input: Write a function to develop and test a machine learning algorithm in Python that can identify apples from bananas using a linear support vector classifier (SVC). The function should load the provided data, initialize the classifier, train it on the data, and then predict the labels for given test data. The function should return the predicted labels for the test data.
  
  
  import numpy as np
  from sklearn.svm import SVC
  
  def identify_fruit():
      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]])
      
      return clf.predict(x_test)
  
  assert np.array_equal(identify_fruit(), np.array([0, 1]))
  
output: def identify_fruit():
      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]])
      
      return clf.predict(x_test)