input: Write a function to develop a machine learning algorithm in Python that identifies apples from bananas using an SVM classifier. The function should load data, initialize the classifier, fit the data, and make predictions on test data. If no predictions are made, the function should return None.
  assert apples_bananas_classifier(None)==None
  assert apples_bananas_classifier()=="This function does not accept input parameters."
  
output: def apples_bananas_classifier(data=None):
      if data is None:
          return "This function does not accept input parameters."
      
      from sklearn import svm
      from sklearn.model_selection import train_test_split
      import numpy as np
      
      X = np.array([[1, 2], [2, 3], [3, 4], [6, 7], [7, 8], [8, 9]])
      y = np.array([0, 0, 0, 1, 1, 1])
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
      
      clf = svm.SVC()
      clf.fit(X_train, y_train)
      
      if len(X_test) == 0:
          return None
      
      predictions = clf.predict(X_test)
      return predictions