input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features A, B, and C. The function should read a dataset from a CSV file named 'data.csv', split it into features (X) and a label (y), fit a DecisionTreeClassifier using entropy as the criterion, and then return the trained classifier.
  assert develop_decision_tree_classifier('data.csv')!=None
  assert hasattr(develop_decision_tree_classifier('data.csv'), 'predict')
  assert callable(develop_decision_tree_classifier('data.csv').predict)
output: def develop_decision_tree_classifier(file_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      data = pd.read_csv(file_path)
      X = data[['A', 'B', 'C']]
      y = data.iloc[:, -1]
      clf = DecisionTreeClassifier(criterion='entropy')
      clf.fit(X, y)
      return clf