<option value="$PROJECT_DIR$/platforms/core-configuration/declarative-dsl-tooling-builders" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/declarative-dsl-tooling-models" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/dependency-management-serialization-codecs" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/encryption-services" />

/// info

Note that the `TestClient` receives data that can be converted to JSON, not Pydantic models.

If you have a Pydantic model in your test and you want to send its data to the application during testing, you can use the `jsonable_encoder` described in [JSON Compatible Encoder](encoder.md){.internal-link target=_blank}.

        end
    end

    style V2 fill:#f9fff3
    style V1 fill:#fff6f0
    style V1Field fill:#fff6f0
    style V2Field fill:#f9fff3
```

...but, you can have separated models using Pydantic v1 and v2 in the same app.

```mermaid
graph TB
    subgraph "✅ Supported"
        direction TB
        subgraph V2["Pydantic v2 Model"]
            V2Field["Pydantic v2 Model"]
        end

    for (int exponent : asList(2, 3, 4, 9, 15, 16, 17, 24, 25, 30)) {
      int x = 1 << exponent;
      intValues.add(x, x + 1, x - 1);
    }
    intValues.add(9999).add(10000).add(10001).add(1000000); // near powers of 10
    intValues.add(5792).add(5793); // sqrt(2^25) rounded up and down
    POSITIVE_INTEGER_CANDIDATES = intValues.build();
    NEGATIVE_INTEGER_CANDIDATES =
        ImmutableList.copyOf(

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

from sqlmodel.main import default_registry

from tests.utils import needs_py310


def clear_sqlmodel():
    # Clear the tables in the metadata for the default base model
    SQLModel.metadata.clear()
    # Clear the Models associated with the registry, to avoid warnings
    default_registry.dispose()


@pytest.fixture(
    name="client",
    params=[
        pytest.param("tutorial002_py39"),

Perceba que isso significa que "`one_person` é uma **instância** da classe `Person`".

Isso não significa que "`one_person` é a **classe** chamada `Person`".

## Modelos Pydantic { #pydantic-models }

O <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> é uma biblioteca Python para executar a validação de dados.

Você declara a "forma" dos dados como classes com atributos.

<img src="/img/python-types/image06.png">

Nota que esto significa "`one_person` es una **instance** de la clase `Person`".

No significa "`one_person` es la **clase** llamada `Person`".

## Modelos Pydantic { #pydantic-models }

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> es un paquete de Python para realizar la validación de datos.

Declaras la "forma" de los datos como clases con atributos.

				})
			}
		}
	})

	var models []User
	if err := DB.Where("name in (?)", []string{"find"}).Find(&models).Error; err != nil || len(models) != 3 {
		t.Errorf("errors happened when query find with in clause: %v, length: %v", err, len(models))
	} else {
		for idx, user := range users {
			t.Run("FindWithInClause#"+strconv.Itoa(idx+1), func(t *testing.T) {
				CheckUser(t, models[idx], user)
			})
		}
	}

            // effective model.
            //
            // As of 3.4, the model version is mandatory even in raw models. The XML element still is optional in the
            // XML schema and this will not change anytime soon. We do not want to build effective models based on
            // models without a version starting with 3.4.