code: def __init__(
      self,
      forward_sde: base_sde.ForwardSDE,
      params: TensorOrTensors,
      shapes: Sequence[torch.Size],
  ):
      # There's a mapping from the noise type of the forward SDE to the noise type of the adjoint.
      # Usually, these two aren't the same, e.g. when the forward SDE has additive noise, the adjoint SDE's diffusion
      # is a linear function of the adjoint variable, so it is not of additive noise.
      sde_type = forward_sde.sde_type
      noise_type = {
          NOISE_TYPES.general: NOISE_TYPES.general,
          NOISE_TYPES.additive: NOISE_TYPES.general,
          NOISE_TYPES.scalar: NOISE_TYPES.scalar,
          NOISE_TYPES.diagonal: NOISE_TYPES.diagonal,
      }.get(forward_sde.noise_type)
      super(AdjointSDE, self).__init__(
          sde_type=sde_type, noise_type=noise_type
      )
  
      self.forward_sde = forward_sde
      self.params = params
      self._shapes = shapes
  
      # Register the core functions. This avoids polluting the codebase with if-statements and achieves speed-ups
      # by making sure it's a one-time cost. The `sde_type` and `noise_type` of the forward SDE determines the
      # registered functions.
      self.f = {
          SDE_TYPES.ito: {
              NOISE_TYPES.diagonal: self.f_corrected_diagonal,
              NOISE_TYPES.additive: self.f_uncorrected,
              NOISE_TYPES.scalar: self.f_corrected_default,
              NOISE_TYPES.general: self.f_corrected_default,
          }.get(forward_sde.noise_type),
          SDE_TYPES.stratonovich: self.f_uncorrected,
      }.get(forward_sde.sde_type)
      self.f_and_g_prod = {
          SDE_TYPES.ito: {
              NOISE_TYPES.diagonal: self.f_and_g_prod_corrected_diagonal,
              NOISE_TYPES.additive: self.f_and_g_prod_uncorrected,
              NOISE_TYPES.scalar: self.f_and_g_prod_corrected_default,
              NOISE_TYPES.general: self.f_and_g_prod_corrected_default,
          }.get(forward_sde.noise_type),
          SDE_TYPES.stratonovich: self.f_and_g_prod_uncorrected,
      }.get(forward_sde.sde_type)
      self.g_prod_and_gdg_prod = {
          NOISE_TYPES.diagonal: self.g_prod_and_gdg_prod_diagonal,
      }.get(forward_sde.noise_type, self.g_prod_and_gdg_prod_default)
  
imports: import torch
  from .None import base_sde
  from .None import misc
  from ..settings import NOISE_TYPES, SDE_TYPES
  from ..types import Sequence, TensorOrTensors
label_desc: Use image node_id 1 to create a new AdjointSDE object from inherited base classes: base_sde with example: obj = AdjointSDE(forward_sde, params, shapes)