input: Write a python function to train a RandomForestClassifier using the given parameters and return the classifier object.
  assert isinstance(train_random_forest(X_train, y_train), RandomForestClassifier)
  assert train_random_forest(X_train, y_train).n_estimators == 100
  assert train_random_forest(X_train, y_train).criterion == "entropy"
  assert train_random_forest(X_train, y_train).random_state == 0
output: from sklearn.ensemble import RandomForestClassifier
  
  def train_random_forest(X_train, y_train):
      clf = RandomForestClassifier(n_estimators=100, criterion="entropy", random_state=0)
      clf.fit(X_train, y_train)
      return clf