input: Write a python function to train an SVM model on the iris dataset and evaluate it using accuracy score and confusion matrix.
  assert evaluate_svm_model()['accuracy'] > 0.9
  assert evaluate_svm_model()['confusion_matrix'].shape == (3, 3)
output: def evaluate_svm_model():
      from sklearn import datasets
      from sklearn.model_selection import train_test_split
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score, confusion_matrix
  
      # 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=0.3, random_state=42)
  
      # Create an SVM classifier and train it
      clf = SVC(kernel='linear')
      clf.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = clf.predict(X_test)
  
      # Calculate accuracy and confusion matrix
      accuracy = accuracy_score(y_test, y_pred)
      conf_matrix = confusion_matrix(y_test, y_pred)
  
      return {'accuracy': accuracy, 'confusion_matrix': conf_matrix}