code: def log_images(
      self, batch, only_inputs=False, log_ema=False, **kwargs
  ):
      log = dict()
      x = self.get_input(batch, self.image_key)
      x = x.to(self.device)
      if not only_inputs:
          xrec, posterior = 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["samples"] = self.decode(
              torch.randn_like(posterior.sample())
          )
          log["reconstructions"] = xrec
          if log_ema or self.use_ema:
              with self.ema_scope():
                  xrec_ema, posterior_ema = self(x)
                  if x.shape[1] > 3:
                      # colorize with random projection
                      assert xrec_ema.shape[1] > 3
                      xrec_ema = self.to_rgb(xrec_ema)
                  log["samples_ema"] = self.decode(
                      torch.randn_like(posterior_ema.sample())
                  )
                  log["reconstructions_ema"] = xrec_ema
      log["inputs"] = x
      return log
  
imports: from contextlib import contextmanager
  import lightning.pytorch
  import torch
  from ldm.modules.diffusionmodules.model import Decoder, Encoder
  from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
  from ldm.modules.ema import LitEma
  from torch.nn import Identity
  from torch.nn import functional
label_desc: Use image node_id 14 for calling the AutoencoderKL obj's underlying member method code with example usage: obj.log_images(batch, only_inputs, log_ema) and returns: log