code: def apply_model(self, x_noisy, t, cond, *args, **kwargs):
      assert isinstance(cond, dict)
      diffusion_model = self.model.diffusion_model
  
      # cond_txt = torch.cat(cond['c_crossattn'], 1) ## Has removed this first dim in the get_input function, same for below hint input
      cond_txt = cond["c_crossattn"]
  
      if cond["c_concat"] is None:
          eps = diffusion_model(
              x=x_noisy,
              timesteps=t,
              context=cond_txt,
              control=None,
              only_mid_control=self.only_mid_control,
          )
      else:
          control = self.control_model(
              x=x_noisy,
              hint=cond["c_concat"],
              timesteps=t,
              context=cond_txt,
          )
          control = [
              c * scale
              for c, scale in zip(control, self.control_scales)
          ]
          eps = diffusion_model(
              x=x_noisy,
              timesteps=t,
              context=cond_txt,
              control=control,
              only_mid_control=self.only_mid_control,
          )
      return eps
  
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 3 for calling the ControlLDM obj's underlying member method code with example usage: obj.apply_model(x_noisy, t, cond) and returns: eps