input: Write a function to implement a decision tree classifier in Python to predict a fourth feature from three other features A, B, and C. Provide the function with the necessary imports and set up. If the input data is given in a CSV file named "data.csv", split the dataset into features and labels accordingly and fit the classifier to the dataset. Return the fitted classifier.
  
  assert decision_tree_classifier('data.csv')==DecisionTreeClassifier(criterion='entropy')
output: def 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)
      X = data[['A', 'B', 'C']]
      y = data.iloc[:, -1]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      clf = DecisionTreeClassifier(criterion='entropy')
      clf.fit(X_train, y_train)
      
      return clf