code: def _validation_step(self, batch, batch_idx, suffix=""):
      x = self.get_input(batch, self.image_key)
      xrec, qloss, ind = self(x, return_pred_indices=True)
      aeloss, log_dict_ae = self.loss(
          qloss,
          x,
          xrec,
          0,
          self.global_step,
          last_layer=self.get_last_layer(),
          split="val" + suffix,
          predicted_indices=ind,
      )
  
      discloss, log_dict_disc = self.loss(
          qloss,
          x,
          xrec,
          1,
          self.global_step,
          last_layer=self.get_last_layer(),
          split="val" + suffix,
          predicted_indices=ind,
      )
      rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
      self.log(
          f"val{suffix}/rec_loss",
          rec_loss,
          prog_bar=True,
          logger=True,
          on_step=False,
          on_epoch=True,
          sync_dist=True,
      )
      self.log(
          f"val{suffix}/aeloss",
          aeloss,
          prog_bar=True,
          logger=True,
          on_step=False,
          on_epoch=True,
          sync_dist=True,
      )
      if version.parse(pl.__version__) >= version.parse("1.4.0"):
          del log_dict_ae[f"val{suffix}/rec_loss"]
      self.log_dict(log_dict_ae)
      self.log_dict(log_dict_disc)
      return self.log_dict
  
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 13 for calling the VQModel obj's underlying member method code with example usage: obj._validation_step(batch, batch_idx, suffix) and returns: self