input: Write a function that implements a basic machine learning model in Python that performs binary classification on the given data. The function should take a list of input features and a corresponding list of binary labels, train a LogisticRegression model, and print the accuracy of the model on the training data. If the input lists are empty, the function should return an accuracy of 0.0.
  
  def binary_classification_model(X, y):
      if not X or not y:
          return 0.0
      
      model = linear_model.LogisticRegression()
      model.fit(X, y)
      
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      
      return accuracy
  
  assert binary_classification_model([[0.3, 0.2, 0.6], [0.3, 0.1, 0.4], [0.2, 0.3, 0.4], [0.7, 0.4, 0.2], [0.5, 0.2, 0.1]], [1, 0, 0, 1, 0]) == 1.0
  assert binary_classification_model([], []) == 0.0
output: def binary_classification_model(X, y):
      if not X or not y:
          return 0.0
      
      model = linear_model.LogisticRegression()
      model.fit(X, y)
      
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      
      return accuracy