code: def test_storage_load_dict_using_wrong_names(self):
      network = layers.join(
          layers.Input(3),
          layers.Relu(4, name="relu"),
          layers.Linear(5, name="linear") >> layers.Relu(),
          layers.Softmax(6, name="softmax"),
      )
  
      storage.load_dict(
          network,
          {
              "metadata": {},  # avoided for simplicity
              "graph": {},  # avoided for simplicity
              # Input layer was avoided on purpose
              "layers": [
                  {
                      "name": "name-1",
                      "class_name": "Relu",
                      "configs": {},
                      "parameters": {
                          "weight": {
                              "trainable": True,
                              "value": np.ones((3, 4)),
                          },
                          "bias": {
                              "trainable": True,
                              "value": np.ones((4,)),
                          },
                      },
                  },
                  {
                      "name": "name-2",
                      "class_name": "Relu",
                      "configs": {},
                      "parameters": {
                          "weight": {
                              "trainable": True,
                              "value": np.ones((4, 5)),
                          },
                          "bias": {
                              "trainable": True,
                              "value": np.ones((5,)),
                          },
                      },
                  },
                  {
                      "name": "name-3",
                      "class_name": "Softmax",
                      "configs": {},
                      "parameters": {
                          "weight": {
                              "trainable": True,
                              "value": np.ones((5, 6)),
                          },
                          "bias": {
                              "trainable": True,
                              "value": np.ones((6,)),
                          },
                      },
                  },
              ],
          },
          load_by="order",
          skip_validation=False,
      )
  
      relu = network.layer("relu")
      self.assertEqual(12, np.sum(self.eval(relu.weight)))
      self.assertEqual(4, np.sum(self.eval(relu.bias)))
  
      linear = network.layer("linear")
      self.assertEqual(20, np.sum(self.eval(linear.weight)))
      self.assertEqual(5, np.sum(self.eval(linear.bias)))
  
      softmax = network.layer("softmax")
      self.assertEqual(30, np.sum(self.eval(softmax.weight)))
      self.assertEqual(6, np.sum(self.eval(softmax.bias)))
  
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 4 for calling the DictStorageTestCase obj's underlying member method code with example usage: obj.test_storage_load_dict_using_wrong_names() without return types