code: def fwd_bwd_step(
      self, dataloader_iter, batch_idx: int, forward_only: bool
  ):
      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(
              with_accuracy=forward_only
          ),
          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,
      )
  
      metrics = None
      if losses_reduced_per_micro_batch:
          loss_mean = torch.stack(
              [l["loss"] for l in losses_reduced_per_micro_batch]
          ).mean()
          if forward_only:
              metrics = torch.stack(
                  [
                      l["accuracy"]
                      for l in losses_reduced_per_micro_batch
                  ]
              ).sum(dim=0)
      else:
          loss_mean = 0.0
  
      return loss_mean, metrics
  
imports: import functools
  import itertools
  from typing import List, Optional, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from apex.transformer.pipeline_parallel.utils import get_num_microbatches
  from megatron.core import parallel_state
  from megatron.core.pipeline_parallel.schedules import get_forward_backward_func
  from omegaconf.dictconfig import DictConfig
  from pytorch_lightning.accelerators import CPUAccelerator
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.multimodal.data.clip.clip_dataset import tokenize
  from nemo.collections.multimodal.data.nsfw.nsfw_dataset import build_dataset
  from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import CLIPTextTransformer, CLIPVisionTransformer
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common.megatron.build_model import build_model
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module, MegatronModule
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank, torch_dtype_from_precision
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 7 for calling the MegatronContentFilteringModel obj's underlying member method code with example usage: obj.fwd_bwd_step(dataloader_iter, batch_idx, forward_only) and returns: loss_mean,  metrics