code: def forward(self, input, return_pred_indices=False):
      quant, diff, (_, _, ind) = self.encode(input)
      dec = self.decode(quant)
      if return_pred_indices:
          return dec, diff, ind
      return dec, diff
  
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 9 for calling the VQModel obj's underlying member method code with example usage: obj.forward(input, return_pred_indices) and returns: dec,  diff, dec,  diff,  ind