code: def on_validation_epoch_end(self):
      torch.distributed.all_reduce(
          self.validation_metrics, op=torch.distributed.ReduceOp.SUM
      )
      accuracy = (
          self.validation_metrics[0] + self.validation_metrics[1]
      ) / self.validation_metrics.sum()
      self.validation_metrics = None
  
      averaged_metrics = 0
      if parallel_state.is_pipeline_last_stage():
          averaged_metrics = torch.stack(
              self.validation_step_outputs
          ).mean()
          torch.distributed.broadcast(averaged_metrics, get_last_rank())
      self.log(
          "val_loss",
          averaged_metrics,
          prog_bar=True,
          rank_zero_only=True,
          batch_size=1,
      )
      self.log(
          "accuracy",
          accuracy,
          prog_bar=True,
          rank_zero_only=True,
          batch_size=1,
      )
  
      logging.info(f"Current evaluation accuracy: {accuracy}")
  
      return averaged_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 10 for calling the MegatronContentFilteringModel obj's underlying member method code with example usage: obj.on_validation_epoch_end() and returns: averaged_metrics