t.Fatalf("errors happened when query: %v", err)
			} else {
				CheckUser(t, newUser, user)
			}
		}
	}
}

func TestCreateFromMap(t *testing.T) {
	if err := DB.Model(&User{}).Create(map[string]interface{}{"Name": "create_from_map", "Age": 18}).Error; err != nil {
		t.Fatalf("failed to create data from map, got error: %v", err)
	}

	var result User

import org.junit.jupiter.api.Test
import org.mockserver.client.MockServerClient
import org.mockserver.configuration.Configuration
import org.mockserver.logging.MockServerLogger
import org.mockserver.model.HttpRequest.request
import org.mockserver.model.HttpResponse.response
import org.mockserver.proxyconfiguration.ProxyConfiguration
import org.mockserver.socket.tls.KeyStoreFactory
import org.testcontainers.containers.MockServerContainer

Adotar temporariamente esse ponto de vista (do *desenvolvedor externo*) pode ajudar a perceber mais facilmente onde colocar os parâmetros, o modelo Pydantic para o corpo, para a resposta, etc. para essa *API externa*.

///

### Crie um `APIRouter` de callback { #create-a-callback-apirouter }

Primeiro crie um novo `APIRouter` que conterá um ou mais callbacks.

    id: str
    value: str


class Message(BaseModel):
    message: str


app = FastAPI()


@app.get(
    "/items/{item_id}",
    response_model=Item,
    responses={
        404: {"model": Message, "description": "The item was not found"},
        200: {
            "description": "Item requested by ID",
            "content": {
                "application/json": {

specific language governing permissions and limitations
under the License.
-->

<model xmlns="http://modello.codehaus.org/MODELLO/1.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://modello.codehaus.org/MODELLO/1.0.0 http://modello.codehaus.org/xsd/modello-1.0.0.xsd"
  xml.namespace="http://maven.apache.org/PARAMDOC/${version}"

import io.mockk.mockk
import io.mockk.slot
import jetbrains.buildServer.configs.kotlin.BuildStep
import jetbrains.buildServer.configs.kotlin.BuildSteps
import jetbrains.buildServer.configs.kotlin.DslContext
import model.CIBuildModel
import model.JsonBasedGradleSubprojectProvider
import org.junit.jupiter.api.Assertions.assertEquals
import org.junit.jupiter.api.BeforeEach
import org.junit.jupiter.api.Test
import org.junit.jupiter.api.extension.ExtendWith

import org.gradle.api.tasks.PathSensitivity
import org.gradle.api.tasks.TaskAction
import gradlebuild.docs.dsl.links.ClassLinkMetaData
import gradlebuild.docs.dsl.links.LinkMetaData
import gradlebuild.docs.model.ClassMetaDataRepository
import gradlebuild.docs.model.SimpleClassMetaDataRepository
import org.w3c.dom.Document
import org.w3c.dom.Element
/**
 * Transforms userguide source into docbook, replacing custom XML elements.
 *

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package gradlebuild.docs.dsl.docbook.model

import gradlebuild.docs.dsl.source.model.ClassMetaData
import org.w3c.dom.Document
import org.w3c.dom.Element
import org.w3c.dom.Node

class ClassDoc implements DslElementDoc {
    private final String className

Importe `BaseSettings` do Pydantic e crie uma subclasse, muito parecido com um modelo do Pydantic.

Da mesma forma que com modelos do Pydantic, você declara atributos de classe com anotações de tipo e, possivelmente, valores padrão.

Você pode usar as mesmas funcionalidades e ferramentas de validação que usa em modelos do Pydantic, como diferentes tipos de dados e validações adicionais com `Field()`.

//// tab | Pydantic v2

 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package gradlebuild.identity.model


data class ReleasedVersions(
    val latestReleaseSnapshot: ReleasedVersion,
    val latestRc: ReleasedVersion,
    val finalReleases: List<ReleasedVersion>