input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features A, B, and C. If the dataset is successfully imported and the classifier is fitted to the dataset, return True; otherwise return False.
  
  def decision_tree_classifier(dataset):
      try:
          # splitting the dataset into features and labels
          X = dataset.iloc[:, :-1].values
          y = dataset.iloc[:, 3].values
  
          # importing DecisionTreeClassifier
          from sklearn.tree import DecisionTreeClassifier
  
          classifier = DecisionTreeClassifier(criterion='entropy')
  
          # fitting the classifier to the dataset
          classifier.fit(X, y)
          return True
      except:
          return False
  
  assert decision_tree_classifier(pd.read_csv('data.csv')) == True
  assert decision_tree_classifier(pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6], 'D': [7, 8]})) == True
  assert decision_tree_classifier(pd.DataFrame({'E': [1, 2], 'F': [3, 4], 'G': [5, 6], 'H': [7, 8]})) == False
  assert decision_tree_classifier(pd.DataFrame({})) == False
  assert decision_tree_classifier(pd.DataFrame({'A': [1, 2]})) == False
output: def decision_tree_classifier(dataset):
      try:
          X = dataset.iloc[:, :-1].values
          y = dataset.iloc[:, 3].values
          from sklearn.tree import DecisionTreeClassifier
          classifier = DecisionTreeClassifier(criterion='entropy')
          classifier.fit(X, y)
          return True
      except:
          return False