code: def log_images(
      self,
      batch: Dict,
      N: int = 8,
      sample: bool = True,
      ucg_keys: List[str] = None,
      **kwargs,
  ) -> Dict:
      conditioner_input_keys = [
          e.input_key for e in self.conditioner.embedders
      ]
      if ucg_keys:
          assert all(
              map(lambda x: x in conditioner_input_keys, ucg_keys)
          ), (
              "Each defined ucg key for sampling must be in the provided conditioner input keys,"
              f"but we have {ucg_keys} vs. {conditioner_input_keys}"
          )
      else:
          ucg_keys = conditioner_input_keys
      log = dict()
  
      x = self.get_input(batch)
  
      c, uc = self.conditioner.get_unconditional_conditioning(
          batch,
          force_uc_zero_embeddings=ucg_keys
          if len(self.conditioner.embedders) > 0
          else [],
      )
  
      sampling_kwargs = {}
  
      N = min(x.shape[0], N)
      x = x.to(self.device)[:N]
      log["inputs"] = x
      z = self.encode_first_stage(x)
      log["reconstructions"] = self.decode_first_stage(z)
      log.update(self.log_conditionings(batch, N))
  
      for k in c:
          if isinstance(c[k], torch.Tensor):
              c[k], uc[k] = map(
                  lambda y: y[k][:N].to(self.device), (c, uc)
              )
  
      if sample:
          with self.ema_scope("Plotting"):
              samples = self.sample(
                  c,
                  shape=z.shape[1:],
                  uc=uc,
                  batch_size=N,
                  **sampling_kwargs,
              )
          samples = self.decode_first_stage(samples)
          log["samples"] = samples
      return log
  
imports: import math
  from contextlib import contextmanager
  from typing import Any, Dict, List, Optional, Tuple, Union
  import pytorch_lightning
  import torch
  from omegaconf import ListConfig, OmegaConf
  from safetensors.torch import load_file
  from torch.optim.lr_scheduler import LambdaLR
  from ..modules import UNCONDITIONAL_CONFIG
  from ..modules.autoencoding.temporal_ae import VideoDecoder
  from ..modules.diffusionmodules.wrappers import OPENAIUNETWRAPPER
  from ..modules.ema import LitEma
  from ..util import default, disabled_train, get_obj_from_str, instantiate_from_config, log_txt_as_img
label_desc: Use image node_id 17 for calling the DiffusionEngine obj's underlying member method code with example usage: obj.log_images(batch, N, sample, ucg_keys) and returns: log