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 load the dataset from a CSV file named 'data.csv', split the dataset into features and labels, train the classifier using entropy as the criterion, and return the trained classifier.
  assert develop_decision_tree_classifier('data.csv').__class__==DecisionTreeClassifier
  assert develop_decision_tree_classifier('data.csv').criterion=='entropy'
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