input: def identify_apples_from_bananas():
      """Develop a machine learning algorithm in Python to identify apples from bananas.
      This function initializes a linear SVM classifier, trains it on provided data, and 
      predicts whether a new set of data points are apples or bananas.
      >>> identify_apples_from_bananas()
      [0 1]
      """
output:     from sklearn import svm
      from sklearn.datasets import make_classification
      from sklearn.model_selection import train_test_split
  
      # Generate synthetic data
      X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_clusters_per_class=1, random_state=42)
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Initialize a linear SVM classifier
      clf = svm.SVC(kernel='linear')
  
      # Train the classifier
      clf.fit(X_train, y_train)
  
      # Predict on the test set
      predictions = clf.predict(X_test)
  
      return predictions