code: def decode(self, h, force_not_quantize=False):
      # also go through quantization layer
      if not force_not_quantize:
          quant, emb_loss, info = self.quantize(h)
      else:
          quant = h
      quant = self.post_quant_conv(quant)
      dec = self.decoder(quant)
      return dec
  
imports: import numpy
  import torch
  import pytorch_lightning
  import torch.nn.functional
  from contextlib import contextmanager
  from torch.optim.lr_scheduler import LambdaLR
  from ldm.modules.ema import LitEma
  from vqvae_quantize import VectorQuantizer2
  from ldm.modules.diffusionmodules.model import Encoder, Decoder
  from ldm.util import instantiate_from_config
  import ldm.models.autoencoder
  from packaging import version
label_desc: Use image node_id 3 for calling the VQModelInterface obj's underlying member method code with example usage: obj.decode(h, force_not_quantize) and returns: dec