input: Write a function to identify apples (labeled as 0) from bananas (labeled as 1) using a Support Vector Machine classifier with a linear kernel. The function should return the predictions on the provided test data.
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.svm import SVC
  
  def identify_fruit():
      # 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_fruit(), [0, 1])
  assert np.array_equal(identify_fruit(), [0, 1]) == True
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]])
      prediction = clf.predict(x_test)
      return prediction