Project: 018_Learning_to_Discretize_Denoising_Diffusion_ODEs | Function: discretize_model_wrapper (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Core LD3 discretization model implementing learned time step parameterization with softmax normalization and decoupled time steps for optimal ODE discretization

**KEY TERMS:** {
  "time_plus": "softmax(ξ) softmax normalization of parameters",
  "time_md": "cumsum cumulative sum for τ'ξ(i)",
  "normed": "(τ'ξ(i) - τ'ξmin)/(τ'ξmax - τ'ξmin) normalization",
  "time_steps": "τξ(i) learned discretization time steps",
  "clipped_time2": "ξᶜᵢ decoupled time adjustment parameters"
}

**CONTEXT CODE:**                     self.cur_iter += 1
                    self._examine_checkpoint(self.cur_iter) # evaluate
                    if self.count_min_lr2_hit >= self.lr2_patient:
                        logging.info(f"{self._current_version} Reach min lr2 5 times. Stop training.")
                        return no_change, True
                
                with torch.no_grad():
                    latent, to_update_mask = self._update_latents(latent, condition, uncondition, ori_latent, img, latent_params, loss_vector_ref, self.prior_bound)
                    if loader_idx == 1 and to_update_mask.sum().item() > 0:
                        # check if this valid latent is moved
                        no_change = False
                
                ori_latent = ori_latent.reshape(-1, self.channels, self.resolution, self.resolution).detach().cpu()
                latent = latent.reshape(-1, self.channels, self.resolution, self.resolution).detach().cpu()
                img = img.detach().cpu()
                condition = condition.detach().cpu() if condition is not None else None
                uncondition = uncondition.detach().cpu() if uncondition is not None else None
                
                for j in range(latent.shape[0]):
                    ori_latents.append(ori_latent[j])
                    targets.append(img[j])
                    latents.append(latent[j])
                    conditions.append(condition[j] if condition is not None else None)
                    unconditions.append(uncondition[j] if uncondition is not None else None)
                
            # update dataset
            if self.prior_bound > 0:
                self._update_dataloader(ori_latents, latents, targets, conditions, unconditions, is_train=loader_idx==0)
            
        return no_change, False
        
    def train(self, training_rounds_v1: int, training_rounds_v2: int) -> None:
        
        total_round = training_rounds_v1 + training_rounds_v2
        self.training_rounds_v1 = training_rounds_v1

        if self.match_prior:
            self._train_to_match_prior()

        while self.cur_round < total_round:
            no_latent_change, should_stop = self._train_one_round()
            if should_stop:
                return
            self.cur_round += 1
            
            if no_latent_change and self.prior_bound > 0:
                self.shift_lr *= self.shift_lr_decay
        
        logging.info(f"{self._current_version} Max round reached, stopping")

def discretize_model_wrapper(input1, input2, lambda_max, lambda_min, noise_schedule, mode, window_rate=0.5):
    '''
    checked!
    '''
    raise NotImplementedError("Function implementation has been masked for testing")

Complete the function implementation replacing "raise NotImplementedError".