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 write_predictions_to_file(self, outputs, output_file_path_prefix):
      with open(
          output_file_path_prefix + "_inputs_preds_labels.jsonl", "w"
      ) as f_json:
          assert (
              len(outputs["inputs"])
              == len(outputs["preds"])
              == len(outputs["labels"])
          )
          for i, p, l in zip(
              outputs["inputs"], outputs["preds"], outputs["labels"]
          ):
              f_json.write(
                  json.dumps({"input": i, "pred": p, "label": l}) + "\n"
              )
  
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 16 for calling the MegatronT5SFTModel obj's underlying member method code with example usage: obj.write_predictions_to_file(outputs, output_file_path_prefix) without return types