input: Write a python function to calculate precision and recall values for a given set of classification labels and scores, then plot the precision-recall curve.
  assert calculate_precision_recall([1, 0, 1, 0, 1, 0, 0, 0, 1, 1], [0.8, 0.6, 0.75, 0.9, 0.7, 0.3, 0.2, 0.1, 0.85, 0.5]) is None
output: def calculate_precision_recall(true_labels, scores):
      from sklearn.metrics import precision_recall_curve
      import matplotlib.pyplot as plt
  
      precision, recall, _ = precision_recall_curve(true_labels, scores)
  
      plt.figure()
      plt.plot(recall, precision, marker='.')
      plt.xlabel('Recall')
      plt.ylabel('Precision')
      plt.title('Precision-Recall Curve')
      plt.show()