input: Write a function to create a decision tree classifier in Python that predicts a fourth feature from three other features given in a dataset. The function should take a CSV file as input and return the trained decision tree classifier.
  
  import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def decision_tree_classifier(file_path):
      # importing the dataset
      dataset = pd.read_csv(file_path)
      
      # splitting the dataset into features and labels
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      
      # creating a DecisionTreeClassifier
      classifier = DecisionTreeClassifier(criterion='entropy')
      
      # fitting the classifier to the dataset
      classifier.fit(X, y)
      
      return classifier
  
  # Test case using a dataset with features A, B, C and target D
  assert isinstance(decision_tree_classifier('data.csv'), DecisionTreeClassifier)
  
  # Test case with an empty dataset
  assert isinstance(decision_tree_classifier('empty_data.csv'), DecisionTreeClassifier)
output: def decision_tree_classifier(file_path):
      dataset = pd.read_csv(file_path)
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)
      return classifier