input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas using a linear Support Vector Classifier (SVC). The function should load the provided data, initialize the classifier, fit the data, and then predict the class of the test data. If the function is successful, it should print the predictions for the given test data.
  
  assert identify_fruits() == [0, 1]
output: def identify_fruits():
      import numpy as np
      from sklearn.svm import SVC
  
      # Sample data
      X_train = np.array([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8]])
      y_train = np.array([0, 0, 0, 0, 1, 1, 1, 1])
      X_test = np.array([[1.5, 1.5], [6.5, 6.5]])
  
      # Initialize the classifier
      clf = SVC(kernel='linear')
  
      # Fit the data
      clf.fit(X_train, y_train)
  
      # Predict the class of the test data
      predictions = clf.predict(X_test)
  
      return list(predictions)