input: Write a python function to load data from a CSV file, split it into train and test sets, vectorize the text data, train a RandomForestClassifier, and return the accuracy score.
  assert isinstance(your_function('data.csv'), float)
  assert 0 <= your_function('data.csv') <= 1
output: def load_and_train_model(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Load data from CSV file
      data = pd.read_csv(csv_file)
  
      # Assuming the CSV has two columns: 'text' and 'label'
      texts = data['text']
      labels = data['label']
  
      # Split data into train and test sets
      X_train, X_test, y_train, y_test = train_test_split(texts, labels, test_size=0.2, random_state=42)
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X_train_vectorized = vectorizer.fit_transform(X_train)
      X_test_vectorized = vectorizer.transform(X_test)
  
      # Train a RandomForestClassifier
      clf = RandomForestClassifier(random_state=42)
      clf.fit(X_train_vectorized, y_train)
  
      # Predict and calculate accuracy
      y_pred = clf.predict(X_test_vectorized)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy