code: def log_images(
      self, batch, only_inputs=False, plot_ema=False, **kwargs
  ):
      log = dict()
      x = self.get_input(batch, self.image_key)
      x = x.to(self.device)
      if only_inputs:
          log["inputs"] = x
          return log
      xrec, _ = self(x)
      if x.shape[1] > 3:
          # colorize with random projection
          assert xrec.shape[1] > 3
          x = self.to_rgb(x)
          xrec = self.to_rgb(xrec)
      log["inputs"] = x
      log["reconstructions"] = xrec
      if plot_ema:
          with self.ema_scope():
              xrec_ema, _ = self(x)
              if x.shape[1] > 3:
                  xrec_ema = self.to_rgb(xrec_ema)
              log["reconstructions_ema"] = xrec_ema
      return log
  
imports: import torch
  import pytorch_lightning
  import torch.nn.functional
  from contextlib import contextmanager
  from taming.modules.vqvae.quantize import VectorQuantizer2
  from ldm.modules.diffusionmodules.model import Encoder, Decoder
  from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
  from ldm.util import instantiate_from_config
label_desc: Use image node_id 16 for calling the VQModel obj's underlying member method code with example usage: obj.log_images(batch, only_inputs, plot_ema) and returns: log, log