code: def training_step(self, batch, batch_idx, optimizer_idx):
      # https://github.com/pytorch/pytorch/issues/37142
      # try not to fool the heuristics
      x = self.get_input(batch, self.image_key)
      xrec, qloss, ind = self(x, return_pred_indices=True)
  
      if optimizer_idx == 0:
          # autoencode
          aeloss, log_dict_ae = self.loss(
              qloss,
              x,
              xrec,
              optimizer_idx,
              self.global_step,
              last_layer=self.get_last_layer(),
              split="train",
              predicted_indices=ind,
          )
  
          self.log_dict(
              log_dict_ae,
              prog_bar=False,
              logger=True,
              on_step=True,
              on_epoch=True,
          )
          return aeloss
  
      if optimizer_idx == 1:
          # discriminator
          discloss, log_dict_disc = self.loss(
              qloss,
              x,
              xrec,
              optimizer_idx,
              self.global_step,
              last_layer=self.get_last_layer(),
              split="train",
          )
          self.log_dict(
              log_dict_disc,
              prog_bar=False,
              logger=True,
              on_step=True,
              on_epoch=True,
          )
          return discloss
  
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 11 for calling the VQModel obj's underlying member method code with example usage: obj.training_step(batch, batch_idx, optimizer_idx) and returns: aeloss, discloss