input: Write a function to identify whether given instances are apples or bananas using a machine learning algorithm implemented in Python. Use a Support Vector Classifier (SVC) with a linear kernel. The function should load data, initialize the classifier, fit the data, and then test it against new instances. Finally, it should return the predicted classes for the test instances.
  
  def identify_fruits(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
  
  assert np.array_equal(identify_fruits(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]])), np.array([0, 1]))
  assert np.array_equal(identify_fruits(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([[1,1,1], [6,6,6]])), np.array([0, 1]))
output: def identify_fruits(X, y, x_test):
      import numpy as np
      from sklearn.svm import SVC
      
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      prediction = clf.predict(x_test)
      return prediction