input: Write a function to train a machine learning model using Support Vector Classification (SVC) to identify apples from bananas based on their features. The function should take no input and return the output of the classifier's prediction on test data.
  import numpy as np
  from sklearn.svm import SVC
  
  def identify_fruits():
      # Load data
      X = np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]])
      y = np.array([0, 0, 0, 1, 0, 1])
  
      # Initialize classifier
      clf = SVC(kernel='linear')
  
      # Fit data
      clf.fit(X, y)
  
      # Test classifier
      x_test = np.array([[2,2,2], [4,4,4]])
      
      prediction = clf.predict(x_test)
      return prediction
  
  assert np.array_equal(identify_fruits(), np.array([0, 1]))==True
output: def identify_fruits():
      X = np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]])
      y = np.array([0, 0, 0, 1, 0, 1])
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      x_test = np.array([[2,2,2], [4,4,4]])
      prediction = clf.predict(x_test)
      return prediction