input: 
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.svm import SVC
  
  def identify_fruit(X, y, x_test):
      """ Develop a machine learning algorithm in Python to identify apples from bananas using a Support Vector Classifier (SVC).
      The function should take training data X and corresponding labels y, and test data x_test. It will return the predictions
      made by the classifier on the test data. Given the example, the output should be a list of predictions.
      
      Example:
      for identify_fruit(np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]]),
                         np.array([0, 0, 0, 1, 0, 1]),
                         np.array([[2,2,2], [4,4,4]]))
      should return [0, 1]
      """
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      prediction = clf.predict(x_test)
      return prediction
  
output:     clf = SVC(kernel='linear')
      clf.fit(X, y)
      prediction = clf.predict(x_test)
      return prediction