input: Write a function to create a decision tree classifier in Python that predicts a fourth feature from three other features (A, B, and C). If the dataset is successfully loaded and the classifier is trained, return True; otherwise, return False. If the dataset is empty, return False.
  assert create_decision_tree_classifier('data.csv')==True
  assert create_decision_tree_classifier('empty_data.csv')==False
output: def create_decision_tree_classifier(filename):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
      
      try:
          data = pd.read_csv(filename)
          if data.empty:
              return False
          
          X = data[['A', 'B', 'C']]
          y = data['D']
          
          X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
          
          clf = DecisionTreeClassifier()
          clf.fit(X_train, y_train)
          
          return True
      except:
          return False