class_docstr: A model in which the layers not defined in order of execution.
class_docstr_tok: ["A","model","in","which","the","layers","not","defined","in","order","of","execution","."]
code: def forward(self, x, y):
      out1 = self.layer1(x)
      out2 = self.layer2(y)
      out = self.relu(torch.cat((out1, out2), 1))
      out = self.combine(out)
      return out
  
imports: from collections import OrderedDict
  from typing import Any
  import pytest
  import torch
  import torch.nn
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
  from lightning.pytorch import LightningModule, Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.utilities.model_summary.model_summary import LEFTOVER_PARAMS_NAME, NOT_APPLICABLE, UNKNOWN_SIZE, ModelSummary, summarize
  from tests_pytorch.helpers.advanced_models import ParityModuleRNN
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 2 for calling the UnorderedModel obj's underlying member method code with example usage: obj.forward(x, y) and returns: out