input: Write a python function to load the iris dataset, train a linear SVM model, and return the accuracy of the model.
  ```python
  assert train_and_evaluate_svm() > 0.9
output: def train_and_evaluate_svm():
      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, 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=0.3, random_state=42)
  
      # Train a linear SVM model
      svm_model = SVC(kernel='linear')
      svm_model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = svm_model.predict(X_test)
  
      # Calculate the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy