input: Write a python function to train and test an SVM classifier using the Iris dataset and return the accuracy score.
  assert svm_classifier_accuracy() > 0.9
output: def svm_classifier_accuracy():
      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 data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
  
      # Initialize the SVM classifier
      clf = SVC(kernel='linear')
  
      # Train the classifier
      clf.fit(X_train, y_train)
  
      # Make predictions
      y_pred = clf.predict(X_test)
  
      # Calculate the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy