input: Write a function to create a decision tree classifier in Python that predicts a fourth feature from three other features (A, B, and C). The function should take the dataset path as an input, load the dataset, split it into features and labels, train a decision tree classifier using entropy criterion, and return the trained classifier.
  assert decision_tree_classifier('data.csv') == DecisionTreeClassifier(criterion='entropy')
  assert decision_tree_classifier('data.csv') == DecisionTreeClassifier(random_state=0)
  assert decision_tree_classifier('nonexistent.csv') == None
  assert decision_tree_classifier('empty.csv') == None
output: def decision_tree_classifier(dataset_path):
      try:
          import pandas as pd
          from sklearn.tree import DecisionTreeClassifier
          data = pd.read_csv(dataset_path)
          if data.empty:
              return None
          X = data[['A', 'B', 'C']]
          y = data.iloc[:, -1]
          clf = DecisionTreeClassifier(criterion='entropy')
          clf.fit(X, y)
          return clf
      except:
          return None