input: from typing import List, Tuple
  
  def predict_customer_ltv(past_transactions: List[Tuple[float]], customer_new_transaction: List[float]) -> float:
      """ Predict the customer lifetime value (LTV) based on past transactions and a new customer transaction.
      The past transactions are a list of tuples where each tuple represents the features of a past transaction.
      The new customer transaction is a list of features for a new transaction.
      The function returns the predicted LTV for the new customer transaction.
  
      >>> predict_customer_ltv([(10, 100, 5, ...), (5, 50, 2, ...), ...], [10, 100, 5, ...])
      1500.0
      """
output:     # Initialize a variable to store the sum of weighted past transactions
      total_weighted_sum = 0.0
      
      # Iterate over each past transaction
      for past_transaction in past_transactions:
          # Calculate the weight based on the similarity between past and new transaction
          weight = sum(abs(past_feature - new_feature) for past_feature, new_feature in zip(past_transaction, customer_new_transaction))
          
          # Calculate the weighted sum of the past transaction
          weighted_sum = weight * sum(past_transaction)
          
          # Add the weighted sum to the total weighted sum
          total_weighted_sum += weighted_sum
      
      # Calculate the predicted LTV by dividing the total weighted sum by the number of past transactions
      predicted_ltv = total_weighted_sum / len(past_transactions)
      
      return predicted_ltv