hasTable := false
			for _, t2 := range tables {
				if t2 == t1 {
					hasTable = true
					break
				}
			}
			if !hasTable {
				t.Fatalf("Failed to get table %v when GetTables", t1)
			}
		}
	}

	for _, m := range allModels {
		if !DB.Migrator().HasTable(m) {
			t.Fatalf("Failed to create table for %#v", m)
		}
	}

	}

	if err = DB.AutoMigrate(allModels...); err != nil {
		log.Printf("Failed to auto migrate, but got error %v\n", err)
		os.Exit(1)
	}

	for _, m := range allModels {
		if !DB.Migrator().HasTable(m) {
			log.Printf("Failed to create table for %#v\n", m)
			os.Exit(1)
		}
	}

			return tx.Exec("DROP TABLE IF EXISTS ?", m.CurrentTable(stmt)).Error
		}); err != nil {
			return err
		}
	}
	return nil
}

// HasTable returns table exists or not for value, value could be a struct or string
func (m Migrator) HasTable(value interface{}) bool {
	var count int64

	m.RunWithValue(value, func(stmt *gorm.Statement) error {
		currentDatabase := m.DB.Migrator().CurrentDatabase()

func TestMultiPubSub(t *testing.T) {
	ps := New[Maskable, Maskable](2)
	ch1 := make(chan Maskable, 1)
	ch2 := make(chan Maskable, 1)
	doneCh := make(chan struct{})
	defer close(doneCh)
	if err := ps.Subscribe(MaskAll, ch1, doneCh, func(entry Maskable) bool { return true }); err != nil {
		t.Fatalf("unexpected error: %v", err)
	}
	if err := ps.Subscribe(MaskAll, ch2, doneCh, func(entry Maskable) bool { return true }); err != nil {

var GetByteBuffer = func() []byte {
	return make([]byte, 0, 4096)
}

// Sub - subscriber entity.
type Sub[T Maskable] struct {
	ch     chan T
	types  Mask
	filter func(entry T) bool
}

// PubSub holds publishers and subscribers
type PubSub[T Maskable, M Maskable] struct {
	// atomics, keep at top:
	types          uint64
	numSubscribers int32
	maxSubscribers int32

	// not atomics:

    functions. AutoGraph is also applied in functions used with `tf.data`,
    `tf.distribute` and `tf.keras` APIS.
*   Adds `enable_tensor_equality()`, which switches the behavior such that:
    *   Tensors are no longer hashable.
    *   Tensors can be compared with `==` and `!=`, yielding a Boolean Tensor
        with element-wise comparison results. This will be the default behavior
        in 2.0.

## Breaking Changes