code: def print_dataset_info(
      superclasses, subclass_splits, label_map, label_map_sub
  ):
      """
      Obtain a dataframe with information about the
      superclasses/subclasses included in the dataset.
  
      Args:
      superclasses (list): WordNet IDs of superclasses
      subclass_splits (tuple): Tuple entries correspond to the source
                               and target domains respectively. A
                               tuple entry is a list, where each
                               element is a list of subclasses to
                               be included in a given superclass in
                               that domain. If split is None,
                               the second tuple element is empty.
      label_map (dict): Map from (super)class number to superclass name
      label_map_sub (dict):  Map from subclass number to subclass name
                                (equivalent to label map for original dataset)
      Returns:
          dataDf (pandas DataFrame): Columns contain relevant information
                                  about the datast
  
      """
  
      def print_names(class_idx):
          return [
              f'{label_map_sub[r].split(",")[0]} ({r})'
              for r in class_idx
          ]
  
      data = {"superclass": []}
      contains_split = len(subclass_splits[1])
      if contains_split:
          data.update(
              {"subclasses (source)": [], "subclasses (target)": []}
          )
      else:
          data.update({"subclasses": []})
  
      for i, (k, v) in enumerate(label_map.items()):
          data["superclass"].append(f"{v}")
          if contains_split:
              data["subclasses (source)"].append(
                  print_names(subclass_splits[0][i])
              )
              data["subclasses (target)"].append(
                  print_names(subclass_splits[1][i])
              )
          else:
              data["subclasses"].append(
                  print_names(subclass_splits[0][i])
              )
  
      dataDf = pd.DataFrame(data)
      return dataDf
  
imports: import sys, os
  import json
  import numpy
  import pandas
  import urllib
  from collections import OrderedDict, Counter
  import operator
  import networkx
  from ..datasets import DATASETS
label_desc: Use image node_id 2 for calling a global function with example usage: print_dataset_info(superclasses, subclass_splits, label_map, label_map_sub) and returns: dataDf, unknown