code: def setup_test_data(self, cfg):
      if hasattr(self, "_test_ds") and self._test_ds is not None:
          consumed_samples = 0
          logging.info(
              f"Setting up test dataloader with len(len(self._test_ds)): {len(self._test_ds)} and consumed samples: {consumed_samples}"
          )
          self._test_dl = torch.utils.data.DataLoader(
              self._test_ds,
              batch_size=self._micro_batch_size,
              num_workers=cfg.num_workers,
              pin_memory=True,
          )
  
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 18 for calling the MegatronControlNet obj's underlying member method code with example usage: obj.setup_test_data(cfg) without return types