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 setup_eval_data(self, datasets, data_cfg):
      dataloaders = []
      for dataset in datasets:
          eval_dl = self.build_data_loader(
              dataset,
              global_batch_size=self.cfg.data.test_ds.global_batch_size
              if hasattr(self.cfg.data, "test_ds")
              else self.cfg.data.validation_ds.global_batch_size,
              shuffle=data_cfg.shuffle,
              num_workers=data_cfg.num_workers,
              pin_memory=data_cfg.pin_memory,
              drop_last=data_cfg.drop_last,
          )
          dataloaders.append(eval_dl)
      return dataloaders
  
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 23 for calling the MegatronT5SFTModel obj's underlying member method code with example usage: obj.setup_eval_data(datasets, data_cfg) and returns: dataloaders