input: Write a function to create a decision tree classifier in Python that predicts the fourth feature based on three other features. The function should handle reading the dataset from a CSV file, splitting the data into features and labels, creating and fitting a decision tree classifier, and then returning the trained classifier. 
  
  assert decision_tree_classifier('data.csv').predict([[1, 2, 3]])==[0]
  assert decision_tree_classifier('data.csv').predict([[4, 5, 6]])==[1]
  assert decision_tree_classifier('data.csv').predict([[7, 8, 9]])==[1]
  assert decision_tree_classifier('data.csv').predict([[2, 3, 4]])==[0]
  assert decision_tree_classifier('data.csv').predict([[5, 6, 7]])==[1]
output: def decision_tree_classifier(file_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      data = pd.read_csv(file_path)
      X = data.iloc[:, :3]
      y = data.iloc[:, 3]
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      return clf