code: def eval_epoch_end(self, outputs, mode="val"):
      generated_field = []
      ground_truth_field = []
      inputs = []
      for output in outputs:
          generated_field += output["generated_field"]
          ground_truth_field += output["ground_truth_field"]
          inputs += output["input"]
  
      with_slots = self.cfg.dataset.target_template == "with_slots"
  
      (
          generated_labels,
          generated_slots,
      ) = DialogueClassificationMetrics.split_label_and_slots(
          generated_field, with_slots=with_slots
      )
      (
          ground_truth_labels,
          ground_truth_slots,
      ) = DialogueClassificationMetrics.split_label_and_slots(
          ground_truth_field, with_slots=with_slots
      )
  
      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",
      )
  
      DialogueClassificationMetrics.save_predictions(
          filename,
          generated_labels,
          generated_slots,
          ground_truth_labels,
          ground_truth_slots,
          generated_field,
          ground_truth_field,
          inputs,
      )
  
      label_acc = np.mean(
          [
              int(generated_labels[i] == ground_truth_labels[i])
              for i in range(len(generated_labels))
          ]
      )
  
      generated_field_ids = torch.tensor(
          [self.label_to_ids[label] for label in generated_labels],
          dtype=int,
      ).to(self.classification_report.device)
  
      ground_truth_field_ids = torch.tensor(
          [self.label_to_ids[label] for label in ground_truth_labels],
          dtype=int,
      ).to(self.classification_report.device)
  
      tp, fn, fp, _ = self.classification_report(
          generated_field_ids, ground_truth_field_ids
      )
  
      (
          precision,
          recall,
          f1,
          report,
      ) = self.classification_report.compute()
      self.classification_report.reset()
  
      (
          slot_precision,
          slot_recall,
          slot_f1,
          slot_joint_goal_accuracy,
      ) = DialogueClassificationMetrics.get_slot_filling_metrics(
          generated_slots, ground_truth_slots
      )
  
      logging.info(report)
  
      self.log("{}_precision".format(self.cfg.dataset.field), precision)
      self.log("{}_f1".format(self.cfg.dataset.field), f1)
      self.log("{}_recall".format(self.cfg.dataset.field), recall)
      self.log(
          "{}_{}_accuracy".format(mode, self.cfg.dataset.field),
          label_acc * 100,
      )
      self.log("slot_precision", slot_precision)
      self.log("slot_recall", slot_recall)
      self.log("slot_f1", slot_f1)
      self.log("slot_joint_goal_accuracy", slot_joint_goal_accuracy)
  
      if mode == "val":
          self.epoch_number += 1
          if self.cfg.save_model:
              filename = "{}/epoch-{}-model.bin".format(
                  self.cfg.dataset.dialogues_example_dir,
                  self.epoch_number,
              )
              torch.save(self.language_model.state_dict(), filename)
  
imports: import collections
  import copy
  import os
  import random
  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 import DialogueGPTClassificationDataset, DialogueSGDDataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.assistant_data_processor import DialogueAssistantDataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.design_data_processor import DialogueDesignDataProcessor
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueClassificationMetrics
  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.collections.nlp.modules.common import VirtualPromptSource, VirtualPromptStyle
  from nemo.collections.nlp.modules.common.text_generation_utils import get_default_sampling_params, megatron_gpt_generate
  from nemo.collections.nlp.modules.common.transformer.text_generation import LengthParam
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 7 for calling the DialogueGPTClassificationModel obj's underlying member method code with example usage: obj.eval_epoch_end(outputs, mode) without return types