input: Write a function to create and fit a decision tree classifier in Python to predict a fourth feature from three other features (A, B, C). The function should read a CSV file named 'data.csv' and split the data into features and labels. It should then fit the decision tree classifier to the data and return the classifier. If the dataset contains incomplete data, the function should return None.
  
  assert decision_tree_classifier([[1, 2, 3, 4], [2, 3, 4, 3], [3, 4, 5, 2]]) == DecisionTreeClassifier
  assert decision_tree_classifier([[1, 2, 3, 4], [2, 3, 4], [3, 4, 5, 2]]) == None
  assert decision_tree_classifier([]) == None
output: 
  import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def decision_tree_classifier():
      try:
          data = pd.read_csv('data.csv')
          if data.isnull().values.any():
              return None
          X = data.iloc[:, :-1]
          y = data.iloc[:, -1]
          clf = DecisionTreeClassifier()
          clf.fit(X, y)
          return clf
      except:
          return None
  