input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. Use the given dataset and import necessary libraries to perform the task. The function should return the trained classifier.
  
  
  from sklearn.tree import DecisionTreeClassifier
  import pandas as pd
  
  def decision_tree_classifier(dataset_file):
      # Importing the dataset 
      dataset = pd.read_csv(dataset_file)
      
      # Splitting the dataset into features and labels 
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      
      # Importing DecisionTreeClassifier 
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      
      # Fitting the classifier to the dataset
      classifier.fit(X, y)
      
      return classifier
  
  # Asserts
  dataset_file = 'data.csv'
  assert isinstance(decision_tree_classifier(dataset_file), DecisionTreeClassifier)
  
output: def decision_tree_classifier(dataset_file):
      dataset = pd.read_csv(dataset_file)
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      classifier.fit(X, y)
      return classifier