input: Write a python function to train a K-Neighbors Classifier on the Iris dataset and return the accuracy score.
  assert round(train_knn_classifier(0.3, 0, 3), 2) == 0.98
  assert round(train_knn_classifier(0.2, 1, 5), 2) == 0.97
  assert round(train_knn_classifier(0.4, 42, 7), 2) == 0.98
output: def train_knn_classifier(test_size, random_state, n_neighbors):
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the Iris dataset
      iris = load_iris()
      X, y = iris.data, iris.target
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
  
      # Initialize the K-Neighbors Classifier
      knn = KNeighborsClassifier(n_neighbors=n_neighbors)
  
      # Train the classifier
      knn.fit(X_train, y_train)
  
      # Predict the labels for the test set
      y_pred = knn.predict(X_test)
  
      # Calculate the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy