code: def get_ode_sampler(
      sde,
      score_fn,
      y,
      inverse_scaler=None,
      denoise=True,
      rtol=1e-5,
      atol=1e-5,
      method="RK45",
      eps=3e-2,
      device="cuda",
      **kwargs,
  ):
      """Probability flow ODE sampler with the black-box ODE solver.
  
      Args:
          sde: An `sdes.SDE` object representing the forward SDE.
          score_fn: A function (typically learned model) that predicts the score.
          y: A `torch.Tensor`, representing the (non-white-)noisy starting point(s)
              to condition the prior on.
          inverse_scaler: The inverse data normalizer.
          denoise: If `True`, add one-step denoising to final samples.
          rtol: A `float` number. The relative tolerance level of the ODE solver.
          atol: A `float` number. The absolute tolerance level of the ODE solver.
          method: A `str`. The algorithm used for the black-box ODE solver.
              See the documentation of `scipy.integrate.solve_ivp`.
          eps: A `float` number. The reverse-time SDE/ODE will be integrated to
              `eps` for numerical stability.
          device: PyTorch device.
  
      Returns:
          A sampling function that returns samples and the number of function
          evaluations during sampling.
      """
      predictor = ReverseDiffusionPredictor(
          sde, score_fn, probability_flow=False
      )
      rsde = sde.reverse(score_fn, probability_flow=True)
  
      def denoise_update_fn(x):
          vec_eps = torch.ones(x.shape[0], device=x.device) * eps
          _, x = predictor.update_fn(x, vec_eps, y)
          return x
  
      def drift_fn(x, t, y):
          """Get the drift function of the reverse-time SDE."""
          return rsde.sde(x, t, y)[0]
  
      def ode_sampler(z=None, **kwargs):
          """The probability flow ODE sampler with black-box ODE solver.
  
          Args:
              model: A score model.
              z: If present, generate samples from latent code `z`.
          Returns:
              samples, number of function evaluations.
          """
          with torch.no_grad():
              # If not represent, sample the latent code from the
              # prior distibution of the SDE.
              x = sde.prior_sampling(y.shape, y).to(device)
  
              def ode_func(t, x):
                  x = (
                      from_flattened_numpy(x, y.shape)
                      .to(device)
                      .type(torch.complex64)
                  )
                  vec_t = torch.ones(y.shape[0], device=x.device) * t
                  drift = drift_fn(x, vec_t, y)
                  return to_flattened_numpy(drift)
  
              # Black-box ODE solver for the probability flow ODE
              solution = integrate.solve_ivp(
                  ode_func,
                  (sde.T, eps),
                  to_flattened_numpy(x),
                  rtol=rtol,
                  atol=atol,
                  method=method,
                  **kwargs,
              )
              nfe = solution.nfev
              x = (
                  torch.tensor(solution.y[:, -1])
                  .reshape(y.shape)
                  .to(device)
                  .type(torch.complex64)
              )
  
              # Denoising is equivalent to running one predictor step without adding noise
              if denoise:
                  x = denoise_update_fn(x)
  
              if inverse_scaler is not None:
                  x = inverse_scaler(x)
              return x, nfe
  
      return ode_sampler
  
imports: import torch
  from scipy import integrate
  from .correctors import Corrector, corrector_dict
  from .predictors import Predictor, ReverseDiffusionPredictor, predictor_dict
label_desc: Use image node_id 4 for calling a global function with example usage: get_ode_sampler(sde, score_fn, y, inverse_scaler, denoise, rtol, atol, method, eps, device) and returns: ode_sampler, x, rsde, x,  nfe, to_flattened_numpy