class_docstr: T5 Finetuning model in the same format as MegatronGPTSFTModel 
class_docstr_tok: ["T5","Finetuning","model","in","the","same","format","as","MegatronGPTSFTModel"]
code: def inference_step(
      self, dataloader_iter, batch_idx: int, mode: str, dataloader_idx=0
  ):
      # Check if iterator is exhausted
      dataloader_iter, done = self._val_iterator_done(dataloader_iter)
      if done:
          return
      # Regular finetuning datasets will return a list of dicts for each microbatch.
      # But T0 datasets will return a single dict for the global batch.
      batch = next(dataloader_iter)
      batch_has_lang_information = (
          isinstance(batch, list) and len(batch[0]) == 7
      )
      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)
  
      # NOTE: There could be extra keys in the processed_batch dictionary such as "langs" for XNLI,
      # this will be ignored.
      loss = self.fwd_bwd_step(
          itertools.chain([batch]), batch_idx, forward_only=True
      )
  
      predicted_token_ids, _ = self.decode(
          tokens_enc=batch["text_enc"],
          enc_mask=batch["enc_mask"],
          num_tokens_to_generate=30,
          bos_id=self.tokenizer.pad_id
          if data_cfg.get("replace_bos_with_pad", False)
          else self.tokenizer.bos_id,
      )
  
      # Special ids to text function to handle stripping <eos> and special tokens with sentencepiece tokenizers.
      preds_text = MegatronT5SFTModel.ids_to_text(
          predicted_token_ids, self.tokenizer
      )
      labels_text = MegatronT5SFTModel.ids_to_text(
          batch["labels"], self.tokenizer
      )
      input_text = MegatronT5SFTModel.ids_to_text(
          batch["text_enc"], self.tokenizer
      )
  
      if not batch_has_lang_information:
          categories = [None] * len(preds_text)
      else:
          categories = batch["lang"]
  
      if self.val_metric is not None or self.test_metric is not None:
          metric = (
              self.val_metric[dataloader_idx]
              if mode == "validation"
              else self.test_metric[dataloader_idx]
          )
          assert len(categories) == len(preds_text) == len(labels_text)
          for _, (pred, label, category) in enumerate(
              zip(preds_text, labels_text, categories)
          ):
              # To compute metrics like pearson or spearman correlation, we need to cast the predicted string and labels to floats.
              pred, label = self.cast_for_metric(
                  pred=pred,
                  label=label,
                  metric_name=self.val_metric_name
                  if mode == "validation"
                  else self.test_metric_name,
                  class_labels=data_cfg.metric.get(
                      "class_labels", None
                  ),
                  labels_are_strings=data_cfg.metric.get(
                      "labels_are_strings", False
                  ),
              )
              if batch_has_lang_information:
                  _ = metric(pred, label, category)
              else:
                  _ = metric(pred, label)
  
      outputs = {
          "preds": preds_text,
          "labels": labels_text,
          "categories": categories,
          "inputs": input_text,
      }
  
      if isinstance(loss, dict):
          outputs.update(loss)
      else:
          outputs["loss"] = loss
      if mode == "validation":
          if (
              type(self.trainer.val_dataloaders) == list
              and len(self.trainer.val_dataloaders) > 1
          ):
              self.validation_step_outputs[dataloader_idx].append(
                  outputs
              )
          else:
              self.validation_step_outputs.append(outputs)
      else:
          if (
              type(self.trainer.test_dataloaders) == list
              and len(self.trainer.test_dataloaders) > 1
          ):
              self.test_step_outputs[dataloader_idx].append(outputs)
          else:
              self.test_step_outputs.append(outputs)
      return outputs
  
imports: import itertools
  import json
  from typing import Dict, List
  import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.common.data import ConcatMapDataset
  from nemo.collections.common.metrics import MetricStringToTorchMetric
  from nemo.collections.common.metrics.classification_accuracy import ExactStringPerCategoryMatchMetric
  from nemo.collections.nlp.data.common.sequence_to_sequence_dataset import SequenceToSequenceDataset
  from nemo.collections.nlp.data.language_modeling.megatron.t5_sft_dataset import T5SFTDataset
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model, T5Sentinel
  from nemo.collections.nlp.modules.common.megatron.utils import get_iterator_k_split
  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 12 for calling the MegatronT5SFTModel obj's underlying member method code with example usage: obj.inference_step(dataloader_iter, batch_idx, mode, dataloader_idx) and returns: outputs