code: def get_forward_output_and_loss_func(self):
      def process_batch(batch):
          """Prepares the global batch for apex fwd/bwd functions.
          Global batch is a list of micro batches.
          """
          # noise_map, condition
          batch[self.cfg.first_stage_key] = batch[
              self.cfg.first_stage_key
          ].cuda(non_blocking=True)
          if isinstance(batch[self.cfg.cond_stage_key], torch.Tensor):
              # in the case of precached text embeddings, cond_stage is also a tensor
              batch[self.cfg.cond_stage_key] = batch[
                  self.cfg.cond_stage_key
              ].cuda(non_blocking=True)
  
          # SD has more dedicated structure for encoding, so we enable autocasting here as well
          with torch.cuda.amp.autocast(
              self.autocast_dtype in (torch.half, torch.bfloat16),
              dtype=self.autocast_dtype,
          ):
              x, c = self.model.get_input(
                  batch, self.cfg.first_stage_key
              )
  
          if not isinstance(c, dict):
              return [x, c]
  
          if len(self.conditioning_keys) == 0:
              self.conditioning_keys = list(c.keys())
          c_list = [c[key] for key in self.conditioning_keys]
          return [x, *c_list]
  
      def fwd_output_and_loss_func(dataloader_iter, model):
          batch = next(dataloader_iter)
          batch = process_batch(batch)
          batch = [x.cuda(non_blocking=True) for x in batch]
          if len(self.conditioning_keys) == 0:
              x, c = batch
          else:
              x = batch[0]
              c = {}
              for idx, key in enumerate(self.conditioning_keys):
                  c[key] = batch[1 + idx]
          loss, loss_dict = model(x, c)
  
          def dummy(output_tensor):
              return loss, loss_dict
  
          # output_tensor, and a function to convert output_tensor to loss + loss_dict
          return loss, dummy
  
      return fwd_output_and_loss_func
  
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 11 for calling the MegatronControlNet obj's underlying member method code with example usage: obj.get_forward_output_and_loss_func() and returns: fwd_output_and_loss_func, list, loss,  dummy, list, loss,  loss_dict