input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. The function should take the dataset file name as input, load the dataset, split it into features and labels, create and train a DecisionTreeClassifier with entropy as the criterion, and return the trained classifier.
  assert len(develop_decision_tree_classifier('data.csv').classes_)>0
output: def develop_decision_tree_classifier(file_name):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
      
      data = pd.read_csv(file_name)
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      clf = DecisionTreeClassifier(criterion='entropy')
      clf.fit(X, y)
      return clf