class_docstr: Megatron GPT Supervised Fine-Tuning
class_docstr_tok: ["Megatron","GPT","Supervised","Fine-Tuning"]
code: def inference_step(
      self, dataloader_iter, batch_idx, mode, dataloader_idx=0
  ):
      # Check if iterator is exhausted
      dataloader_iter, done = self._val_iterator_done(dataloader_iter)
      if done:
          return
      batch = next(dataloader_iter)
      data_cfg = (
          self.cfg.data.validation_ds
          if mode == "validation"
          else self.cfg.data.test_ds
      )
      self._reconfigure_and_process_inference_batch(batch, data_cfg)
      # Meta data from dataset
      metadata = batch.get("metadata", [{}] * len(batch["tokens"]))
      loss = super().validation_step(
          itertools.chain([batch]), batch_idx
      )
  
      if (
          data_cfg.get("write_predictions_to_file", False)
          or data_cfg.metric.name != "loss"
      ):
          # We need _inference_config to get generation params
          # add_BOS and tokens_to_generate are set in dataset
          if self.get_inference_config() is None:
              self.set_inference_config(inference_config={})
          self._inference_config["add_BOS"] = data_cfg.add_bos
          self._inference_config["tokens_to_generate"] = data_cfg.get(
              "tokens_to_generate"
          )
  
          output = self.predict_step(batch, batch_idx, dataloader_idx)
          inputs_text = [
              self.tokenizer.ids_to_text(c.tolist())
              for c in batch["contexts"]
          ]
          labels_text = [
              self.tokenizer.ids_to_text(a.tolist())
              for a in batch["answers"]
          ]
          preds_text = [
              self.tokenizer.ids_to_text(
                  t[l.item() :][: data_cfg.get("tokens_to_generate")]
              )
              for t, l in zip(
                  output["token_ids"], batch["context_lengths"]
              )
          ]
      else:
          inputs_text, labels_text, preds_text = [], [], []
  
      outputs = {
          "loss": loss,
          "preds": preds_text,  # [str]
          "labels": labels_text,  # [str]
          "inputs": inputs_text,  # [str]
          "metadata": metadata,  # [dict]
      }
  
      if mode == "validation":
          if (
              type(self.trainer.val_dataloaders) == list
              and len(self.trainer.val_dataloaders) > 1
          ):
              # super().validation_step appends just loss to self.validation_step_outputs, replace the last appended loss with the outputs dict
              self.validation_step_outputs[dataloader_idx][-1] = outputs
          else:
              # super().validation_step appends just loss to self.validation_step_outputs, replace the last appended loss with the outputs dict
              self.validation_step_outputs[-1] = outputs
      else:
          if (
              type(self.trainer.test_dataloaders) == list
              and len(self.trainer.test_dataloaders) > 1
          ):
              self.test_step_outputs[dataloader_idx][-1] = outputs
          else:
              self.test_step_outputs[-1] = outputs
      return outputs
  
imports: import itertools
  import json
  from functools import partial
  from typing import Any, Optional
  import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.common.metrics import MetricStringToTorchMetric
  from nemo.collections.nlp.data.language_modeling.megatron.base_dataset_utils import get_datasets_weights_and_num_samples
  from nemo.collections.nlp.data.language_modeling.megatron.blendable_dataset import BlendableDataset
  from nemo.collections.nlp.data.language_modeling.megatron.gpt_sft_chat_dataset import GPTSFTChatDataset
  from nemo.collections.nlp.data.language_modeling.megatron.gpt_sft_dataset import GPTSFTDataset, GPTSFTPackedDataset
  from nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers import MegatronPretrainingBatchSampler
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.modules.common.megatron.utils import get_iterator_k_split
  from nemo.collections.nlp.modules.common.text_generation_utils import generate, get_computeprob_response
  from nemo.collections.nlp.parts.mixins.nlp_adapter_mixins import NLPAdapterModelMixin
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.utils import AppState, logging
label_desc: Use image node_id 10 for calling the MegatronGPTSFTModel obj's underlying member method code with example usage: obj.inference_step(dataloader_iter, batch_idx, mode, dataloader_idx) and returns: outputs