input: Write a function to develop a decision tree classifier in Python that predicts a fourth feature from three other features (A, B, C). The function should read the dataset from a CSV file, split the dataset into features and labels, and then use the DecisionTreeClassifier from sklearn to train the model. The function should return the trained classifier.
  
  def train_decision_tree_classifier(csv_file):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      # importing the dataset 
      dataset = pd.read_csv(csv_file)
      
      # splitting the dataset into features and labels 
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      
      # creating the DecisionTreeClassifier 
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      
      # fitting the classifier to the dataset
      classifier.fit(X, y)
      
      return classifier
  
  assert type(train_decision_tree_classifier('data.csv')) == DecisionTreeClassifier
  assert train_decision_tree_classifier('data.csv').criterion == 'entropy'
output: def train_decision_tree_classifier(csv_file):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      dataset = pd.read_csv(csv_file)
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)
      return classifier