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