input: Write a function to implement and train a decision tree classifier that predicts the fourth feature from three given features A, B, and C using Python. The function should return the trained classifier. If the dataset file 'data.csv' does not exist, the function should raise a FileNotFoundError. 
  
  assert isinstance(train_decision_tree(exists=True), DecisionTreeClassifier)
  assert isinstance(train_decision_tree(exists=False), FileNotFoundError)
output: def train_decision_tree(exists=True):
      if not exists:
          raise FileNotFoundError
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      data = pd.read_csv('data.csv')
      X = data[['A', 'B', 'C']]
      y = data['D']
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      return clf