input: Write a function to develop a decision tree classifier in Python using the given dataset to predict the fourth feature from the first three features. Load the dataset from 'data.csv', split it into features (A, B, C) and the label, and fit a DecisionTreeClassifier using entropy as the criterion.
  
  import pandas as pd
  
  def decision_tree_classifier():
      # 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
      
      # importing DecisionTreeClassifier 
      from sklearn.tree import DecisionTreeClassifier
      
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      
      # fitting the classifier to the dataset
      classifier.fit(X,y)
      
      return classifier
  
  assert decision_tree_classifier() is not None
output: def decision_tree_classifier():
      dataset = pd.read_csv('data.csv')
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      from sklearn.tree import DecisionTreeClassifier
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)
      return classifier