input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features.
  
  def decision_tree_classifier(dataset_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
  
      dataset = pd.read_csv(dataset_path)
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, 3].values
  
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)
      return classifier
  
  assert decision_tree_classifier('data1.csv').predict([[1, 2, 3]]) == [1]
  assert decision_tree_classifier('data2.csv').predict([[4, 5, 6]]) == [0]
  assert decision_tree_classifier('data3.csv').predict([[7, 8, 9]]) == [1]
  assert decision_tree_classifier('data4.csv').predict([[10, 11, 12]]) == [0]
  assert decision_tree_classifier('empty_data.csv').predict([]) == []
output: def decision_tree_classifier(dataset_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
  
      dataset = pd.read_csv(dataset_path)
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, 3].values
  
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)
      return classifier