input: Write a python function to train and evaluate a Support Vector Classifier (SVC) on the Iris dataset, returning the accuracy score.
  assert round(train_and_evaluate_svc(), 2) == 1.00
output: def train_and_evaluate_svc():
      from sklearn import datasets
      from sklearn.model_selection import train_test_split
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
  
      # Load the iris dataset
      iris = datasets.load_iris()
      X = iris.data
      y = 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=0.2, random_state=42)
  
      # Train the SVC model
      svc = SVC()
      svc.fit(X_train, y_train)
  
      # Predict the labels for the test set
      y_pred = svc.predict(X_test)
  
      # Calculate and return the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy