code: def print_network_spec(mlmodel_spec, interface_only=False):
      
      inputs, outputs, layers_info = summarize_neural_network_spec(mlmodel_spec)
  
      print('Inputs:')
      for i in inputs:
          name, description = i
          print('  {} {}'.format(name, description))
  
      print('Outputs:')
      for o in outputs:
          name, description = o
          print('  {} {}'.format(name, description))
  
      if layers_info is None:
          print('\n(This MLModel is not a neural network model or does not contain any layers)')
  
      if layers_info and not interface_only:
          print('\nLayers:')
          for idx, l in enumerate(layers_info):
              layer_type, name, in_blobs, out_blobs, params_info = l
              print('[{}] ({}) {}'.format(idx, layer_type, name))
              print('  Input blobs: {}'.format(in_blobs))
              print('  Output blobs: {}'.format(out_blobs))
              if len(params_info) > 0:
                  print('  Parameters: ')
              for param in params_info:
                  print('    {} = {}'.format(param[0], param[1]))
  
      print('\n')
docs: Print the network information summary.
      Args:
      mlmodel_spec : the mlmodel spec
      interface_only : Shows only the input and output of the network