diff --git a/test.go b/test.go
index 94e7d56..fb317dd 100644
--- a/test.go
+++ b/test.go
@@ -6,7 +6,7 @@ import (
 	dbtypes "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
 	"git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch"
 
-	my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
+	//my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
 
 	torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
 	"github.com/charmbracelet/log"
@@ -46,25 +46,24 @@ func main() {
 
 	m.To(d)
 
-	var ones_grad float64 = 0
 	var count = 0
 
-	vars1 := m.Vs.Variables()
-
-    for k, v := range vars1 {
-        ones := torch.MustOnes(v.MustSize(), gotch.Float, d)
-        v := ones.MustSetRequiresGrad(true, false)
-        v.MustDrop()
-        ones.RetainGrad(false)
-
-        m.Vs.UpdateVarTensor(k, ones, true)
-        m.Refresh()
-    }
-
-	opt, err := my_nn.DefaultAdamConfig().Build(m.Vs, 0.001)
-	if err != nil {
-		return
-	}
+//	vars1 := m.Vs.Variables()
+//
+//    for k, v := range vars1 {
+//        ones := torch.MustOnes(v.MustSize(), gotch.Float, d)
+//        v := ones.MustSetRequiresGrad(true, false)
+//        v.MustDrop()
+//        ones.RetainGrad(false)
+//
+//        m.Vs.UpdateVarTensor(k, ones, true)
+//        m.Refresh()
+//    }
+//
+//	opt, err := my_nn.DefaultAdamConfig().Build(m.Vs, 0.001)
+//	if err != nil {
+//		return
+//	}
 
     log.Info("start")
 
@@ -75,8 +74,8 @@ func main() {
 		// ones.RetainGrad(false)
 
 		res := m.ForwardT(ones, true)
-		res = res.MustSetRequiresGrad(true, true)
-		res.RetainGrad(false)
+		//res = res.MustSetRequiresGrad(true, true)
+		//res.RetainGrad(false)
 
 		outs := torch.MustZeros([]int64{1, 18}, gotch.Float, d)
 
@@ -84,21 +83,21 @@ func main() {
 		if err != nil {
 			log.Fatal(err)
 		}
-		loss = loss.MustSetRequiresGrad(true, true)
+	//	loss = loss.MustSetRequiresGrad(true, true)
 
-		opt.ZeroGrad()
+		//opt.ZeroGrad()
 
 		log.Info("loss", "loss", loss.Float64Values())
 
 		loss.MustBackward()
 
-		opt.Step()
+		//opt.Step()
 
 		// log.Info(mean.MustGrad(false).Float64Values())
 		//ones_grad = ones.MustGrad(true).MustMax(true).Float64Values()[0]
-		log.Info(res.MustGrad(true).MustMax(true).Float64Values())
+	//	log.Info(res.MustGrad(true).MustMax(true).Float64Values())
 
-		log.Info(ones_grad)
+//		log.Info(ones_grad)
 
 		vars := m.Vs.Variables()