input: Write a python function to create and visualize a decision tree using given dataset.
  assert create_and_visualize_decision_tree() is not None
output: def create_and_visualize_decision_tree():
      import matplotlib.pyplot as plt
      from sklearn.datasets import load_iris
      from sklearn.tree import DecisionTreeClassifier, plot_tree
  
      # Load the dataset
      iris = load_iris()
      X, y = iris.data, iris.target
  
      # Create and train the decision tree classifier
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
  
      # Visualize the decision tree
      plt.figure(figsize=(20, 10))
      plot_tree(clf, filled=True, feature_names=iris.feature_names, class_names=iris.target_names)
      plt.show()
      return clf