code: def log_images(self, batch, only_inputs=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
      log["inputs"] = x
      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 11 for calling the AutoencoderKL obj's underlying member method code with example usage: obj.log_images(batch, only_inputs) and returns: log