input: def predict_customer_lifetime_value(transactions: np.ndarray) -> float:
      """ Predict the customer lifetime value given a numpy array of customer's past transactions.
      The model uses a RandomForestRegressor and scales the input data using StandardScaler.
      >>> predict_customer_lifetime_value(np.array([[10, 100, 5, ...]]))
      2500.0
      """
output:     
      from sklearn.ensemble import RandomForestRegressor
      from sklearn.preprocessing import StandardScaler
      import numpy as np
  
      # Flatten the input array to a 1D array
      transactions_flat = transactions.flatten()
  
      # Scale the input data
      scaler = StandardScaler()
      scaled_transactions = scaler.fit_transform(transactions_flat.reshape(-1, 1))
  
      # Train a RandomForestRegressor model
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(scaled_transactions, transactions_flat)
  
      # Predict the customer lifetime value
      predicted_value = model.predict(scaled_transactions)[0]
  
      return predicted_value
  