input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. The model should use a logistic regression algorithm from the `sklearn` library and print the accuracy of the model on the training data. If the provided data is empty, the function should return a message indicating that the data is insufficient for training.
  
  
  def binary_classification_model(data, labels):
      if not data or not labels:
          return "Insufficient data for training"
      
      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 f'Model accuracy: {accuracy}'
  
  # Test cases
  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]) == 'Model accuracy: 1.0'
  assert binary_classification_model([], []) == "Insufficient data for training"
  assert binary_classification_model([[0.1, 0.2, 0.3]], [1]) == 'Model accuracy: 1.0'
  
output: def binary_classification_model(data, labels):
      if not data or not labels:
          return "Insufficient data for training"
      
      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 f'Model accuracy: {accuracy}'