input: 
  def create_and_print_neural_network_output() -> None:
      """ Given a 3-layer artificial neural network setup, computes and prints the network's output.
      >>> create_and_print_neural_network_output()
      [2.9]
      """
  
output:     import numpy as np
  
      # Define the network parameters
      input_layer = np.array([1.0])
      weights_input_to_hidden = np.array([[0.5], [0.5]])
      weights_hidden_to_output = np.array([[1.0, 1.0]])
      bias_hidden = np.array([0.1, 0.1])
      bias_output = np.array([0.2])
  
      # Define the activation function (ReLU for hidden layer, linear for output layer)
      def relu(x):
          return np.maximum(0, x)
  
      def linear(x):
          return x
  
      # Compute the output of the network
      hidden_layer_input = np.dot(weights_input_to_hidden.T, input_layer) + bias_hidden
      hidden_layer_output = relu(hidden_layer_input)
      output_layer_input = np.dot(weights_hidden_to_output, hidden_layer_output) + bias_output
      output_layer_output = linear(output_layer_input)
  
      # Print the output
      print(output_layer_output.tolist())