if db5.Error != nil {
		t.Errorf("Should not raise error for join where identical fields in different tables. Error: %s", db5.Error.Error())
	}

 * limitations under the License.
 */
package gradlebuild.docs.dsl.source

import com.github.javaparser.JavaParser
import gradlebuild.docs.DocGenerationException
import gradlebuild.docs.model.ClassMetaDataRepository
import gradlebuild.docs.model.SimpleClassMetaDataRepository
import groovy.time.TimeCategory
import groovy.time.TimeDuration
import org.gradle.api.Action
import org.gradle.api.Transformer
import org.gradle.api.file.FileTree

    # For coverage
    class Model(BaseModel):
        foo: Union[str, Dict[str, Any]]

    fields = get_model_fields(Model)
    assert not is_scalar_field(fields[0])


def test_get_model_fields_cached():
    class Model(BaseModel):
        foo: str

    non_cached_fields = get_model_fields(Model)
    non_cached_fields2 = get_model_fields(Model)
    cached_fields = get_cached_model_fields(Model)

{* ../../docs_src/first_steps/tutorial001.py hl[8] *}

Możesz zwrócić `dict`, `list`, pojedynczą wartość jako `str`, `int`, itp.

Możesz również zwrócić modele Pydantic (więcej o tym później).

Istnieje wiele innych obiektów i modeli, które zostaną automatycznie skonwertowane do formatu JSON (w tym ORM itp.). Spróbuj użyć swoich ulubionych, jest bardzo prawdopodobne, że są już obsługiwane.

## Podsumowanie

* Automatyczna dokumentacja modelu danych za pomocą <a href="https://json-schema.org/" class="external-link" target="_blank"><strong>JSON Schema</strong></a> (ponieważ OpenAPI bazuje na JSON Schema).

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
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

{!../../docs_src/body/tutorial001.py!}
```

## Crie seu modelo de dados

Então você declara seu modelo de dados como uma classe que herda `BaseModel`.

Utilize os tipos Python padrão para todos os atributos:

```Python hl_lines="7-11"
{!../../docs_src/body/tutorial001.py!}
```

import jetbrains.buildServer.configs.kotlin.BuildStep
import jetbrains.buildServer.configs.kotlin.buildSteps.script
import model.CIBuildModel
import model.Stage

class CheckTeamCityKotlinDSL(model: CIBuildModel, stage: Stage) : OsAwareBaseGradleBuildType(
    os = Os.LINUX, stage = stage, init = {
        id("${model.projectId}_CheckTeamCityKotlinDSL")
        name = "CheckTeamCityKotlinDSL"
        description = "Check Kotlin DSL in .teamcity/"

{* ../../docs_src/first_steps/tutorial001.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.

## Recap

* Import `FastAPI`.

from fastapi.datastructures import DefaultPlaceholder
from fastapi.dependencies.models import Dependant
from fastapi.dependencies.utils import (
    _get_flat_fields_from_params,
    get_flat_dependant,
    get_flat_params,
)
from fastapi.encoders import jsonable_encoder
from fastapi.openapi.constants import METHODS_WITH_BODY, REF_PREFIX, REF_TEMPLATE
from fastapi.openapi.models import OpenAPI
from fastapi.params import Body, ParamTypes