input: Write a function that develops a machine learning algorithm in Python to identify apples from bananas using a Support Vector Classifier (SVC) with a linear kernel. The function should load the provided data, initialize the classifier, fit the data, and predict the class for given test samples. The function should return the predictions for the test samples.
  
  assert identify_fruits([[2,2,2], [4,4,4]]) == [0, 1]
  assert identify_fruits([[1,1,1], [5,5,5]]) == [0, 1]
  assert identify_fruits([[3,3,3], [2,2,7]]) == [0, 1]
  assert identify_fruits([[6,6,6], [7,7,7]]) == [1, 1]
  assert identify_fruits([[-1,-1,-1], [4,4,7]]) == [0, 1]
output: def identify_fruits(test_samples):
      import numpy as np
      from sklearn.svm import SVC
      
      # Sample training data
      X_train = np.array([[1, 1, 1], [5, 5, 5]])
      y_train = np.array([0, 1])  # 0 for apple, 1 for banana
      
      # Initialize the classifier
      clf = SVC(kernel='linear')
      
      # Fit the data
      clf.fit(X_train, y_train)
      
      # Predict the class for given test samples
      predictions = clf.predict(test_samples)
      
      return predictions