input: 
  def predict_customer_ltv(transactions_csv_path: str, customer_input: List[float]) -> float:
      """ Predicts the customer lifetime value (LTV) given a CSV file path containing past transactions and a list of past transactions for a new customer.
      
      >>> predict_customer_ltv('transactions.csv', [10, 100, 5, ...])
      1234.56
      >>> predict_customer_ltv('transactions.csv', [5, 75, 2, ...])
      987.65
      """
  
output:     import pandas as pd
      from sklearn.linear_model import LinearRegression
  
      # Load the transactions data
      transactions_df = pd.read_csv(transactions_csv_path)
  
      # Prepare the data for training
      X = transactions_df.drop('ltv', axis=1)
      y = transactions_df['ltv']
  
      # Train the model
      model = LinearRegression()
      model.fit(X, y)
  
      # Predict LTV for the new customer
      predicted_ltv = model.predict([customer_input])[0]
  
      return predicted_ltv