input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features and fit the classifier to the dataset. The function should return the trained DecisionTreeClassifier.
  
  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
      
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      
      #fitting the classifier to the dataset
      classifier.fit(X,y)
      
      return classifier
  
  assert isinstance(decision_tree_classifier(), DecisionTreeClassifier)
  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