input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. If the dataset is empty or if any of the features A, B, or C are missing, return None.
  assert develop_decision_tree('data.csv')==None
  assert develop_decision_tree('empty_data.csv')==None
  assert develop_decision_tree('incomplete_data.csv')==None
output: def develop_decision_tree(filename):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      try:
          data = pd.read_csv(filename)
      except:
          return None
      
      if data.empty or data.isnull().any().any():
          return None
      
      X = data[['A', 'B', 'C']]
      y = data['D']
      
      if X.isnull().any().any() or y.isnull().any():
          return None
      
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      
      return clf