input: Write a function to develop a decision tree classifier in Python that predicts the fourth feature from three other features (A, B, and C). The function should read data from a CSV file, split the dataset into features and labels, train the classifier, and return the trained classifier.
  assert decision_tree_classifier() != None
  classifier = decision_tree_classifier()
  assert classifier.predict([[1, 2, 3]]) is not None
  assert classifier.predict([[4, 5, 6]]) is not None
output: def decision_tree_classifier():
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
  
      data = pd.read_csv('data.csv')
      X = data[['A', 'B', 'C']]
      y = data.iloc[:, 3]
      X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
      classifier = DecisionTreeClassifier()
      classifier.fit(X_train, y_train)
      return classifier