input: def binary_classification_model(data: List[List[float]], labels: List[int]) -> float:
      """ Implements a basic machine learning model using logistic regression to perform 
      binary classification on the given data. The model is trained on the input data and 
      labels, and the accuracy of the model is calculated and returned.
      >>> 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
      """
      from sklearn import linear_model
      from sklearn.metrics import accuracy_score
  
      model = linear_model.LogisticRegression()
      model.fit(data, labels)
  
      y_pred = model.predict(data)
      accuracy = accuracy_score(labels, y_pred)
  
      return accuracy
output:     from sklearn import linear_model
      from sklearn.metrics import accuracy_score
  
      model = linear_model.LogisticRegression()
      model.fit(data, labels)
  
      y_pred = model.predict(data)
      accuracy = accuracy_score(labels, y_pred)
  
      return accuracy