input: Write a python function to load a CSV file, split the data into training and testing sets, vectorize the text data using CountVectorizer, train a Multinomial Naive Bayes classifier, and return the accuracy score.
  ```python
  assert round(train_and_evaluate_model('data.csv'), 2) > 0.5
  assert isinstance(train_and_evaluate_model('data.csv'), float)
output: def train_and_evaluate_model(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import accuracy_score
  
      # Load the CSV file
      data = pd.read_csv(csv_file)
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(data['text'], data['label'], test_size=0.2, random_state=42)
      
      # Vectorize the text data using CountVectorizer
      vectorizer = CountVectorizer()
      X_train_vect = vectorizer.fit_transform(X_train)
      X_test_vect = vectorizer.transform(X_test)
      
      # Train a Multinomial Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_train_vect, y_train)
      
      # Make predictions and calculate the accuracy score
      y_pred = clf.predict(X_test_vect)
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy