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 Logistic Regression model from the sklearn library and print the accuracy of the model on the provided dataset.
  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])==None
  assert binary_classification_model([
   [0.1, 0.1, 0.1],
   [0.2, 0.2, 0.2],
   [0.3, 0.3, 0.3],
   [0.4, 0.4, 0.4],
   [0.5, 0.5, 0.5]], [0, 1, 1, 0, 1])==None
output: def binary_classification_model(X, y):
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
      
      model = LogisticRegression()
      model.fit(X, y)
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      print(accuracy)