code: def get_forward_output_and_loss_func(
      self, with_accuracy: bool = False
  ):
      def loss_fn(prediction: torch.Tensor, target: torch.Tensor):
          loss = F.binary_cross_entropy_with_logits(prediction, target)
          out_dict = {"loss": loss}
  
          if with_accuracy:
              accuracy_components = torch.stack(
                  [
                      ((prediction > 0) & (target == 1.0)).sum(),  # tp
                      ((prediction < 0) & (target == 0.0)).sum(),  # tn
                      ((prediction > 0) & (target == 0.0)).sum(),  # fp
                      ((prediction < 0) & (target == 1.0)).sum(),  # fn
                  ]
              )
              out_dict["accuracy"] = accuracy_components
  
          return loss, out_dict
  
      def forward_step(dataloader_iter, model):
          images, labels = next(dataloader_iter)
  
          if (
              parallel_state.get_pipeline_model_parallel_world_size()
              == 1
              or parallel_state.is_pipeline_first_stage()
          ):
              images = images.cuda(non_blocking=True)
              labels = labels.cuda(non_blocking=True)
          else:
              images, labels = None, None
  
          classification = model(
              images,
              mlp_factor=self.mlp_factor,
              emb_factor=self.emb_factor,
          )
  
          return classification.squeeze(-1), functools.partial(
              loss_fn, target=labels.float()
          )
  
      return forward_step
  
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 5 for calling the MegatronContentFilteringModel obj's underlying member method code with example usage: obj.get_forward_output_and_loss_func(with_accuracy) and returns: forward_step, loss,  out_dict, classification,  functools