input: Write a python function to load an email dataset, split it into training and test sets, transform the data using CountVectorizer, train a Multinomial Naive Bayes classifier, and make predictions.
  assert load_and_predict('emails.csv').shape == (4,)  # This assertion is to check if the predictions for test set have the same length as the test set labels.
output: 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
  
  def load_and_predict(file_path):
      # Load dataset
      data = pd.read_csv(file_path)
      
      # Split dataset into features and labels
      X = data['email']
      y = data['label']
      
      # Split data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Transform data using CountVectorizer
      vectorizer = CountVectorizer()
      X_train_counts = vectorizer.fit_transform(X_train)
      X_test_counts = vectorizer.transform(X_test)
      
      # Train a Multinomial Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_train_counts, y_train)
      
      # Make predictions on the test set
      predictions = clf.predict(X_test_counts)
      
      return predictions