t.Errorf("Should found two users's name in 1, 3, but got %v", len(users3))
	}

	db := DB.Scopes(func(tx *gorm.DB) *gorm.DB {
		return tx.Table("custom_table")
	}).Session(&gorm.Session{})

	db.AutoMigrate(&User{})
	if db.Find(&User{}).Statement.Table != "custom_table" {
		t.Errorf("failed to call Scopes")
	}

	result := DB.Scopes(NameIn1And2, func(tx *gorm.DB) *gorm.DB {
		return tx.Session(&gorm.Session{})

# Full Stack FastAPI Template

Templates, while typically come with a specific setup, are designed to be flexible and customizable. This allows you to modify and adapt them to your project's requirements, making them an excellent starting point. 🏁

You can use this template to get started, as it includes a lot of the initial set up, security, database and some API endpoints already done for you.

func (listener *httpListener) Addrs() (addrs []net.Addr) {
	for i := range listener.listeners {
		addrs = append(addrs, listener.listeners[i].Addr())
	}

	return addrs
}

// TCPOptions specify customizable TCP optimizations on raw socket
type TCPOptions struct {
	UserTimeout int // this value is expected to be in milliseconds

They are, more or less, at opposite ends, complementing each other.

Requests has a very simple and intuitive design, it's very easy to use, with sensible defaults. But at the same time, it's very powerful and customizable.

That's why, as said in the official website:

> Requests is one of the most downloaded Python packages of all time

The way you use it is very simple. For example, to do a `GET` request, you would write:

- '`kubeadm`: added validation to verify that the `CertificateKey` is a valid hex
  encoded AES key.' ([#120064](https://github.com/kubernetes/kubernetes/pull/120064), [@SataQiu](https://github.com/SataQiu))
- A customizable `OrderedScoreFuncs()` function was introduced. Out-of-tree plugins 
  that used the scheduler's preemption interface could implement this function

        deprecated and will be removed in a future release.
    *   Metrics update and collection logic in default `Model.train_step()` is
        now customizable via overriding `Model.compute_metrics()`.
    *   Losses computation logic in default `Model.train_step()` is now
        customizable via overriding `Model.compute_loss()`.
    *   `jit_compile` added to `Model.compile()` on an opt-in basis to compile