input: Write a function to develop a decision tree classifier in Python that predicts a fourth feature from three other features. The function should read a dataset, split it into features and labels, initialize a DecisionTreeClassifier with entropy criterion, and fit the classifier to the dataset. The function should return the trained 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
  
      # initializing DecisionTreeClassifier with entropy criterion
      classifier = DecisionTreeClassifier(criterion='entropy')
  
      # fitting the classifier to the dataset
      classifier.fit(X, y)
  
      return classifier
  
  assert isinstance(decision_tree_classifier(), DecisionTreeClassifier) == True
  assert decision_tree_classifier().criterion == 'entropy'
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