code: def eval_epoch_end(self, outputs, mode="val"):
      generated_field = []
      ground_truth_field = []
      inputs = []
      loss = []
  
      for output in outputs:
          generated_field += output["generated_field"]
          ground_truth_field += output["ground_truth_field"]
          inputs += output["input"]
          loss.append(output["loss"].item())
  
      os.makedirs(self.cfg.dataset.dialogues_example_dir, exist_ok=True)
      filename = os.path.join(
          self.cfg.dataset.dialogues_example_dir,
          f"{mode}_predictions_epoch{self.epoch_number}.jsonl",
      )
  
      DialogueGenerationMetrics.save_predictions(
          filename,
          generated_field,
          ground_truth_field,
          inputs,
      )
  
      label_acc = np.mean(
          [
              int(generated_field[i] == ground_truth_field[i])
              for i in range(len(generated_field))
          ]
      )
      precision, recall, f1 = DialogueGenerationMetrics.get_f1(
          generated_field, ground_truth_field
      )
      bleu = DialogueGenerationMetrics.get_bleu(
          generated_field, ground_truth_field
      )
      avg_loss = np.mean(loss)
      ppl = np.exp(avg_loss)
  
      self.log("{}_accuracy".format(mode), label_acc * 100)
      self.log("precision", precision)
      self.log("recall", recall)
      self.log("f1", f1)
      self.log("bleu", bleu)
      self.log("{}_loss".format(mode), avg_loss)
      self.log("{}_ppl".format(mode), ppl)
  
      if mode == "val":
          self.epoch_number += 1
          if self.cfg.save_model:
              filename = (
                  "{}/val_loss-{}-epoch-{}-answer-extender.bin".format(
                      self.cfg.dataset.dialogues_example_dir,
                      avg_loss,
                      self.epoch_number,
                  )
              )
              torch.save(self.language_model.state_dict(), filename)
  
imports: import copy
  import os
  from typing import Dict, Optional, Union
  import numpy
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from transformers import AutoModelWithLMHead
  from nemo.collections.nlp.data.dialogue.data_processor.mellon_qa_data_processor import DialogueMellonQADataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.ms_marco_data_processor import DialogueMSMarcoDataProcessor
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_gpt_generation_dataset import DialogueGPTGenerationDataset
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueGenerationMetrics
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_prompt_learning_model import MegatronGPTPromptLearningModel
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 6 for calling the DialogueGPTGenerationModel obj's underlying member method code with example usage: obj.eval_epoch_end(outputs, mode) without return types