input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. The function should fit the model on the provided input data X and labels y, and then return the accuracy score of the model. If the input data is empty, the function should return 0.
  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([[0.3, 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
  assert binary_classification_model([[0.3, 0.2, 0.6], [0.3, 0.1, 0.4]], [1, 0]) == 1.0
  assert binary_classification_model([[0.3, 0.2, 0.6], [0.3, 0.1, 0.4], [0.2, 0.3, 0.4]], [0, 0, 0]) == 0.0
output: def binary_classification_model(X, y):
      if not X:
          return 0.0
      model = LogisticRegression()
      model.fit(X, y)
      return model.score(X, y)