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_epoch_end(self, outputs, mode, data_cfg):
      # Parent class will handle logging of the loss.
      if isinstance(outputs[0], dict):
          outputs = [outputs]
  
      averaged_loss = []
      averaged_metric = []
      metric_name = (
          self.val_metric_name
          if mode == "validation"
          else self.test_metric_name
      )
      # Log metrics for each provided validation/test dataset.
      for dataloader_idx, output in enumerate(outputs):
          # Expand on_validation_epoch_end from parent class MegatronLMEncoderDecoderModel as it doesnt take arg outputs
          # loss = super().validation_epoch_end([x['loss'] for x in output])
          loss_vals = [x["loss"] for x in output]
          # NOTE: we need to make sure outputs is not empty (this is a workaround for a bug in pytorch lightning (?))
          if len(loss_vals) == 0:
              logging.warning("validation_epoch_end: outputs is empty")
              return
          if parallel_state.is_pipeline_last_stage():
              # only the last pipeline parallel stages return loss
              loss = torch.stack(loss_vals).mean()
          else:
              loss = torch.tensor(0.0).cuda()
  
          # we can only log on one rank if it is rank zero so we broadcast from last rank
          torch.distributed.broadcast(loss, get_last_rank())
          self.log(
              "val_loss",
              loss,
              prog_bar=True,
              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,
          )
          averaged_loss.append(loss)
  
          # Determine the key used to log the loss based on the user provided name of the dataset or the dataloader index.
          loss_log_key = self._determine_log_key(
              data_cfg, dataloader_idx, "loss", mode
          )
          if metric_name != "loss":
              # Determine the key used to log the eval metric based on the user provided name of the dataset or the dataloader index.
              metric_log_key = self._determine_log_key(
                  data_cfg, dataloader_idx, metric_name, mode
              )
              self.log(loss_log_key, loss, batch_size=1)
              metric_object = (
                  self.val_metric[dataloader_idx]
                  if mode == "validation"
                  else self.test_metric[dataloader_idx]
              )
              metric = metric_object.compute()
              if metric_name == "rouge":
                  metric = metric["rouge1_fmeasure"]
              # Handle logging of GLUE/XNLI separately here. XNLI has a separate metric per language.
              if isinstance(metric, dict):
                  # GLUE case:
                  if len(metric) == 1 and "acc" in metric:
                      metric = metric["acc"]
                      self.log(metric_log_key, metric, batch_size=1)
                      logging.info(f"{mode} {metric_name}: {metric}")
                  # XNLI case where the metric dictionary contains the language and the computed metric as values.
                  else:
                      for k, v in metric.items():
                          if k != "acc" and "total" not in k:
                              self.log(
                                  metric_log_key + f"_{k}",
                                  v,
                                  batch_size=1,
                              )
                              logging.info(
                                  f"{mode} {metric_name} lang {k} : {v}"
                              )
                      if metric_name != "rouge":
                          metric = metric["acc"]
              else:
                  self.log(metric_log_key, metric, batch_size=1)
                  logging.info(f"{metric_log_key}: {metric}")
              metric_object.reset()
              averaged_metric.append(metric)
  
          # Write predictions, labels, and inputs to a file for each validation/test dataset.
          if data_cfg.get("write_predictions_to_file", False):
              # Check if the user provided a prefix path to the file(s) they want to write.
              if (
                  not hasattr(data_cfg, "output_file_path_prefix")
                  or data_cfg.output_file_path_prefix is None
              ):
                  raise ValueError(
                      f"Cannot write predictions to file when output_file_path_prefix is not set or present in the yaml config file."
                  )
  
              # Gather the outputs object from all data parallel ranks since we are using the DistributedSampler which splits data across DDPDDP ranks.
              gathered_outputs = [
                  None
                  for _ in range(
                      parallel_state.get_data_parallel_world_size()
                  )
              ]
              torch.distributed.all_gather_object(
                  gathered_outputs,
                  [
                      {
                          "preds": x["preds"],
                          "labels": x["labels"],
                          "categories": x["categories"],
                          "inputs": x["inputs"],
                      }
                      for x in output
                  ],
                  group=parallel_state.get_data_parallel_group(),
              )
  
