input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. Import necessary libraries, define the input data and labels, train the model using Logistic Regression, and print the model's accuracy.
  
  def binary_classification_model():
      from sklearn import linear_model
      from sklearn.metrics import accuracy_score
      
      X = [[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]]
      y = [1, 0, 0, 1, 0]
      
      model = linear_model.LogisticRegression()
      model.fit(X, y)
      
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      
      print('Model accuracy:', accuracy)
  
  assert binary_classification_model.__code__.co_name=='binary_classification_model'
  assert binary_classification_model.__module__=='__main__'
  assert model.__class__.__name__=='LogisticRegression'
  assert len(y_pred)==5
  assert type(accuracy)==float
output: def binary_classification_model():
      from sklearn import linear_model
      from sklearn.metrics import accuracy_score
      
      X = [[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]]
      y = [1, 0, 0, 1, 0]
      
      model = linear_model.LogisticRegression()
      model.fit(X, y)
      
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      
      print('Model accuracy:', accuracy)