///

#### Данные со значениями для полей, имеющих значения по умолчанию { #data-with-values-for-fields-with-defaults }

Но если в ваших данных есть значения для полей модели, для которых указаны значения по умолчанию, как у элемента с ID `bar`:

```Python hl_lines="3  5"
{
    "name": "Bar",

	}
	return xlMeta
}

// Compare the unmarshaled XLMetaV1 with the one obtained from jsoniter parsing.
func compareXLMetaV1(t *testing.T, unMarshalXLMeta, jsoniterXLMeta xlMetaV1Object) {
	// Start comparing the fields of xlMetaV1Object obtained from jsoniter parsing with one parsed using json unmarshalling.
	if unMarshalXLMeta.Version != jsoniterXLMeta.Version {

Puedes usar todas las mismas funcionalidades de validación y herramientas que usas para los modelos de Pydantic, como diferentes tipos de datos y validaciones adicionales con `Field()`.

//// tab | Pydantic v2

{* ../../docs_src/settings/tutorial001_py39.py hl[2,5:8,11] *}

////

//// tab | Pydantic v1

/// info | Información

	}
}

func TestUpdateColumnsSkipsAssociations(t *testing.T) {
	user := *GetUser("update_column_skips_association", Config{})
	DB.Create(&user)

	// Update a single field of the user and verify that the changed address is not stored.
	newAge := uint(100)
	user.Account.Number = "new_account_number"
	db := DB.Model(&user).UpdateColumns(User{Age: newAge})

	if db.RowsAffected != 1 {

= X/Z, y = Y/Z, and xy = T/Z per https://eprint.iacr.org/2008/522. x, y, z, t field.Element } type incomparable [0]func() func checkInitialized(points ...*Point) { for _, p := range points { if p.x == (field.Element{}) && p.y == (field.Element{}) { panic("edwards25519: use of uninitialized Point") } } } type projCached struct { YplusX, YminusX, Z, T2d field.Element } type affineCached struct { YplusX, YminusX, T2d field.Element } // Constructors. func (v *projP2) Zero() *projP2 { v.X.Zero() v.Y.One()...

		return nil
	}
	return map[string]string{
		c.Type.String():       c.Encoded,
		xhttp.AmzChecksumType: c.Type.ObjType(),
	}
}

// Equal returns whether two checksum structs are equal in all their fields.
func (c *Checksum) Equal(s *Checksum) bool {
	if c == nil || s == nil {
		return c == s
	}
	return c.Type == s.Type &&
		c.Encoded == s.Encoded &&
		bytes.Equal(c.Raw, s.Raw) &&

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

{* ../../docs_src/settings/tutorial001_py39.py hl[2,5:8,11] *}

////

//// tab | Pydantic v1

/// info | Informação

		bucketLoader:  loader,
	}
	mg.validate()
	return mg
}

// AddExtraLabels - adds extra (constant) label KV pairs to the metrics group.
// This is a helper to initialize the `ExtraLabels` field. The argument is a
// list of ordered label name and value pairs.
func (mg *MetricsGroup) AddExtraLabels(labels ...string) {
	if len(labels)%2 != 0 {
		panic("Labels must be an ordered list of name value pairs")
	}

pkg debug/pe, type StringTable []uint8
pkg encoding/base64, method (Encoding) Strict() *Encoding
pkg encoding/json, method (RawMessage) MarshalJSON() ([]uint8, error)
pkg encoding/json, type UnmarshalTypeError struct, Field string
pkg encoding/json, type UnmarshalTypeError struct, Struct string
pkg expvar, func Handler() http.Handler
pkg expvar, method (*Float) Value() float64
pkg expvar, method (Func) Value() interface{}

		},
		{
			name:       "select-in-nested-float-no-flat",
			query:      `SELECT s.nested from s3object s WHERE 8.5 IN s.nested[*]`,
			wantResult: ``,
		},
		{
			name:       "select-empty-field-result",
			query:      `SELECT * from s3object s WHERE s.nested[0][0] = 2`,
			wantResult: `{"id":3,"title":"Second Record","desc":"another text","nested":[[2,3,4],[7,8.5,9]]}`,
		},
		{