input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. The function should return the trained classifier. If the input dataset is empty, it should return None.
  
  def decision_tree_classifier(dataset_path):
      if dataset_path is None:
          return None
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      dataset = pd.read_csv(dataset_path)
      if dataset.empty:
          return None
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      classifier.fit(X, y)
      return classifier
  
  assert decision_tree_classifier('data.csv') is not None
  assert decision_tree_classifier(None) is None
  assert decision_tree_classifier('empty_data.csv') is None
output: def decision_tree_classifier(dataset_path):
      if dataset_path is None:
          return None
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      dataset = pd.read_csv(dataset_path)
      if dataset.empty:
          return None
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      classifier.fit(X, y)
      return classifier