Constant loss value in Adam optimization method

I have NN code which is written with pytorch. In the loss value calculation when code is using Adam optimization method loss values does not change during the loop while the LBGFS optimization method gets on everything is ok and loss values decrease in the normal way. How can I get rid of this issue?

    for epo in range(epo_adam):
model.train()
optimizer_adam.zero_grad()
loss , momentum_loss , loss_data , loss_BC , continuity_loss = adop_loss_Weight(model, x, y, z, u_exact, v_exact , w_exact , p_exact ,
           x_b, y_b, z_b, u_b, v_b, w_b ,p_b)
loss.backward()
if epo %500 == 0:
  print(f'Epoch Adam {epo}, Total Loss: {loss.item():.10f}')
if loss.item() <=0.15 :
  print("Optimzation Method is swtiching to LBGF-S . . . ")
  break


for epochs in range(epochs):
model.train()
loss = optimizer.step(closure)

if epochs % 20 == 0:
    print(f'Epoch LBGF-s {epochs}, Total Loss: {loss.item():.5f}')
    #print(f'The highest Loss is:  {max(momentum_loss.item() , continuity_loss.item() , loss_data.item() , loss_bc.item()):.6f}')
    #print(time.time())

I searched a lot but couldn't find any solution. I think constant loss values in Adam optimization is not reasonable!

The problem here is that there is a missing call to optimizer_adam.step(), so the weights aren't updated after calculating gradients.