code: def get_ode_sampler(self, y, N=None, minibatch=None, **kwargs):
      N = self.sde.N if N is None else N
      sde = self.sde.copy()
      sde.N = N
  
      kwargs = {"eps": self.t_eps, **kwargs}
      if minibatch is None:
          return sampling.get_ode_sampler(
              sde, self.score_fn, y=y, device=y.device, **kwargs
          )
      else:
          M = y.shape[0]
  
          def batched_sampling_fn():
              samples, ns = [], []
              for i in range(int(math.ceil(M / minibatch))):
                  y_mini = y[i * minibatch : (i + 1) * minibatch]
                  sampler = sampling.get_ode_sampler(
                      sde,
                      self.score_fn,
                      y=y_mini,
                      device=y_mini.device,
                      **kwargs,
                  )
                  sample, n = sampler()
                  samples.append(sample)
                  ns.append(n)
              samples = torch.cat(samples, dim=0)
              return sample, ns
  
          return batched_sampling_fn
  
file_path: espnet/espnet2/enh/diffusion/score_based_diffusion.py
imports: import math
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import Dict, Optional, Tuple
  import torch
  import espnet2.enh.diffusion.sampling
  from espnet2.enh.diffusion.abs_diffusion import AbsDiffusion
  from espnet2.enh.diffusion.sdes import OUVESDE, OUVPSDE, SDE
  from espnet2.enh.layers.dcunet import DCUNet
  from espnet2.enh.layers.ncsnpp import NCSNpp
  from espnet2.train.class_choices import ClassChoices
label_desc: Use image node_id 4 for calling the ScoreModel obj's underlying member method code with example usage: obj.get_ode_sampler(y, N, minibatch) and returns: sampling, batched_sampling_fn, sample,  ns