input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. The function should read data from a CSV file and split the data into features (A, B, C) and labels. It should then train a DecisionTreeClassifier with entropy as the criterion. If the model is successfully fit to the data, return True; otherwise, return False. If the dataset is empty, return False.
  assert decision_tree_classifier('data.csv')==True
  assert decision_tree_classifier('empty_data.csv')==False
output: def decision_tree_classifier(file_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      try:
          data = pd.read_csv(file_path)
          if data.empty:
              return False
          
          features = data[['A', 'B', 'C']]
          labels = data.iloc[:, -1]
          
          clf = DecisionTreeClassifier(criterion='entropy')
          clf.fit(features, labels)
          
          return True
      except:
          return False