}

	return
}

func (db *DB) Row() *sql.Row {
	tx := db.getInstance().Set("rows", false)
	tx = tx.callbacks.Row().Execute(tx)
	row, ok := tx.Statement.Dest.(*sql.Row)
	if !ok && tx.DryRun {
		db.Logger.Error(tx.Statement.Context, ErrDryRunModeUnsupported.Error())
	}
	return row
}

func (db *DB) Rows() (*sql.Rows, error) {
	tx := db.getInstance().Set("rows", true)
	tx = tx.callbacks.Row().Execute(tx)

		t.Fatalf("Create failed: %v", err)
	}
	rows, err := gorm.G[User](DB).Raw("SELECT name FROM users WHERE id IN ?", []uint{user.ID, user2.ID}).Rows(ctx)
	if err != nil {
		t.Fatalf("Rows failed: %v", err)
	}

	count := 0
	for rows.Next() {
		var name string
		if err := rows.Scan(&name); err != nil {
			t.Fatalf("rows.Scan failed: %v", err)
		}
		count++
	}
	if count != 2 {

		return fc(data, batch)
	}).Error
}

func (g execG[T]) Row(ctx context.Context) *sql.Row {
	return g.g.apply(ctx).Row()
}

func (g execG[T]) Rows(ctx context.Context) (*sql.Rows, error) {
	return g.g.apply(ctx).Rows()

		t.Fatalf("AutoMigrate err: %v", err)
	}

	const ROWS = 10
	for idx := 0; idx < ROWS; idx++ {
		if err := DB.Create(&AutoIncrementStruct{}).Error; err != nil {
			t.Fatalf("create auto_increment_struct fail, err: %v", err)
		}
	}

	rows := make([]*AutoIncrementStruct, 0, ROWS)
	if err := DB.Order("id ASC").Find(&rows).Error; err != nil {
		t.Fatalf("find auto_increment_struct fail, err: %v", err)

/// tip

You could use any other SQL or NoSQL database library you want (in some cases called <abbr title="Object Relational Mapper, a fancy term for a library where some classes represent SQL tables and instances represent rows in those tables">"ORMs"</abbr>), FastAPI doesn't force you to use anything. 😎

///

}

func (cs *callbacks) Update() *processor {
	return cs.processors["update"]
}

func (cs *callbacks) Delete() *processor {
	return cs.processors["delete"]
}

func (cs *callbacks) Row() *processor {
	return cs.processors["row"]
}

func (cs *callbacks) Raw() *processor {
	return cs.processors["raw"]
}

func (p *processor) Execute(db *DB) *DB {
	// call scopes
	for len(db.Statement.scopes) > 0 {

    *   Added L2 unit normalization layer `tf.keras.layers.UnitNormalization`.
    *   Added `tf.keras.regularizers.OrthogonalRegularizer`, a new regularizer
        that encourages orthogonality between the rows (or columns) or a weight
        matrix.
    *   Added `tf.keras.layers.RandomBrightness` layer for image preprocessing.
    *   Added APIs for switching between interactive logging and absl logging.

78f4738947 title: 'Mastering Soft Delete: Advanced SQLAlchemy Techniques' - author: Shubhendra Kushwaha author_link: https://www.linkedin.com/in/theshubhendra/ link: https://theshubhendra.medium.com/role-based-row-filtering-advanced-sqlalchemy-techniques-733e6b1328f6 title: 'Role based row filtering: Advanced SQLAlchemy Techniques' German: - author: Marcel Sander (actidoo) author_link: https://www.actidoo.com link: https://www.actidoo.com/de/blog/python-fastapi-domain-driven-design title: Domain-driven...