input: Write a python function to train an SVM classifier using the given dataset and make a prediction for new data points.
  assert train_and_predict_svm([1, 0, 1]) == 'orange'
  assert train_and_predict_svm([0, 1, 0]) == 'pear'
  assert train_and_predict_svm([0, 0, 1]) == 'lemon'
output: def train_and_predict_svm(data_point):
      from sklearn import svm
      import numpy as np
  
      # Dataset and labels
      X = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 1], [0, 1, 1]])
      y = np.array(['apple', 'pear', 'lemon', 'orange', 'banana'])
  
      # Create and train the SVM classifier
      clf = svm.SVC()
      clf.fit(X, y)
  
      # Make a prediction
      prediction = clf.predict([data_point])
  
      return prediction[0]