code: def make_table(
      model_column,
      frac_positives_column,
      mean_pred_value_column,
      hist_column,
      edge_column,
  ):
      columns = [
          "model",
          "fraction_of_positives",
          "mean_predicted_value",
          "hist_dict",
          "edge_dict",
      ]
  
      data = list(
          zip(
              model_column,
              frac_positives_column,
              mean_pred_value_column,
              hist_column,
              edge_column,
          )
      )
  
      return wandb.Table(columns=columns, data=data)
  
file_path: wandb/wandb/sklearn/calculate/calibration_curves.py
imports: from warnings import simplefilter
  import numpy
  import sklearn
  from sklearn import model_selection, naive_bayes
  from sklearn.calibration import CalibratedClassifierCV
  from sklearn.linear_model import LogisticRegression
  import wandb
  from wandb.sklearn import utils
label_desc: Use image node_id 2 for calling a global function with example usage: make_table(model_column, frac_positives_column, mean_pred_value_column, hist_column, edge_column) and returns: wandb