code: def add_unseen_labels(
      train_label_dict: Dict[LabelType, int],
      test_label_list: List[LabelType],
  ) -> Dict[LabelType, int]:
      """
      Merge test set labels that are not seen in the training data with seen ones.
  
      Parameters
      ----------
      train_label_dict : Dict[:class:`skll.types.LabelType`, int]
          Dictionary mapping training set class labels to class indices.
      test_label_list : List[:class:`skll.types.LabelType`]
          List containing labels in the test set.
  
      Returns
      -------
      Dict[:class:`skll.types.LabelType`, int]
          Dictionary mapping merged labels from both the training and test sets
          to indices.
      """
      # get the list of labels that were in the training set
      train_label_list = list(train_label_dict.keys())
  
      # identify any unseen labels in the test set
      unseen_test_label_list = [
          label
          for label in test_label_list
          if label not in train_label_list
      ]
  
      # create a new dictionary for these unseen labels with label indices
      # for them starting _after_ those for the training set labels
      unseen_label_dict = {
          label: i
          for i, label in enumerate(
              unseen_test_label_list, start=len(train_label_list)
          )
      }
  
      # combine the train label dictionary with this unseen label one & return
      train_and_test_label_dict = train_label_dict.copy()
      train_and_test_label_dict.update(unseen_label_dict)
      return train_and_test_label_dict
  
imports: from __future__ import annotations
  import inspect
  import logging
  import sys
  import time
  from collections import Counter, defaultdict
  from csv import DictWriter, excel_tab
  from functools import wraps
  from importlib import import_module
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
  import joblib
  import numpy
  import scipy.sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_selection import SelectKBest
  from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_fscore_support
  from sklearn.model_selection import KFold, LeaveOneGroupOut, ShuffleSplit, StratifiedKFold
  from skll.data import FeatureSet
  from skll.metrics import _CUSTOM_METRICS, _PREDEFINED_CUSTOM_METRICS, use_score_func
  from skll.types import ComputeEvalMetricsResults, ConfusionMatrix, FeaturesetIterator, FoldMapping, IdType, IndexIterator, LabelType, PathOrStr, SparseFeatureMatrix
  from skll.utils.constants import CLASSIFICATION_ONLY_METRICS, CORRELATION_METRICS, REGRESSION_ONLY_METRICS, UNWEIGHTED_KAPPA_METRICS, WEIGHTED_KAPPA_METRICS
  from skll.version import VERSION
label_desc: Use image node_id 1 for calling a global function with example usage: add_unseen_labels(train_label_dict, test_label_list) and returns: train_and_test_label_dict