input: from typing import List
  
  
  def identify_apples_from_bananas(image_feature: List[List[float]], true_labels: List[int]) -> List[int]:
      """Develop a machine learning algorithm in Python to identify apples from bananas.
      The input is a list of image features and true labels where 0 represents an apple and 1 represents a banana.
      The function trains a Support Vector Classifier (SVC) with a linear kernel and then predicts the category for new images.
      Return the predicted labels for the new images.
      
      >>> identify_apples_from_bananas([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]], [0, 0, 0, 1, 0, 1])
      [0, 1]
      """
output:     from sklearn.svm import SVC
      from sklearn.model_selection import train_test_split
  
      X_train, X_test, y_train, y_test = train_test_split(image_feature, true_labels, test_size=0.5, random_state=42)
  
      svc = SVC(kernel='linear')
      svc.fit(X_train, y_train)
  
      return svc.predict(X_test)