Önce, `pydantic` içinden `BaseModel`'i import etmeniz gerekir:

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

## Veri modelinizi oluşturun { #create-your-data-model }

Sonra veri modelinizi, `BaseModel`'den kalıtım alan bir class olarak tanımlarsınız.

Tüm attribute'lar için standart Python type'larını kullanın:

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

	stmt := dryRunDB.Create(&user).Statement
	if stmt.SQL.String() == "" || len(stmt.Vars) != 9 {
		t.Errorf("Failed to generate sql, got %v", stmt.SQL.String())
	}

	stmt2 := dryRunDB.Find(&user, "id = ?", user.ID).Statement
	if stmt2.SQL.String() == "" || len(stmt2.Vars) != 1 {
		t.Errorf("Failed to generate sql, got %v", stmt2.SQL.String())
	}
}

type ageInt int8

	objectAPI := api.ObjectAPI()
	if objectAPI == nil {
		writeErrorResponse(ctx, w, errorCodes.ToAPIErr(ErrServerNotInitialized), r.URL)
		return
	}

	vars := mux.Vars(r)
	bucket := vars["bucket"]
	object, err := unescapePath(vars["object"])
	if err != nil {
		writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
		return
	}

	// Check for auth type to return S3 compatible error.

package clause_test

import (
	"fmt"
	"testing"

	"gorm.io/gorm/clause"
)

func TestGroupBy(t *testing.T) {
	results := []struct {
		Clauses []clause.Interface
		Result  string
		Vars    []interface{}
	}{
		{
			[]clause.Interface{clause.Select{}, clause.From{}, clause.GroupBy{
				Columns: []clause.Column{{Name: "role"}},
				Having:  []clause.Expression{clause.Eq{"role", "admin"}},
			}},

/// note | Detalles técnicos

En realidad, `Query`, `Path` y otros que verás a continuación crean objetos de subclases de una clase común `Param`, que es a su vez una subclase de la clase `FieldInfo` de Pydantic.

Y `Field` de Pydantic también regresa una instance de `FieldInfo`.

`Body` también devuelve objetos de una subclase de `FieldInfo` directamente. Y hay otros que verás más adelante que son subclases de la clase `Body`.

package clause_test

import (
	"fmt"
	"testing"

	"gorm.io/gorm/clause"
)

func TestLocking(t *testing.T) {
	results := []struct {
		Clauses []clause.Interface
		Result  string
		Vars    []interface{}
	}{
		{
			[]clause.Interface{clause.Select{}, clause.From{}, clause.Locking{Strength: clause.LockingStrengthUpdate}},
			"SELECT * FROM `users` FOR UPDATE", nil,
		},
		{

E o resto será manipulado pelo **FastAPI**.

Se você rodar a aplicação e ir até [http://localhost:8000/v1/](http://localhost:8000/v1/), você verá o retorno do Flask:

```txt
Hello, World from Flask!
```

E se você for até [http://localhost:8000/v2](http://localhost:8000/v2), você verá o retorno do FastAPI:

```JSON
{
    "message": "Hello World"
}

        return uuid.UUID

    @property
    def __dict__(self):
        """Spoof a missing __dict__ by raising TypeError, this is how
        asyncpg.pgroto.pgproto.UUID behaves"""
        raise TypeError("vars() argument must have __dict__ attribute")


def test_pydanticv2():
    from pydantic import field_serializer

    app = FastAPI()

    @app.get("/fast_uuid")
    def return_fast_uuid():

var (
	_ clause.Assigner = clause.Assignment{}
	_ clause.Assigner = clause.Set{}
)

func TestSet(t *testing.T) {
	results := []struct {
		Clauses []clause.Interface
		Result  string
		Vars    []interface{}
	}{
		{
			[]clause.Interface{
				clause.Update{},
				clause.Set([]clause.Assignment{{clause.PrimaryColumn, 1}}),
			},
			"UPDATE `users` SET `users`.`id`=?",
			[]interface{}{1},
		},

---

For the next terms, use the following translations:

* framework: framework (do not translate to "marco")
* performance: rendimiento
* program (verb): programar
* code (verb): programar
* type hints: anotaciones de tipos
* type annotations: anotaciones de tipos
* autocomplete: autocompletado
* completion (in the context of autocompletion): autocompletado
* feature: funcionalidad