input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas. The function should initialize a Support Vector Classifier with a linear kernel, fit the data, and then predict the class of the given test data. If the given test data is classified as 0, it means it represents an apple, otherwise, it represents a banana. The function should return an array of predictions for the test data.
  assert identify_fruit([]) == np.array([])
  assert identify_fruit([[2,2,2]]) == np.array([0])
  assert identify_fruit([[4,4,4]]) == np.array([1])
output: def identify_fruit(test_data):
      import numpy as np
      from sklearn.svm import SVC
      X_train = np.array([[1, 1, 1], [3, 3, 3]])
      y_train = np.array([0, 1])
      clf = SVC(kernel='linear')
      clf.fit(X_train, y_train)
      predictions = clf.predict(test_data)
      return predictions