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. The function should load a dataset from a CSV file, split the dataset into features and labels, fit the classifier to the dataset, and return the trained classifier.
  assert classifier_decision_tree(load_dataset("data.csv")).predict([[1, 2, 3]]) == [0]
  assert classifier_decision_tree(load_dataset("data.csv")).predict([[4, 5, 6]]) == [1]
  assert classifier_decision_tree(load_dataset("data.csv")).predict([[7, 8, 9]]) == [0]
  assert classifier_decision_tree(load_dataset("empty.csv")) == None
  assert classifier_decision_tree(load_dataset("invalid_path.csv")) == None
output: def classifier_decision_tree(dataset):
      if dataset is None or dataset.empty:
          return None
      X = dataset[['A', 'B', 'C']]
      y = dataset['D']
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      return clf