input: Write a python function to split the Iris dataset into train and test sets, train a Decision Tree classifier, make predictions, and calculate the accuracy.
  assert split_train_test_iris()[0] == 0.9736842105263158
  assert split_train_test_iris()[1] == 0.9736842105263158
output: def split_train_test_iris():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
      
      # Load the Iris dataset
      iris = load_iris()
      X, y = iris.data, iris.target
      
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
      
      # Initialize the Decision Tree classifier
      clf = DecisionTreeClassifier(random_state=42)
      
      # Train the classifier
      clf.fit(X_train, y_train)
      
      # Make predictions on the training set
      train_predictions = clf.predict(X_train)
      
      # Make predictions on the testing set
      test_predictions = clf.predict(X_test)
      
      # Calculate the accuracy for both train and test sets
      train_accuracy = accuracy_score(y_train, train_predictions)
      test_accuracy = accuracy_score(y_test, test_predictions)
      
      return train_accuracy, test_accuracy