input: Write a python function to implement a simple 2-layer neural network to compute the output from given inputs and random weights.
  assert compute_output(np.array([2, 1]), np.array([[0.5, 0.2], [0.3, 0.7]]), np.array([[0.6], [0.9]])) == 0.678
  assert compute_output(np.array([1, 3]), np.array([[0.4, 0.1], [0.2, 0.8]]), np.array([[0.5], [0.3]])) == 0.38
output: import numpy as np
  
  def compute_output(inputs, weights1, weights2):
      def sigmoid(x):
          return 1 / (1 + np.exp(-x))
      
      # First layer computation
      hidden_layer_input = np.dot(inputs, weights1)
      hidden_layer_output = sigmoid(hidden_layer_input)
      
      # Second layer computation
      output_layer_input = np.dot(hidden_layer_output, weights2)
      output = sigmoid(output_layer_input)
      
      return output[0]