code: def test_transfer_learning_using_position(self):
      network_pretrained = layers.join(
          layers.Input(10),
          layers.Elu(5),
          layers.Elu(2, name="elu"),
          layers.Sigmoid(1),
      )
      network_new = layers.join(
          layers.Input(10),
          layers.Elu(5),
          layers.Elu(2),
      )
      pretrained_layers_stored = storage.save_dict(network_pretrained)
  
      with self.assertRaises(ParameterLoaderError):
          storage.load_dict(
              network_new,
              pretrained_layers_stored,
              load_by="names_or_order",
              ignore_missing=False,
          )
  
      storage.load_dict(
          network_new,
          pretrained_layers_stored,
          load_by="names_or_order",
          ignore_missing=True,
      )
  
      random_input = asfloat(np.random.random((12, 10)))
      new_network_output = self.eval(network_new.output(random_input))
      pretrained_output = self.eval(
          network_pretrained.end("elu").output(random_input)
      )
  
      np.testing.assert_array_almost_equal(
          pretrained_output, new_network_output
      )
  
imports: import copy
  import numpy
  from neupy.utils import asfloat
  from neupy import layers, storage
  from neupy.storage import validate_data_structure, InvalidFormat, ParameterLoaderError, load_layer_parameter
  from base import BaseTestCase
label_desc: Use image node_id 1 for calling the TransferLearningTestCase obj's underlying member method code with example usage: obj.test_transfer_learning_using_position() without return types