input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. The function should use the `LogisticRegression` model from `sklearn.linear_model` and calculate the accuracy using `accuracy_score` from `sklearn.metrics`.
  assert implementation([[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 implementation([[0.3, 0.2, 0.6], [0.3, 0.1, 0.4], [0.2, 0.3, 0.4], [0.7, 0.4, 0.2]], [1, 0, 0, 1])=='Model accuracy: 1.0'
  assert implementation([[0.3, 0.2, 0.6], [0.3, 0.1, 0.4], [0.2, 0.3, 0.4]], [1, 0, 1])=='Model accuracy: 0.6666666666666666'
  assert implementation([], [])=='Model accuracy: None'
  assert implementation([[0.3, 0.2, 0.6]], [1])=='Model accuracy: 1.0'
output: 
  def implementation(data, labels):
      if not data or not labels:
          return 'Model accuracy: None'
      model = LogisticRegression()
      model.fit(data, labels)
      predictions = model.predict(data)
      accuracy = accuracy_score(labels, predictions)
      return f'Model accuracy: {accuracy}'
  