code: def make_table(
      y_pred_train,
      residuals_train,
      y_pred_test,
      residuals_test,
      train_score_,
      test_score_,
  ):
      y_pred_column, dataset_column, residuals_column = [], [], []
  
      datapoints, max_datapoints_train = 0, 100
      for pred, residual in zip(y_pred_train, residuals_train):
          # add class counts from training set
          y_pred_column.append(pred)
          dataset_column.append("train")
          residuals_column.append(residual)
          datapoints += 1
          if utils.check_against_limit(
              datapoints, "residuals", max_datapoints_train
          ):
              break
  
      datapoints = 0
      for pred, residual in zip(y_pred_test, residuals_test):
          # add class counts from training set
          y_pred_column.append(pred)
          dataset_column.append("test")
          residuals_column.append(residual)
          datapoints += 1
          if utils.check_against_limit(
              datapoints, "residuals", max_datapoints_train
          ):
              break
  
      columns = [
          "dataset",
          "y_pred",
          "residuals",
          "train_score",
          "test_score",
      ]
      data = [
          [
              dataset_column[i],
              y_pred_column[i],
              residuals_column[i],
              train_score_,
              test_score_,
          ]
          for i in range(len(y_pred_column))
      ]
  
      table = wandb.Table(columns=columns, data=data)
  
      return table
  
imports: from warnings import simplefilter
  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(y_pred_train, residuals_train, y_pred_test, residuals_test, train_score_, test_score_) and returns: table