class_docstr: Base class for all SDEs.
  
  Inheriting from this class ensures `noise_type` and `sde_type` are valid attributes, which the solver depends on.
class_docstr_tok: ["Base","class","for","all","SDEs",".","Inheriting","from","this","class","ensures","`","noise_type","`","and","`","sde_type","`","are","valid","attributes",",","which","the","solver","depends","on","."]
code: def __init__(self, noise_type, sde_type):
      super(BaseSDE, self).__init__()
      if noise_type not in NOISE_TYPES:
          raise ValueError(
              f"Expected noise type in {NOISE_TYPES}, but found {noise_type}"
          )
      if sde_type not in SDE_TYPES:
          raise ValueError(
              f"Expected sde type in {SDE_TYPES}, but found {sde_type}"
          )
      # Making these Python properties breaks `torch.jit.script`.
      self.noise_type = noise_type
      self.sde_type = sde_type
  
imports: import abc
  import torch
  from torch import nn
  from .None import misc
  from ..settings import NOISE_TYPES, SDE_TYPES
  from ..types import Tensor
label_desc: Use image node_id 1 to create a new BaseSDE object from inherited base classes: abc, nn with example: obj = BaseSDE(noise_type, sde_type)