diff --git a/mpq/mpq_quantize.py b/mpq/mpq_quantize.py
index d72ae15..6eb5161 100644
--- a/mpq/mpq_quantize.py
+++ b/mpq/mpq_quantize.py
@@ -474,10 +474,10 @@ def dse(og_model, max_acc_drop, weights_per_layer, fp_accuracy, train_loader, te
             w = weights_per_layer[mid]
             
             f_w = []
-            for i in range(len(seq_counts)):
-                t_w = w[i]
-                c,l = seq_counts[i]
-                for j in range(c+l):
+            for j in range(len(seq_counts)):
+                t_w = w[j]
+                c,l = seq_counts[j]
+                for _ in range(c+l):
                     f_w.append(t_w)
 
             if(len(seq_counts) > 0):
@@ -489,9 +489,9 @@ def dse(og_model, max_acc_drop, weights_per_layer, fp_accuracy, train_loader, te
             quant_net = quant_net.to(device)
             print(f'==========================\nEvaluating Configuration: {mid} --> Weights: {w}')
 
-            for i in range(len(epochs)):
+            for k in range(len(epochs)):
                 quant_net = train_quant_model(quant_net, train_loader, val_loader, device,
-                                      epochs = epochs[i], lr = lr[i])
+                                      epochs = epochs[k], lr = lr[k])
             
             # Evaluate the trained quantized network
             accuracy = quant_net_evaluation(quant_net, test_loader, device)
@@ -518,10 +518,10 @@ def dse(og_model, max_acc_drop, weights_per_layer, fp_accuracy, train_loader, te
         test_accuracy = []
         for i, w in enumerate(weights_per_layer):
             f_w = []
-            for i in range(len(seq_counts)):
-                t_w = w[i]
-                c,l = seq_counts[i]
-                for j in range(c+l):
+            for j in range(len(seq_counts)):
+                t_w = w[j]
+                c,l = seq_counts[j]
+                for _ in range(c+l):
                     f_w.append(t_w)
 
             if(len(seq_counts) > 0):
@@ -531,9 +531,9 @@ def dse(og_model, max_acc_drop, weights_per_layer, fp_accuracy, train_loader, te
             quant_net = Quant_Model(og_model, w, layer_mapping, sign)
             quant_net = quant_net.to(device)
             print(f'===================================\nModel No {i} --> {w}')
-            for i in range(len(epochs)):
+            for k in range(len(epochs)):
                 quant_net = train_quant_model(quant_net, train_loader, val_loader, device,
-                                      epochs = epochs[i], lr = lr[i])
+                                      epochs = epochs[k], lr = lr[k])
             accuracy = quant_net_evaluation(quant_net, test_loader, device)
             test_accuracy.append(accuracy)