code: def step_pred(self, score, x, t):
      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      log_mean_coeff = (
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min)
          - 0.5 * t * self.config.beta_min
      )
      std = paddle.sqrt(1.0 - paddle.exp(2.0 * log_mean_coeff))
      score = -score / std[:, None, None, None]
  
      # compute
      dt = -1.0 / len(self.timesteps)
  
      beta_t = self.config.beta_min + t * (
          self.config.beta_max - self.config.beta_min
      )
      drift = -0.5 * beta_t[:, None, None, None] * x
      diffusion = paddle.sqrt(beta_t)
      drift = drift - diffusion[:, None, None, None] ** 2 * score
      x_mean = x + drift * dt
  
      # add noise
      noise = self.randn_like(x)
      x = x_mean + diffusion[:, None, None, None] * np.sqrt(-dt) * noise
  
      return x, x_mean
  
file_path: PaddleHub/modules/image/text_to_image/stable_diffusion_waifu/diffusers/schedulers/scheduling_sde_vp.py
imports: import numpy
  import paddle
  from ..configuration_utils import ConfigMixin
  from ..configuration_utils import register_to_config
  from .scheduling_utils import SchedulerMixin
label_desc: Use image node_id 3 for calling the ScoreSdeVpScheduler obj's underlying member method code with example usage: obj.step_pred(score, x, t) and returns: x,  x_mean