code: def make_table(train, test, train_sizes):
      data = []
      for i in range(len(train)):
          if utils.check_against_limit(
              i,
              "learning_curve",
              utils.chart_limit / 2,
          ):
              break
          train_set = ["train", utils.round_2(train[i]), train_sizes[i]]
          test_set = ["test", utils.round_2(test[i]), train_sizes[i]]
          data.append(train_set)
          data.append(test_set)
  
      table = wandb.Table(
          columns=["dataset", "score", "train_size"], data=data
      )
      return table
  
imports: from warnings import simplefilter
  import numpy
  from sklearn import model_selection
  import wandb
  from wandb.sklearn import utils
label_desc: Use image node_id 2 for calling a global function with example usage: make_table(train, test, train_sizes) and returns: table