input: Write a function that develops a decision tree classifier in Python to predict a fourth feature from three other features. The function should take a CSV file path as input, read the dataset, split it into features and labels, and then train a DecisionTreeClassifier using the features to predict the fourth feature. The function should return the trained classifier.
  
  from sklearn.tree import DecisionTreeClassifier
  import pandas as pd
  
  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[:, -1].values
      
      #initializing the DecisionTreeClassifier 
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      
      #fitting the classifier to the dataset
      classifier.fit(X, y)
      
      return classifier
  
  assert isinstance(train_decision_tree_classifier('test_data.csv'), DecisionTreeClassifier) == True
output: def train_decision_tree_classifier(file_path):
      dataset = pd.read_csv(file_path)
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, -1].values
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)
      return classifier