input: from typing import List
  
  
  def predict_house_price(input_list: List[float]) -> float:
      """Implement a neural network to predict the price of a house based on the given set of features.
      >>> predict_house_price([0.8, 0.4, 0.7])
      192000.0
      """
output:     import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      # Example weights and biases for a simple neural network
      weights = {
          'hidden_layer': np.array([[0.5, 0.3], [0.2, 0.4], [0.1, 0.6]]),
          'output_layer': np.array([[0.7], [0.8]])
      }
      biases = {
          'hidden_layer': np.array([0.1, 0.2]),
          'output_layer': np.array([0.3])
      }
  
      # Convert input list to numpy array
      input_array = np.array(input_list).reshape(1, -1)
  
      # Forward pass through the neural network
      hidden_layer_input = np.dot(input_array, weights['hidden_layer']) + biases['hidden_layer']
      hidden_layer_output = np.maximum(0, hidden_layer_input)  # ReLU activation
      output_layer_input = np.dot(hidden_layer_output, weights['output_layer']) + biases['output_layer']
  
      # Predicted price
      predicted_price = output_layer_input[0][0] * 200000  # Scaling factor for price
  
      return predicted_price