code: def training_step(self, dataloader_iter, batch_idx):
      self._optimizer.zero_grad()
  
      loss_mean, _ = self.fwd_bwd_step(
          dataloader_iter, batch_idx, forward_only=False
      )
  
      if self.megatron_amp_O2:
          self._optimizer.allreduce_main_grads()
      else:
          self.allreduce_gradients()
  
      torch.distributed.broadcast(loss_mean, get_last_rank())
      if self.cfg.precision == 16:
          loss_scale = self.trainer.precision_plugin.scaler._scale
          if loss_scale is not None:
              self.log(
                  "loss_scale", loss_scale, batch_size=1, prog_bar=True
              )
  
      self.log(
          "reduced_train_loss",
          loss_mean,
          prog_bar=True,
          rank_zero_only=True,
          batch_size=1,
      )
      lr = self._optimizer.param_groups[0]["lr"]
      self.log(
          "lr", lr, rank_zero_only=True, batch_size=1, prog_bar=True
      )
      self.log(
          "global_step",
          self.trainer.global_step + 1,
          prog_bar=True,
          rank_zero_only=True,
          batch_size=1,
      )
      self.log(
          "consumed_samples",
          self.compute_consumed_samples(
              self.trainer.global_step + 1 - self.init_global_step
          ),
          prog_bar=True,
          rank_zero_only=True,
          batch_size=1,
      )
  
      return loss_mean
  
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 8 for calling the MegatronContentFilteringModel obj's underlying member method code with example usage: obj.training_step(dataloader_iter, batch_idx) and returns: loss_mean