input: Write a python function to train an SVC classifier on the iris dataset and make predictions.
  assert len(train_svc_classifier()[0]) == 30  # 20% of 150 samples
  assert len(train_svc_classifier()[1]) == 30  # 20% of 150 samples
  assert train_svc_classifier()[0][0] in [0, 1, 2]  # Predictions should be one of the class labels in the iris dataset
output: def train_svc_classifier():
      from sklearn import datasets
      from sklearn.model_selection import train_test_split
      from sklearn.svm import SVC
      
      # Load the iris dataset
      iris = datasets.load_iris()
      X = iris.data
      y = iris.target
      
      # Split the dataset into training and test sets (80% train, 20% test)
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Create an SVC classifier
      clf = SVC()
      
      # Train the classifier
      clf.fit(X_train, y_train)
      
      # Make predictions on the test set
      y_pred = clf.predict(X_test)
      
      return y_pred, y_test