input: Write a function in Python to develop a machine learning algorithm that can identify apples from bananas using a Support Vector Machine (SVM) with a linear kernel. The function should load the data, initialize an SVM classifier, fit the classifier with the data, and then test the classifier on new data, printing the predictions. The function should return the predictions as a list.
  
  def identify_fruits():
      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]])
      
      prediction = clf.predict(x_test)
      print(prediction)
      return list(prediction)
  
  assert identify_fruits() == [0, 0]
  assert identify_fruits() == [1, 1]
  assert identify_fruits() == [0, 1]
  assert identify_fruits() == [1, 0]
  assert identify_fruits() == [0, 0]
output: 
  def identify_fruits():
      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]])
      
      prediction = clf.predict(x_test)
      print(prediction)
      return list(prediction)
  