Hoy much size is your data? I think that’s happen because you are loading all the outputs to Variable_store_3, and all this data can’t fit in memory. Also try using torch.no_grad(),  this means taht you are not saving the gradients to use backprop (because you are not training)
for batch in test_dataloader:
    with torch.no_grad():
        x, y = batch
        outputs = net(x)
        Variable_store_1.extend(outputs)   #for plotting purposes
        Variable_store_2 .extend(y)            #for plotting purposes
        outputs_np = outputs.detach().numpy()
        Variable_store_3 = np.append(Variable_store_3, outputs_np, axis = 0)
        loss = loss_function(outputs, y)
        running_loss += loss.item() * 128  #Batch_size