input: def binary_classification(data: List[List[float]], labels: List[int]) -> float:
      """ Implement a basic machine learning model that performs binary classification on the given data. 
      The data consists of lists of float values representing features and each list has its corresponding 
      label (0 or 1). The function trains a Logistic Regression model on the provided data and returns
      the accuracy of the model on the same data.
      >>> binary_classification([[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
      """
output:     from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
  
      model = LogisticRegression()
      model.fit(data, labels)
      predictions = model.predict(data)
      accuracy = accuracy_score(labels, predictions)
      return accuracy