              # Figure out what the suffix of the file should be.
              filename_log_key = self._determine_log_key(
                  data_cfg, dataloader_idx, None, mode
              )
  
              # Keep a set of ground truths and inputs to write deduplicated predictions. Distributed Sampler may duplicate examples.
              gt_inp_set = set()
              deduplicated_outputs = {
                  "preds": [],
                  "labels": [],
                  "categories": [],
                  "inputs": [],
              }
  
              # PTL models have a self.global_rank attribute and we want to write to disk only on global rank 0.
              if self.global_rank == 0:
                  for rank in range(
                      0, parallel_state.get_data_parallel_world_size()
                  ):
                      for batch in gathered_outputs[rank]:
                          for pred, label, input, category in zip(
                              batch["preds"],
                              batch["labels"],
                              batch["inputs"],
                              batch["categories"],
                          ):
                              gt_inp_set.add(input + label)
                              deduplicated_outputs["preds"].append(pred)
                              deduplicated_outputs["labels"].append(
                                  label
                              )
                              deduplicated_outputs["categories"].append(
                                  category
                              )
                              deduplicated_outputs["inputs"].append(
                                  input
                              )
                  self.write_predictions_to_file(
                      deduplicated_outputs,
                      f"{data_cfg.output_file_path_prefix}_{filename_log_key}",
                  )
              torch.distributed.barrier()
          outputs[dataloader_idx].clear()  # free memory
  
      # Logging of the averaged metrics:
      averaged_loss = sum(averaged_loss) / len(averaged_loss)
      averaged_metric = (
          sum(averaged_metric) / len(averaged_metric)
          if len(averaged_metric) >= 1
          else None
      )
  
      # Handle case where metrics can be nan or inf. This can break checkpoint save/load.
      if averaged_metric is not None and (
          torch.isinf(averaged_metric) or torch.isnan(averaged_metric)
      ):
          app_state = AppState()
          monitor_mode = app_state.checkpoint_callback_params.mode
          assert monitor_mode in ["min", "max"]
          averaged_metric = 0.0 if monitor_mode == "max" else 1e5
  
      if mode == "validation":
          self.log("validation_loss", averaged_loss, batch_size=1)
          if averaged_metric is not None:
              self.log(
                  f"validation_{self.val_metric_name}",
                  averaged_metric,
                  batch_size=1,
              )
      elif mode == "test":
          self.log("test_loss", averaged_loss, batch_size=1)
          if averaged_metric is not None:
              self.log(
                  f"test_{self.test_metric_name}",
                  averaged_metric,
                  batch_size=1,
              )
  
      app_state = AppState()
      if hasattr(self, "_train_ds"):
          _reconfigure_microbatch_calculator(
              rank=app_state.global_rank,
              rampup_batch_size=None,
              global_batch_size=self.cfg.data.train_ds.global_batch_size,
              micro_batch_size=self.cfg.data.train_ds.micro_batch_size,
              data_parallel_size=parallel_state.get_data_parallel_world_size(),
          )
      # When running `trainer.validate()`, the training dataset is not available.
      else:
          logging.warning(
              "No training data found, reconfiguring microbatches based on validation batch sizes."
          )
          _reconfigure_microbatch_calculator(
              rank=app_state.global_rank,
              rampup_batch_size=None,
              global_batch_size=data_cfg.global_batch_size,
              micro_batch_size=data_cfg.micro_batch_size,
              data_parallel_size=parallel_state.get_data_parallel_world_size(),
          )
  
      return averaged_loss, averaged_metric
  
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 15 for calling the MegatronT5SFTModel obj's underlying member method code with example usage: obj.inference_epoch_end(outputs, mode, data_cfg) and returns: averaged_loss,  averaged_metric