code: def fwd_bwd_step(self, dataloader_iter, batch_idx, forward_only):
      tensor_shape = None  # Placeholder
  
      # handle asynchronous grad reduction
      no_sync_func = None
      if not forward_only and self.with_distributed_adam:
          no_sync_func = partial(
              self._optimizer.no_sync,
              greedy_grad_copy=self.megatron_amp_O2,
          )
  
      # pipeline schedules will get these from self.model.config
      for module in self.get_module_list():
          module.config.no_sync_func = no_sync_func
  
      # run forward and backwards passes for an entire global batch
      # we do this inside training_step to support pipeline parallelism
      fwd_bwd_function = get_forward_backward_func()
  
      losses_reduced_per_micro_batch = fwd_bwd_function(
          forward_step_func=self.get_forward_output_and_loss_func(),
          data_iterator=dataloader_iter,
          model=self.model,
          num_microbatches=get_num_microbatches(),
          forward_only=forward_only,
          seq_length=None,
          micro_batch_size=self.cfg.micro_batch_size,
      )
  
      # losses_reduced_per_micro_batch is a list of dictionaries
      # [{"loss": 0.1}, {"loss": 0.2}, ...] which are from gradient accumulation steps
      # only the last stages of the pipeline return losses
      loss_dict = {}
      if losses_reduced_per_micro_batch:
          if (not forward_only) or self.cfg.data.get(
              "validation_drop_last", True
          ):
              # average loss across micro batches
              for key in losses_reduced_per_micro_batch[0]:
                  loss_tensors_list = [
                      loss_reduced[key]
                      for loss_reduced in losses_reduced_per_micro_batch
                  ]
                  loss_tensor = torch.stack(loss_tensors_list)
                  loss_dict[key] = loss_tensor.mean()
              loss_mean = loss_dict["train/loss"]
          else:
              raise NotImplementedError(
                  "Losses of micro batches sizes must be uniform!"
              )
      else:
          if forward_only:
              loss_mean = []
          else:
              loss_mean = torch.tensor(
                  0.0, device=torch.cuda.current_device()
              )
  
      return loss_mean, loss_dict
  
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 6 for calling the MegatronControlNet obj's underlying member method code with example usage: obj.fwd_bwd_step(dataloader_iter, batch_idx, forward_only) and returns: loss_mean,  loss_dict