code: def reconstruct(self, net: Tensor, **kwargs: Any) -> Tensor:
      name = self.__class__.__name__
      if not self.can_reconstruct:
          raise ValueError(f"`{name}` does not support `reconstruct`")
      labels = kwargs.pop(LABEL_KEY, None)
      batch = {INPUT_KEY: net, LABEL_KEY: labels}
      if self.is_conditional and labels is None:
          raise ValueError(
              f"`{LABEL_KEY}` should be provided in `reconstruct` "
              f"for conditional `{name}`"
          )
      return _forward(self, 0, batch, INPUT_KEY, **kwargs)[
          PREDICTIONS_KEY
      ]
  
imports: import torch
  import random
  from abc import abstractmethod
  from abc import ABCMeta
  from torch import Tensor
  from typing import Any
  from typing import Optional
  from cftool.misc import safe_execute
  from cftool.misc import shallow_copy_dict
  from cftool.types import tensor_dict_type
  from ...schema import _forward
  from ...constants import INPUT_KEY
  from ...constants import LABEL_KEY
  from ...constants import PREDICTIONS_KEY
  from ...misc.toolkit import slerp
  from ...misc.toolkit import get_device
label_desc: Use image node_id 5 for calling the GeneratorMixin obj's underlying member method code with example usage: obj.reconstruct(net) and returns: _forward