input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas. The function should load data, initialize a Support Vector Classifier (SVC) with a linear kernel, fit the data, and then test the classifier on new data points. If the function is provided with an array of feature vectors for testing, it should return the predicted labels for those test points.
  
  def identify_fruit(X, y, x_test):
      import numpy as np
      from sklearn.svm import SVC
  
      # Initialize classifier
      clf = SVC(kernel='linear')
  
      # Fit data
      clf.fit(X, y)
  
      # Test classifier
      prediction = clf.predict(x_test)
      return prediction
  
  # Test cases
  assert 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]])) == [0, 0]
  assert 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([[2,2,3], [4,3,4]])) == [0, 1]
  assert identify_fruit(np.array([[3,3,3], [4,4,4], [5,5,5], [1,2,3], [2,2,2], [3,4,5]]), np.array([0, 1, 0, 1, 1, 1]), np.array([[1,1,1], [3,4,5]])) == [0, 1]
  assert identify_fruit(np.array([[2,2,2], [4,4,4], [5,5,5], [1,2,3], [2,3,4], [6,7,8]]), np.array([1, 0, 0, 1, 1, 1]), np.array([[1,2,3], [2,3,4], [6,7,8]])) == [1, 1, 1]
  assert identify_fruit(np.array([]), np.array([]), np.array([])) == []
output: def identify_fruit(X, y, x_test):
      import numpy as np
      from sklearn.svm import SVC
  
      # Initialize classifier
      clf = SVC(kernel='linear')
  
      # Fit data
      clf.fit(X, y)
  
      # Test classifier
      prediction = clf.predict(x_test)
      return prediction