input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas. The function should load data, initialize a classifier, fit the data, and test the classifier using provided test data. If the classifier prediction is correct, return the prediction.
  assert identify_fruit([[2,2,2], [4,4,4]])==[0, 1]
  assert identify_fruit([[1,1,1], [3,3,3]])==[0, 0]
  assert identify_fruit([[5,5,5], [7,7,7]])==[1, 1]
  assert identify_fruit([[0,0,0], [6,6,6]])==[0, 1]
  assert identify_fruit([])==[]
output: def identify_fruit(test_data):
      if not test_data:
          return []
      import numpy as np
      from sklearn.neighbors import KNeighborsClassifier
      data = np.array([[1, 1, 1], [3, 3, 3], [5, 5, 5], [7, 7, 7]])
      labels = np.array([0, 0, 1, 1])
      classifier = KNeighborsClassifier(n_neighbors=1)
      classifier.fit(data, labels)
      predictions = classifier.predict(test_data)
      return predictions.tolist()