code: def prior_sampling(self, shape, y):
      if shape != y.shape:
          warnings.warn(
              f"Target shape {shape} does not match shape of y {y.shape}!"
              "Ignoring target shape."
          )
      std = self._std(torch.ones((y.shape[0],), device=y.device))
      x_T = y + torch.randn_like(y) * std[:, None, None, None]
      return x_T
  
imports: import abc
  import warnings
  import numpy
  import torch
label_desc: Use image node_id 9 for calling the OUVPSDE obj's underlying member method code with example usage: obj.prior_sampling(shape, y) and returns: x_T