code: def make_columns(class_ids, counts_train, counts_test):
      class_column, dataset_column, count_column = [], [], []
  
      for i in range(len(class_ids)):
          # add class counts from training set
          class_column.append(class_ids[i])
          dataset_column.append("train")
          count_column.append(counts_train[i])
          # add class counts from test set
          class_column.append(class_ids[i])
          dataset_column.append("test")
          count_column.append(counts_test[i])
  
          if utils.check_against_limit(
              i,
              "class_proportions",
              utils.chart_limit,
          ):
              break
  
      return class_column, dataset_column, count_column
  
file_path: wandb/wandb/sklearn/calculate/class_proportions.py
imports: from warnings import simplefilter
  import numpy
  from sklearn.utils.multiclass import unique_labels
  import wandb
  from wandb.sklearn import utils
label_desc: Use image node_id 3 for calling a global function with example usage: make_columns(class_ids, counts_train, counts_test) and returns: class_column,  dataset_column,  count_column