code: def training_step(self, dataloader_iter, batch_idx):
      # we zero grads here because we also call backward in the megatron-core fwd/bwd functions
      self._optimizer.zero_grad()
      batch = next(dataloader_iter)
      loss_mean = self.fwd_bwd_step(
          itertools.chain([batch]), batch_idx, forward_only=False
      )
      self.allreduce_gradients()
  
      ## logging
      # we can only log on one rank if it is rank zero so we broadcast from last rank
      # we can avoid this broadcast by updating the PTL log function to accept specific ranks
      torch.distributed.broadcast(loss_mean, get_last_rank())
  
      if self.torch_dtype == torch.float16:
          loss_scale = self.trainer.precision_plugin.scaler._scale
          if loss_scale is not None:
              self.log("loss_scale", loss_scale, batch_size=1)
  
      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)
      self.log(
          "global_step",
          self.trainer.global_step,
          prog_bar=True,
          rank_zero_only=True,
          batch_size=1,
      )
  
      # Need to make sure the frozen model param learning rate stays 0.0
      # so forceing lr to be 0.0 for gpt layers before param update
      return loss_mean
  
imports: import itertools
  import os
  import torch
  from omegaconf.dictconfig import DictConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.common.parts.adapter_modules import LinearAdapterConfig
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_prompt_learning_model import MegatronGPTPromptLearningModel
  from nemo.collections.nlp.modules.common import VirtualPromptStyle
  from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import AdapterName, InfusedAdapterConfig, MLPInfusedAdapterConfig, ParallelLinearAdapterConfig
  from nemo.collections.nlp.modules.common.megatron.utils import average_losses_across_data_parallel_group
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.core.classes.mixins import adapter_mixins
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 10 for calling the MegatronGPTBaseAdapterModel obj's underlying member method code with example usage: obj.training_step(dataloader_iter, batch_idx) and returns: loss_mean