code: def calibration_plot(
      fraction_positives,
      mean_predicted_values,
      algorithm_names=None,
      class_name=None,
      filename=None,
      callbacks=None,
  ):
      assert len(fraction_positives) == len(mean_predicted_values)
  
      sns.set_style("whitegrid")
  
      colors = plt.get_cmap("tab10").colors
  
      num_algorithms = len(fraction_positives)
  
      plt.figure(figsize=(9, 9))
      plt.grid(which="both")
      plt.grid(which="minor", alpha=0.5)
      plt.grid(which="major", alpha=0.75)
  
      plt.plot([0, 1], [0, 1], "k:", label="Perfectly calibrated")
  
      for i in range(num_algorithms):
          # ax1.plot(mean_predicted_values[i], fraction_positives[i],
          #         label=algorithms[i] if algorithm_names is not None and i < len(algorithms) else '')
  
          # sns.tsplot(mean_predicted_values[i], fraction_positives[i], ax=ax1, color=colors[i])
  
          assert len(mean_predicted_values[i]) == len(
              fraction_positives[i]
          )
          order = min(3, len(mean_predicted_values[i]) - 1)
  
          sns.regplot(
              x=mean_predicted_values[i],
              y=fraction_positives[i],
              order=order,
              x_estimator=np.mean,
              color=colors[i],
              marker="o",
              scatter_kws={"s": 40},
              label=algorithm_names[i]
              if algorithm_names is not None
              and i < len(algorithm_names)
              else f"Model {i}",
          )
  
      ticks = np.linspace(0.0, 1.0, num=11)
      plt.xlim([-0.05, 1.05])
      plt.xticks(ticks)
      plt.xlabel("Predicted probability")
      plt.ylabel("Observed probability")
      plt.ylim([-0.05, 1.05])
      plt.yticks(ticks)
      plt.legend(loc="lower right")
      if class_name is not None:
          plt.title(f"{class_name}: Calibration (reliability curve)")
      else:
          plt.title("Calibration (reliability curve)")
  
      plt.tight_layout()
      visualize_callbacks(callbacks, plt.gcf())
      if filename:
          plt.savefig(filename)
      else:
          plt.show()
  
imports: import copy
  import logging
  from collections import Counter
  from sys import platform
  from typing import Dict, List
  import numpy
  import pandas
  import ptitprince
  from packaging import version
  from ludwig.constants import SPACE, TRAINING, VALIDATION
label_desc: Use image node_id 16 for calling a global function with example usage: calibration_plot(fraction_positives, mean_predicted_values, algorithm_names, class_name, filename, callbacks) without return types