      # Actual training steps.
               model_out = self.model(aug_img)
               loss = self.loss(model_out,aug_locs)
               loss.backward()
               optimizer.step()
               
               <b>if (step % conf.save_step == 0) and (step>0):</b>
                  # every conf.save_step save the model to f'deepnet-{step}' file.
                  model_param_dict = {'step':step,'model_state_params':self.model.state_dict(),...}
                  # Add optimizer parameters to the 