input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas. The function should load a dataset, initialize a classifier, fit the data, and test the classifier. Return the predictions for the test data.
  
  from sklearn.svm import SVC
  import numpy as np
  
  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]])
  
      # Return predictions
      return clf.predict(x_test)
  
  assert np.array_equal(identify_fruits(), np.array([0, 1]))
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]])
      return clf.predict(x_test)