastUpdatedTimestamp( java.util.Date date )
    {
        java.util.TimeZone timezone = java.util.TimeZone.getTimeZone( "UTC" );
        java.text.DateFormat fmt = new java.text.SimpleDateFormat( "yyyyMMddHHmmss" );
        fmt.setTimeZone( timezone );
        setLastUpdated( fmt.format( date ) );
    }
          ]]></code>

when dealing with untrusted data. Based on the security history of these
libraries we consider that it is safe to work with untrusted inputs for PNG,
BMP, GIF, WAV, RAW, RAW\_PADDED, CSV and PROTO formats. All other input formats,
including tensorflow-io should be sandboxed if used to process untrusted data.

For example, if an attacker were to upload a malicious video file, they could

Lambda

MinIO's Object Lambda implementation allows for transforming your data to serve unique data format requirements for each application. For example, a dataset created by an ecommerce application might include personally identifiable information (PII). When the same data is processed for analytics, PII should be redacted. However, if the same dataset is used for a marketing campaign, you might need to enrich the data with additional details, such as information from the customer loyalty database....