code: def convert_intent_ids_to_intent_names(self, intent_preds):
      # Retrieve intent and slot vocabularies from configuration.
      intent_labels = self.cfg.data_desc.intent_labels
  
      predicted_intents = []
  
      # convert numerical outputs to Intent and Slot labels from the dictionaries
      for intent_num in intent_preds:
          # if intent_num < len(intent_labels):
          predicted_intents.append(intent_labels[int(intent_num)])
          # else:
          #     # should not happen
          #     predicted_intents.append("Unknown Intent")
      return predicted_intents
  
imports: import os
  from typing import Dict, List, Optional
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from nemo.collections.common.losses import AggregatorLoss, CrossEntropyLoss
  from nemo.collections.nlp.data.dialogue.data_processor.assistant_data_processor import DialogueAssistantDataProcessor
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_bert_dataset import DialogueBERTDataset, DialogueIntentSlotInferenceDataset
  from nemo.collections.nlp.data.intent_slot_classification import IntentSlotDataDesc
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueClassificationMetrics
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules.common import SequenceTokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.classes import typecheck
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 24 for calling the IntentSlotClassificationModel obj's underlying member method code with example usage: obj.convert_intent_ids_to_intent_names(intent_preds) and returns: predicted_intents