input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. The function should read a dataset from a CSV file, split it into features and labels, fit a DecisionTreeClassifier to the dataset, and return the classifier. The fourth feature to be predicted is the last column in the dataset. If the dataset is empty, the function should return None.
  assert develop_decision_tree_classifier('data.csv') != None
  assert develop_decision_tree_classifier('empty_data.csv') == None
output: def develop_decision_tree_classifier(file_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      data = pd.read_csv(file_path)
      
      if data.empty:
          return None
      
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      classifier = DecisionTreeClassifier()
      classifier.fit(X, y)
      
      return classifier