input: Write a function to develop a decision tree classifier in Python that predicts a fourth feature from three other features. The function should import the dataset from a CSV file, split it into features (A, B, C) and label (D), and then train a DecisionTreeClassifier using the entropy criterion. It should return the trained classifier.
  
  
  import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def train_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 with entropy criterion
      classifier = DecisionTreeClassifier(criterion='entropy')
      
      # Fitting the classifier to the dataset
      classifier.fit(X, y)
      
      return classifier
  
  # Test cases
  assert isinstance(train_decision_tree_classifier('data.csv'), DecisionTreeClassifier)
  
output: import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def train_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