code: def __init__(self, sde, fast_dg_ga_jvp_column_sum=False):
      super(ForwardSDE, self).__init__(
          sde_type=sde.sde_type, noise_type=sde.noise_type
      )
      self._base_sde = sde
  
      # 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.
  
      if hasattr(sde, "f_and_g_prod"):
          self.f_and_g_prod = sde.f_and_g_prod
      elif hasattr(sde, "f") and hasattr(sde, "g_prod"):
          self.f_and_g_prod = self.f_and_g_prod_default1
      else:  # (f_and_g,) or (f, g,).
          self.f_and_g_prod = self.f_and_g_prod_default2
  
      self.f = getattr(sde, "f", self.f_default)
      self.g = getattr(sde, "g", self.g_default)
      self.f_and_g = getattr(sde, "f_and_g", self.f_and_g_default)
      self.g_prod = getattr(sde, "g_prod", self.g_prod_default)
      self.prod = {NOISE_TYPES.diagonal: self.prod_diagonal}.get(
          sde.noise_type, self.prod_default
      )
      self.g_prod_and_gdg_prod = {
          NOISE_TYPES.diagonal: self.g_prod_and_gdg_prod_diagonal,
          NOISE_TYPES.additive: self.g_prod_and_gdg_prod_additive,
      }.get(sde.noise_type, self.g_prod_and_gdg_prod_default)
      self.dg_ga_jvp_column_sum = {
          NOISE_TYPES.general: (
              self.dg_ga_jvp_column_sum_v2
              if fast_dg_ga_jvp_column_sum
              else self.dg_ga_jvp_column_sum_v1
          )
      }.get(sde.noise_type, self._return_zero)
  
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 ForwardSDE object from inherited base classes: BaseSDE with example: obj = ForwardSDE(sde, fast_dg_ga_jvp_column_sum)