input: Write a function to create and train a decision tree classifier in Python to predict a fourth feature from three other features. The function should take the dataset path as input, split the dataset into features and labels, train the classifier, and return the trained classifier. 
  
  def train_decision_tree_classifier(dataset_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
  
      #importing the dataset 
      dataset = pd.read_csv(dataset_path)
  
      #splitting the dataset into features and labels 
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
  
      #creating and fitting the classifier to the dataset
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      classifier.fit(X, y)
      
      return classifier
  
  assert isinstance(train_decision_tree_classifier('data.csv'), DecisionTreeClassifier)
  assert train_decision_tree_classifier('empty_data.csv') is None
output: def train_decision_tree_classifier(dataset_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
  
      try:
          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
      except Exception:
          return None