code: def forward(self, input, sample_posterior=True):
      posterior = self.encode(input)
      if sample_posterior:
          z = posterior.sample()
      else:
          z = posterior.mode()
      dec = self.decode(z)
      return dec, posterior
  
imports: from contextlib import contextmanager
  import pytorch_lightning
  import torch
  import torch.nn.functional
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.model import Decoder, Encoder
  from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import DiagonalGaussianDistribution
  from nemo.collections.multimodal.parts.stable_diffusion.utils import instantiate_from_config
label_desc: Use image node_id 8 for calling the AutoencoderKL obj's underlying member method code with example usage: obj.forward(input, sample_posterior) and returns: dec,  posterior