input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data using Logistic Regression. Return the model's accuracy. If the input data is empty, return 0.
  assert accuracy_score_logistic_regression([[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 accuracy_score_logistic_regression([], [])==0
  assert accuracy_score_logistic_regression([[0.3, 0.2, 0.6], [0.8, 0.1, 0.4], [0.2, 0.3, 0.4]], [1, 0, 0])==0.6666666666666666
output: 
  def accuracy_score_logistic_regression(data, labels):
      if not data:
          return 0
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
      model = LogisticRegression()
      model.fit(data, labels)
      predictions = model.predict(data)
      return accuracy_score(labels, predictions)
  