input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas using a Support Vector Classifier (SVC) with a linear kernel. If no input is provided, the function should load predefined data, train the classifier, and then test it on new data points. It should return the predictions for the test data points.
  assert identify_fruits(None)==[0, 1]
  assert identify_fruits(np.array([[6,6,6], [-2,2,7]]))==[1, 0]
  assert identify_fruits(np.array([[3,3,3], [4,4,4]]))==[0, 1]
  assert identify_fruits(np.array([[5,5,5], [-1,2,5]]))==[1, 0]
  assert identify_fruits(np.array([[1,1,1], [2,3,4]]))==[0, 0]
output: def identify_fruits(data=None):
      from sklearn.svm import SVC
      import numpy as np
      
      if data is None:
          X_train = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]])
          y_train = np.array([0, 0, 0, 1, 1, 1])
          X_test = np.array([[6, 6, 6], [-2, 2, 7]])
      else:
          X_train = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]])
          y_train = np.array([0, 0, 0, 1, 1, 1])
          X_test = data
      
      clf = SVC(kernel='linear')
      clf.fit(X_train, y_train)
      predictions = clf.predict(X_test)
      return predictions