code: def plot_privacy_vs_accuracy(
      results: AttackResultsCollection,
      privacy_metrics: Sequence[PrivacyMetric],
  ):
      """Plots privacy vulnerabilities vs accuracy plots.
  
      In case multiple privacy metrics are specified, the plot will feature
      multiple subplots (one subplot per metrics). Multiple model variants
      are supported.
      Args:
        results: AttackResults for the plot
        privacy_metrics: List of enumerated privacy metrics that should be plotted.
  
      Returns:
        A pyplot figure with privacy vs accuracy plots.
  
      """
      _validate_results(results.attack_results_list)
      all_results_df = _calculate_combined_df_with_metadata(
          results.attack_results_list
      )
      return _generate_subplots(
          all_results_df=all_results_df,
          x_axis_metric="Train accuracy",
          figure_title="Privacy vs Utility Analysis",
          privacy_metrics=privacy_metrics,
      )
  
file_path: privacy/tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/privacy_report.py
imports: from typing import Iterable, Sequence
  import matplotlib.pyplot
  import pandas
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResults
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResultsCollection
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResultsDFColumns
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import ENTIRE_DATASET_SLICE_STR
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import PrivacyMetric
label_desc: Use image node_id 2 for calling a global function with example usage: plot_privacy_vs_accuracy(results, privacy_metrics) and returns: _generate_subplots