input: Write a function to create a decision tree classifier in Python using the given features A, B, and C to predict a fourth feature. The function should load the dataset, split the dataset into features and labels, initialize and fit the classifier, and then return the trained classifier.
  assert create_decision_tree_classifier("data.csv") is not None
  assert isinstance(create_decision_tree_classifier("data.csv"), DecisionTreeClassifier)
  assert create_decision_tree_classifier("") is None
  assert create_decision_tree_classifier("nonexistent.csv") is None
output: def create_decision_tree_classifier(file_path):
      if not file_path:
          return None
      try:
          import pandas as pd
          from sklearn.tree import DecisionTreeClassifier
          data = pd.read_csv(file_path)
          features = data[['A', 'B', 'C']]
          labels = data.iloc[:, -1]
          classifier = DecisionTreeClassifier()
          classifier.fit(features, labels)
          return classifier
      except:
          return None