input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas. The function will load data, initialize a Support Vector Classifier (SVC) with a linear kernel, fit the classifier on the data, and then test it with new data. The function will return the predictions for the test data.  
  
  def identify_apples_or_bananas():  
      import numpy as np  
      from sklearn.svm import SVC  
    
      # 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]])  
    
      # Return predictions  
      return clf.predict(x_test)  
    
  # Test cases  
  assert list(identify_apples_or_bananas()) == [0, 1]  
  
output: def identify_apples_or_bananas():  
      import numpy as np  
      from sklearn.svm import SVC  
    
      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)