input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features A, B, and C using the provided dataset in 'data.csv'. The function should train the classifier using the dataset and return the trained classifier.
  assert isinstance(develop_decision_tree_classifier('data.csv'), DecisionTreeClassifier) == True
  assert develop_decision_tree_classifier('nonexistent.csv') == None
output: def develop_decision_tree_classifier(filename):
      try:
          import pandas as pd
          from sklearn.tree import DecisionTreeClassifier
          data = pd.read_csv(filename)
          X = data[['A', 'B', 'C']]
          y = data.iloc[:, -1]
          clf = DecisionTreeClassifier()
          clf.fit(X, y)
          return clf
      except FileNotFoundError:
          return None