input: def binary_classification_model(data: List[List[float]], labels: List[int]) -> float:
      """ Implement a basic machine learning model in Python that performs binary classification on the given data.
      The model uses logistic regression from the sklearn library. The function returns the accuracy of the model.
      
      >>> data = [[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]]
      >>> labels = [1, 0, 0, 1, 0]
      >>> binary_classification_model(data, labels)
      1.0
      """
output:     from sklearn.linear_model import LogisticRegression
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
      model = LogisticRegression()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)