input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. The function should load the dataset from a CSV file, split the dataset into features and labels, train the decision tree classifier, and return the trained classifier. If the input dataset is empty, the function should return None.
  assert develop_decision_tree_classifier('data.csv')!=None
  assert develop_decision_tree_classifier('empty_dataset.csv')==None
output: def develop_decision_tree_classifier(file_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
      
      data = pd.read_csv(file_path)
      
      if data.empty:
          return None
      
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
      
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
      
      return clf