code: def forward(
      self,
      net: Tensor,
      *,
      sample_posterior: bool = True,
      apply_tanh: Optional[bool] = None,
  ) -> dict:
      distribution = self.encode(net)
      z = (
          distribution.sample()
          if sample_posterior
          else distribution.mode()
      )
      net = self.decode(z, apply_tanh=apply_tanh)
      return {
          PREDICTIONS_KEY: net,
          GaussianDistribution.key: distribution,
      }
  
imports: import torch
  import numpy
  import torch.nn
  from torch import Tensor
  from typing import Tuple
  from typing import Optional
  from cftool.types import tensor_dict_type
  from .common import IAutoEncoder
  from .common import AutoEncoderInit
  from .common import AutoEncoderLPIPSWithDiscriminator
  from ...schemas import CustomTrainStepLoss
  from ....schema import IDLModel
  from ....constants import INPUT_KEY
  from ....constants import PREDICTIONS_KEY
label_desc: Use image node_id 3 for calling the AutoEncoderKL obj's underlying member method code with example usage: obj.forward(net) and returns: dict