input: Write a function to create a decision tree classifier in Python to predict a fourth feature from three other features using the given dataset. The function should import the necessary libraries, read the dataset from a CSV file, split the dataset into features and labels, create a decision tree classifier with a specified criterion, and train the classifier on the dataset. The function should return the trained classifier.
  assert create_decision_tree_classifier('data.csv', 'entropy').predict([0.5, 0.3, 0.2]) is not None
  assert create_decision_tree_classifier('data.csv', 'gini').predict([0.1, 0.4, 0.5]) is not None
  assert create_decision_tree_classifier('nonexistent.csv', 'entropy') is None
  assert create_decision_tree_classifier('data.csv', 'entropy').predict([1, 1, 1]) is not None
  assert create_decision_tree_classifier('', 'gini') is None
output: def create_decision_tree_classifier(file_path, criterion):
      try:
          import pandas as pd
          from sklearn.tree import DecisionTreeClassifier
          from sklearn.model_selection import train_test_split
          
          if not file_path:
              return None
          
          data = pd.read_csv(file_path)
          X = data.iloc[:, :-1]
          y = data.iloc[:, -1]
          
          clf = DecisionTreeClassifier(criterion=criterion)
          clf.fit(X, y)
          
          return clf
      except Exception:
          return None