input: Write a python function to train and evaluate a decision tree classifier on the Iris dataset.
  assert round(train_and_evaluate_decision_tree(0.2, 0), 2) == 1.0
  assert round(train_and_evaluate_decision_tree(0.3, 42), 2) >= 0.93
  assert round(train_and_evaluate_decision_tree(0.25, 7), 2) >= 0.95
output: 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
  
  def train_and_evaluate_decision_tree(test_size, random_state):
      # 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=test_size, random_state=random_state)
  
      # Initialize the Decision Tree Classifier
      clf = DecisionTreeClassifier(random_state=random_state)
  
      # Train the classifier
      clf.fit(X_train, y_train)
  
      # Predict the labels for the test set
      y_pred = clf.predict(X_test)
  
      # Calculate the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy