result.Languages = append(user.Languages, result.Languages...)
	result.Toys = append(user.Toys, result.Toys...)

	sort.Slice(result.Languages, func(i, j int) bool {
		return strings.Compare(result.Languages[i].Code, result.Languages[j].Code) > 0
	})

	sort.Slice(result.Toys, func(i, j int) bool {
		return result.Toys[i].ID < result.Toys[j].ID
	})

	sort.Slice(result2.Languages, func(i, j int) bool {

		*GetUser("bulk_5", Config{Account: false, Pets: 0, Toys: 3, Company: true, Manager: false, Team: 1, Languages: 3, Friends: 1}),
		*GetUser("bulk_6", Config{Account: true, Pets: 4, Toys: 3, Company: false, Manager: true, Team: 1, Languages: 3, Friends: 0}),
		*GetUser("bulk_7", Config{Account: true, Pets: 1, Toys: 3, Company: true, Manager: true, Team: 4, Languages: 3, Friends: 1}),

		*GetUser("slice-hasmany-2", Config{Toys: 0, Tools: 2}),
		*GetUser("slice-hasmany-3", Config{Toys: 4}),
	}

	DB.Create(&users)

	// Count
	AssertAssociationCount(t, users, "Toys", 6, "")
	AssertAssociationCount(t, users, "Tools", 2, "")

	// Find
	var toys []Toy
	if DB.Model(&users).Association("Toys").Find(&toys); len(toys) != 6 {
		t.Errorf("toys count should be %v, but got %v", 6, len(toys))
	}

		ID  int
		Cat struct {
			Name string
			Toy  Toy   `gorm:"polymorphic:Owner;"`
			Toys []Toy `gorm:"polymorphic:Owner;"`
		} `gorm:"embedded;embeddedPrefix:cat_"`
		Dog struct {
			ID     int
			Name   string
			UserID int
			Toy    Toy   `gorm:"polymorphic:Owner;"`
			Toys   []Toy `gorm:"polymorphic:Owner;"`
		}
		Toys []Toy `gorm:"polymorphic:Owner;"`
	}

### Court

Des **valeurs par défaut** sont définies pour tout, des configurations optionnelles sont présentent partout. Tous ces paramètres peuvent être ajustés afin de faire ce que vous voulez et définir l'API dont vous avez besoin.

Mais, **tout fonctionne** par défaut.

### Validation

* Validation pour la plupart (ou tous?) les **types de données** Python incluant:
    * objets JSON (`dict`).

    this.max = max;
  }

  /**
   * Returns statistics over a dataset containing the given values.
   *
   * @param values a series of values, which will be converted to {@code double} values (this may
   *     cause loss of precision)
   */
  public static Stats of(Iterable<? extends Number> values) {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.addAll(values);
    return accumulator.snapshot();
  }

	"gorm.io/gorm/clause"
	. "gorm.io/gorm/utils/tests"
)

func TestPreloadWithAssociations(t *testing.T) {
	user := *GetUser("preload_with_associations", Config{
		Account:   true,
		Pets:      2,
		Toys:      3,
		Company:   true,
		Manager:   true,
		Team:      4,
		Languages: 3,
		Friends:   1,
	})

	if err := DB.Create(&user).Error; err != nil {
		t.Fatalf("errors happened when create: %v", err)
	}

   *     cause loss of precision)
   */
  public void addAll(Iterable<? extends Number> values) {
    for (Number value : values) {
      add(value.doubleValue());
    }
  }

  /**
   * Adds the given values to the dataset.
   *
   * @param values a series of values, which will be converted to {@code double} values (this may
   *     cause loss of precision)
   */

        for (long time : testTimes) {
            SMBUtil.writeTime(time, buffer, 0);
            long readTime = SMBUtil.readTime(buffer, 0);

            // Account for precision loss in conversion
            long expectedTime = ((time + SmbConstants.MILLISECONDS_BETWEEN_1970_AND_1601) * 10000L) / 10000L
                    - SmbConstants.MILLISECONDS_BETWEEN_1970_AND_1601;

* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.