input: def predict_customer_lifetime_value(transactions_file: str, customer_input: List[float]) -> float:
      """Predict the customer lifetime value based on the past transactions data and a new customer's input data.
      The function reads transactions data from a CSV file, scales the features, trains a RandomForestRegressor,
      and then predicts the lifetime value for a new customer based on their input data.
      >>> predict_customer_lifetime_value('transactions.csv', [10, 100, 5, ...])
      1234.56
      """
output:     import pandas as pd
      from sklearn.ensemble import RandomForestRegressor
      from sklearn.preprocessing import StandardScaler
  
      # Load the transactions data
      df = pd.read_csv(transactions_file)
  
      # Separate features and target
      X = df.drop('lifetime_value', axis=1)
      y = df['lifetime_value']
  
      # Scale the features
      scaler = StandardScaler()
      X_scaled = scaler.fit_transform(X)
  
      # Train the model
      model = RandomForestRegressor()
      model.fit(X_scaled, y)
  
      # Scale the new customer input
      customer_input_scaled = scaler.transform([customer_input])
  
      # Predict the lifetime value
      predicted_value = model.predict(customer_input_scaled)
  
      return predicted_value[0]