code: def _preprocess(
      self, net: Tensor, *, deterministic: bool = False
  ) -> Tensor:
      net = self.first_stage.encode(net)
      if isinstance(net, GaussianDistribution):
          net = net.mode() if deterministic else net.sample()
      net = self.scale_factor * net
      return net
  
imports: import json
  import numpy
  from enum import Enum
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import Tuple
  from typing import Union
  from typing import Optional
  from cftool.misc import print_info
  from cftool.misc import print_warning
  from cftool.array import tensor_dict_type
  from .ddpm import make_condition_model
  from .ddpm import DDPM
  from .utils import CROSS_ATTN_TYPE
  from ..ae.kl import GaussianDistribution
  from ....zoo import DLZoo
  from ....schema import IDLModel
  from ....misc.toolkit import freeze
  from ....misc.toolkit import get_tensors
  from ....misc.toolkit import download_static
  from ....modules.blocks import IHook
  from ....modules.blocks import LoRAManager
label_desc: Use image node_id 3 for calling the LDM obj's underlying member method code with example usage: obj._preprocess(net) and returns: net