input: Write a function to develop a decision tree classifier in Python to predict the value of the fourth feature from the first three features. The function should be named `decision_tree_classifier` and it should take a filename as input which contains the dataset. The function should load the dataset, split it into features and labels, train a DecisionTreeClassifier with entropy as the criterion, and return the trained classifier.
  
  assert decision_tree_classifier('data.csv') # returns a trained DecisionTreeClassifier object
output: def decision_tree_classifier(filename):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      data = pd.read_csv(filename)
      X = data.iloc[:, :3]
      y = data.iloc[:, 3]
      clf = DecisionTreeClassifier(criterion='entropy')
      clf.fit(X, y)
      return clf