new_contents: import numpy as np
  
  def sigmoid(x):
      """
      Calculate sigmoid
      """
      return 1/(1+np.exp(-x))
  
  # Network size
  N_input = 4
  N_hidden = 3
  N_output = 2
  
  np.random.seed(42)
  # Make some fake data
  X = np.random.randn(4)
  
  weights_input_to_hidden = np.random.normal(0, scale=0.1, size=(N_input, N_hidden))
  weights_hidden_to_output = np.random.normal(0, scale=0.1, size=(N_hidden, N_output))
  
  
  # TODO: Make a forward pass through the network
  
  hidden_layer_in = np.dot(X, weights_input_to_hidden)
  hidden_layer_out = sigmoid(hidden_layer_in)
  
  print('Hidden-layer Output:')
  print(hidden_layer_out)
  
  output_layer_in = np.dot(hidden_layer_out, weights_hidden_to_output)
  output_layer_out = sigmoid(output_layer_in)
  
  print('Output-layer Output:')
  print(output_layer_out)
  
subject: add multilayer