input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features (A, B, C). The function should import the dataset from a CSV file, split the dataset into features and labels, create and fit a DecisionTreeClassifier with entropy as the criterion, and return the classifier.
  
  def decision_tree_classifier():
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      # importing 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
      
      # creating DecisionTreeClassifier with entropy as the criterion
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      
      # fitting the classifier to the dataset
      classifier.fit(X, y)
      
      return classifier
  
  # Example test case
  assert isinstance(decision_tree_classifier(), DecisionTreeClassifier)
output: def decision_tree_classifier():
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      dataset = pd.read_csv('data.csv')
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)
      return classifier