input: Write a function to build a decision tree classifier in Python that predicts a fourth feature (D) from three other features (A, B, C). The function should load data from 'data.csv', split the data into features and labels, and then fit the decision tree classifier using entropy as the criterion. The function should return the trained classifier.
  
  assert build_decision_tree() is not None
output: def build_decision_tree():
      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['D']
      clf = DecisionTreeClassifier(criterion='entropy')
      clf.fit(X, y)
      return clf