code: def get_forward_output_and_loss_func(self):
      def process_batch(batch):
          """Prepares the batch for megatron fwd/bwd functions.
          Global batch is a list of micro batches.
          """
          # Base model and SR models have slightly different batch input:
          # Base model would only require images (64x64),
          # while SR models (both SR256 and SR1024) require low-res image (64x64) and
          # actual (cropped) image (256x256)
          if self.cfg.unet_type == "base":
              x_start = batch["images"]
              # Pass in DUMMY_TENSOR because megatron requires each input to be
              # tensor (not None) with same batch size (first dim)
              x_lowres = DUMMY_TENSOR.repeat(x_start.shape[0])
          elif (
              self.cfg.unet_type == "sr"
              or self.cfg.unet_type == "sr-unet"
          ):
              x_start = batch["images_256"]
              x_lowres = batch["images_64"]
          else:
              raise NotImplemented(
                  f"Unknown UNet type: {self.cfg.unet_type}"
              )
  
          if self.cfg.conditioning.get("online_encoding", False):
              input_text = batch["raw_text"]
              # Encode the text embeddings using text encoder.
              with torch.no_grad():
                  text_embed, text_mask = self.text_encoder.encode(
                      input_text
                  )
          else:
              text_conditioning_key = self.cfg.conditioning.out_key
              text_embed = batch[f"{text_conditioning_key}_embeddings"]
              text_mask = batch[f"{text_conditioning_key}_mask"]
          return [x_start, text_embed, text_mask, x_lowres]
  
      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]
          loss, loss_dict = model(*batch)
  
          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: import itertools
  from datetime import datetime
  from functools import partial
  from typing import Any
  import torch
  from omegaconf import DictConfig, open_dict
  from pytorch_lightning import Trainer
  from nemo.collections.multimodal.data.imagen.imagen_dataset import build_train_valid_datasets
  from nemo.collections.multimodal.models.text_to_image.imagen.precond import ContinousDDPMPrecond, EDMPrecond
  from nemo.collections.multimodal.modules.imagen.diffusionmodules.nets import EfficientUNetModel, UNetModel
  from nemo.collections.multimodal.modules.imagen.encoder.t5encoder import T5Encoder
  from nemo.collections.multimodal.modules.imagen.sampler.sampler import DDPMSampler, EDMSampler
  from nemo.collections.multimodal.parts.imagen.utils import random_dropout
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.core.classes.common import Serialization
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the MegatronImagen 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, loss,  loss_dict