code: def log_images(
      self,
      batch,
      N=4,
      n_row=2,
      sample=False,
      ddim_steps=50,
      ddim_eta=0.0,
      return_keys=None,
      quantize_denoised=True,
      inpaint=True,
      plot_denoise_rows=False,
      plot_progressive_rows=True,
      plot_diffusion_rows=False,
      unconditional_guidance_scale=9.0,
      unconditional_guidance_label=None,
      use_ema_scope=True,
      **kwargs,
  ):
      use_ddim = ddim_steps is not None
  
      log = dict()
      batch = next(batch)
      batch["images"] = batch["images"].to(torch.cuda.current_device())
      batch["hint"] = batch["hint"].to(torch.cuda.current_device())
      N = batch["images"].shape[0]
      z, c = self.get_input(batch, self.first_stage_key, bs=N)
      c_cat, c = c["c_concat"][:N], c["c_crossattn"][:N]
      N = min(z.shape[0], N)
      n_row = min(z.shape[0], n_row)
      log["reconstruction"] = self.decode_first_stage(z)
      log["control"] = c_cat * 2.0 - 1.0
      log["conditioning"] = log_txt_as_img(
          (512, 512), batch[self.cond_stage_key], size=16
      )
  
      if plot_diffusion_rows:
          # get diffusion row
          diffusion_row = list()
          z_start = z[:n_row]
          for t in range(self.num_timesteps):
              if (
                  t % self.log_every_t == 0
                  or t == self.num_timesteps - 1
              ):
                  t = repeat(torch.tensor([t]), "1 -> b", b=n_row)
                  t = t.to(self.device).long()
                  noise = torch.randn_like(z_start)
                  z_noisy = self.q_sample(
                      x_start=z_start, t=t, noise=noise
                  )
                  diffusion_row.append(self.decode_first_stage(z_noisy))
  
          diffusion_row = torch.stack(
              diffusion_row
          )  # n_log_step, n_row, C, H, W
          diffusion_grid = rearrange(
              diffusion_row, "n b c h w -> b n c h w"
          )
          diffusion_grid = rearrange(
              diffusion_grid, "b n c h w -> (b n) c h w"
          )
          assert (
              TORCHVISION_AVAILABLE
          ), "Torchvision imports failed but they are required."
          diffusion_grid = make_grid(
              diffusion_grid, nrow=diffusion_row.shape[0]
          )
          log["diffusion_row"] = diffusion_grid
  
      if sample:
          # get denoise row
          samples, z_denoise_row = self.sample_log(
              cond={"c_concat": c_cat, "c_crossattn": c},
              batch_size=N,
              ddim=use_ddim,
              ddim_steps=ddim_steps,
              eta=ddim_eta,
          )
          x_samples = self.decode_first_stage(samples)
          log["samples"] = x_samples
          if plot_denoise_rows:
              denoise_grid = self._get_denoise_row_from_list(
                  z_denoise_row
              )
              log["denoise_row"] = denoise_grid
  
      if unconditional_guidance_scale > 1.0:
          uc_cross = self.get_unconditional_conditioning(N)
          uc_cat = c_cat  # torch.zeros_like(c_cat)
          uc_full = {"c_concat": uc_cat, "c_crossattn": uc_cross}
          samples_cfg, _ = self.sample_log(
              cond={"c_concat": c_cat, "c_crossattn": c},
              batch_size=N,
              ddim=use_ddim,
              ddim_steps=ddim_steps,
              eta=ddim_eta,
              unconditional_guidance_scale=unconditional_guidance_scale,
              unconditional_conditioning=uc_full,
          )
          x_samples_cfg = self.decode_first_stage(samples_cfg)
          log[
              f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"
          ] = x_samples_cfg
  
      return log
  
imports: from typing import Any
  import einops
  import torch
  import torch.nn
  from einops import rearrange, repeat
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from pytorch_lightning.utilities.rank_zero import rank_zero_only
  from torch._inductor import config
  from nemo.collections.multimodal.data.controlnet.controlnet_dataset import build_train_valid_datasets
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import LatentDiffusion
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.ddim import DDIMSampler
  from nemo.collections.multimodal.modules.stable_diffusion.attention import SpatialTransformer
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel import AttentionBlock, Downsample, ResBlock, TimestepEmbedSequential, UNetModel
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import conv_nd, linear, timestep_embedding, zero_module
  from nemo.collections.multimodal.parts.stable_diffusion.utils import exists, log_txt_as_img
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the ControlLDM obj's underlying member method code with example usage: obj.log_images(batch, N, n_row, sample, ddim_steps, ddim_eta, return_keys, quantize_denoised, inpaint, plot_denoise_rows, plot_progressive_rows, plot_diffusion_rows, unconditional_guidance_scale, unconditional_guidance_label, use_ema_scope) and returns: log