input: Write a function to implement a machine learning algorithm in Python that identifies apples from bananas using a Support Vector Classifier (SVC). The function should initialize and fit the classifier on the given dataset, then predict the class for a test dataset. If the function is correctly implemented, the test dataset should be classified as [0, 1] where 0 represents apples and 1 represents bananas.
  assert identify_fruits()==[0, 1]
output: def identify_fruits():
      from sklearn import svm
      import numpy as np
      
      # Sample dataset
      X_train = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
      y_train = np.array([0, 0, 1, 1])
      
      # Test dataset
      X_test = np.array([[1.5, 1.5], [3.5, 3.5]])
      
      # Initialize and fit the SVC
      clf = svm.SVC(kernel='linear')
      clf.fit(X_train, y_train)
      
      # Predict the class for the test dataset
      predictions = clf.predict(X_test)
      
      return list(predictions)