input: Write a function to develop a decision tree classifier in Python that predicts a fourth feature from three other features using the dataset from a CSV file. The function should return the trained classifier. If the dataset is empty, return None.
  
  assert develop_decision_tree_classifier('data.csv').predict([[5, 3, 2]]) == [1]
  assert develop_decision_tree_classifier('data.csv').predict([[7, 4, 1]]) == [0]
  assert develop_decision_tree_classifier('empty_data.csv') is None
  assert develop_decision_tree_classifier('data.csv').predict([[6, 2, 3]]) == [1]
  assert develop_decision_tree_classifier('data.csv').predict([[8, 5, 4]]) == [2]
output: import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def develop_decision_tree_classifier(filename):
      try:
          data = pd.read_csv(filename)
          if data.empty:
              return None
          X = data.iloc[:, :-1]
          y = data.iloc[:, -1]
          clf = DecisionTreeClassifier()
          clf.fit(X, y)
          return clf
      except:
          return None