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

	var emptyUser User
	var err error
	// belongs to
	err = DB.Model(&emptyUser).Association("Company").Delete(&user1.Company)
	AssertEqual(t, err, gorm.ErrPrimaryKeyRequired)
	// has many
	err = DB.Model(&emptyUser).Association("Pets").Delete(&user1.Pets)
	AssertEqual(t, err, gorm.ErrPrimaryKeyRequired)
	// has one
	err = DB.Model(&emptyUser).Association("Account").Delete(&user1.Account)

## Importe `Field`

Primeiro, você tem que importá-lo:

{* ../../docs_src/body_fields/tutorial001.py hl[4] *}

/// warning | Aviso

Note que `Field` é importado diretamente do `pydantic`, não do `fastapi` como todo o resto (`Query`, `Path`, `Body`, etc).

///

## Declare atributos do modelo

		}
	}

	r = dryDB.Table("(?) as u", DB.Model(&User{}).Select("name")).Find(&User{}).Statement
	if !regexp.MustCompile("SELECT \\* FROM \\(SELECT .name. FROM .users. WHERE .users.\\..deleted_at. IS NULL\\) as u WHERE .u.\\..deleted_at. IS NULL").MatchString(r.Statement.SQL.String()) {
		t.Errorf("Table with escape character, got %v", r.Statement.SQL.String())
	}

	// Clear
	DB.Model(&users).Association("Company").Clear()
	AssertAssociationCount(t, users, "Company", 0, "After Clear")

	DB.Model(&users).Association("Manager").Clear()
	AssertAssociationCount(t, users, "Manager", 0, "After Clear")

	// shared company
	company := Company{Name: "shared"}
	if err := DB.Model(&users[0]).Association("Company").Append(&company); err != nil {

////

/// info

In Pydantic Version 1 hieß die Methode zum Abrufen des JSON-Schemas für ein Modell `Item.schema()`, in Pydantic Version 2 heißt die Methode `Item.model_json_schema()`.

///

Obwohl wir nicht die standardmäßig integrierte Funktionalität verwenden, verwenden wir dennoch ein Pydantic-Modell, um das JSON-Schema für die Daten, die wir in YAML empfangen möchten, manuell zu generieren.

///

## Declarar atributos del modelo

Después puedes utilizar `Field` con los atributos del modelo:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[11:14] *}

`Field` funciona de la misma manera que `Query`, `Path` y `Body`, tiene todos los mismos parámetros, etc.

/// note | Detalles técnicos

import java.util.List;
import java.util.Map;
import java.util.Properties;

import org.apache.maven.model.Activation;
import org.apache.maven.model.Profile;
import org.apache.maven.model.building.ModelProblem;
import org.apache.maven.model.profile.DefaultProfileActivationContext;
import org.apache.maven.model.profile.ProfileSelector;
import org.apache.maven.profiles.activation.ProfileActivationException;

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

It receives an object, like a Pydantic model, and returns a JSON compatible version:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

In this example, it would convert the Pydantic model to a `dict`, and the `datetime` to a `str`.

     */
    @Nonnull
    Optional<Boolean> plugins();

    /**
     * Should apply all upgrade options (equivalent to --model-version 4.1.0 --infer --model --plugins).
     * This is a convenience option that combines model upgrade, inference, compatibility fixes, and plugin upgrades.
     *
     * @return an {@link Optional} containing the boolean value {@code true} if specified, or empty
     */