code: def get_input(self, batch, k, bs=None, *args, **kwargs):
      x, c = super().get_input(
          batch, self.first_stage_key, *args, **kwargs
      )
      control = batch[self.control_key]
      if bs is not None:
          control = control[:bs]
      control = control.to(torch.cuda.current_device())
      if self.channels_last:
          control = control.permute(0, 3, 1, 2).to(non_blocking=True)
      else:
          control = einops.rearrange(control, "b h w c -> b c h w")
          control = control.to(
              memory_format=torch.contiguous_format
          ).float()
      return x, dict(c_crossattn=c, c_concat=control)
  
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 2 for calling the ControlLDM obj's underlying member method code with example usage: obj.get_input(batch, k, bs) and returns: x,  dict