input: Write a python function to train an SVR model on stock price data and plot the predicted versus actual stock prices.
  assert train_and_plot_svr_model('stock-data.csv') is None
output: def train_and_plot_svr_model(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.svm import SVR
      from sklearn.preprocessing import StandardScaler
      from sklearn.metrics import mean_squared_error
      import matplotlib.pyplot as plt
  
      # Load the data
      data = pd.read_csv(csv_file)
  
      # Assume the data has columns 'Date' and 'Close' for simplicity
      # Extract the features and target
      X = data.index.values.reshape(-1, 1)  # Using index as feature
      y = data['Close'].values
  
      # Split the 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)
  
      # Standardize the data
      scaler = StandardScaler()
      X_train_scaled = scaler.fit_transform(X_train)
      X_test_scaled = scaler.transform(X_test)
  
      # Initialize and train the SVR model
      svr = SVR(kernel='rbf', C=1e3, gamma=0.1)
      svr.fit(X_train_scaled, y_train)
  
      # Make predictions
      y_pred = svr.predict(X_test_scaled)
  
      # Plot the results
      plt.figure(figsize=(14, 7))
      plt.plot(data.index, data['Close'], color='blue', label='Actual Stock Prices')
      plt.scatter(X_test, y_pred, color='red', label='Predicted Stock Prices')
      plt.xlabel('Date Index')
      plt.ylabel('Stock Price')
      plt.title('SVR Model: Predicted vs Actual Stock Prices')
      plt.legend()
      plt.show()