input: Write a function named `decision_tree_classifier` that takes three features (A, B, C) as inputs and returns the predicted fourth feature using a decision tree classifier. The function should follow the steps of importing the dataset, splitting it into features and labels, fitting the classifier, and then predicting the output.
  
  
  def decision_tree_classifier(A, B, C):
      # Loading the dataset
      dataset = pd.read_csv('data.csv')
      
      # Splitting the dataset into features and labels
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, 3].values
      
      # Importing DecisionTreeClassifier
      from sklearn.tree import DecisionTreeClassifier
      
      # Initializing the classifier
      classifier = DecisionTreeClassifier(criterion='entropy')
      
      # Fitting the classifier to the dataset
      classifier.fit(X, y)
      
      # Predicting the output using the classifier
      return classifier.predict([[A, B, C]])[0]
  
  # Test cases
  assert decision_tree_classifier(1, 2, 3) == 4
  assert decision_tree_classifier(5, 6, 7) == 8
  assert decision_tree_classifier(9, 10, 11) == 12
  
output: def decision_tree_classifier(A, B, C):
      dataset = pd.read_csv('data.csv')
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, 3].values
      from sklearn.tree import DecisionTreeClassifier
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)
      return classifier.predict([[A, B, C]])[0]