input: Write a function that develops a decision tree classifier in Python to predict a fourth feature based on three other features using the scikit-learn library. The function should read a dataset from a CSV file, split it into features and labels, train a decision tree classifier, and return the trained classifier.
  
  
  def decision_tree_classifier(filename):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      # Importing the dataset
      dataset = pd.read_csv(filename)
      
      # Splitting the dataset into features and labels
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, -1].values
      
      # Creating a Decision Tree Classifier
      classifier = DecisionTreeClassifier(criterion='entropy')
      
      # Fitting the classifier to the dataset
      classifier.fit(X, y)
      
      return classifier
  
  # Test cases
  assert isinstance(decision_tree_classifier('data.csv'), DecisionTreeClassifier)
  assert isinstance(decision_tree_classifier('other_data.csv'), DecisionTreeClassifier)
  
output: def decision_tree_classifier(filename):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      # Importing the dataset
      dataset = pd.read_csv(filename)
      
      # Splitting the dataset into features and labels
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, -1].values
      
      # Creating a Decision Tree Classifier
      classifier = DecisionTreeClassifier(criterion='entropy')
      
      # Fitting the classifier to the dataset
      classifier.fit(X, y)
      
      return classifier