input: Write a function to create a decision tree classifier in Python that predicts the fourth feature from three other features (A, B, C). The function should take a dataset file path as input, load the dataset, split it into features and labels, and train a decision tree classifier using the entropy criterion.
  assert decision_tree_classifier('data.csv')==DecisionTreeClassifier(criterion='entropy')
  assert decision_tree_classifier('empty.csv')==None
  assert decision_tree_classifier('incomplete_data.csv')==DecisionTreeClassifier(criterion='entropy')
  assert decision_tree_classifier('nonexistent_file.csv')==None
  assert decision_tree_classifier('data_with_nan.csv')==DecisionTreeClassifier(criterion='entropy')
output: def decision_tree_classifier(file_path):
      try:
          import pandas as pd
          from sklearn.tree import DecisionTreeClassifier
          data = pd.read_csv(file_path)
          if data.isnull().values.any():
              return None
          X = data[['A', 'B', 'C']]
          y = data.iloc[:, 3]
          clf = DecisionTreeClassifier(criterion='entropy')
          clf.fit(X, y)
          return clf
      except:
          return None