input: Write a python function to train and test a DecisionTreeClassifier using given dataset and return its accuracy.
  assert round(train_test_decision_tree_classifier(df, label), 2) == 0.33
output: def train_test_decision_tree_classifier(df, label):
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
      
      X = df.drop(label, axis=1)
      y = df[label]
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
      
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
      
      y_pred = clf.predict(X_test)
      
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy