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 __init__(self):
      super().__init__()
      # note: the definition order is intentionally scrambled for this test
      self.layer2 = nn.Linear(10, 2)
      self.combine = nn.Linear(7, 9)
      self.layer1 = nn.Linear(3, 5)
      self.relu = nn.ReLU()
      # this layer is unused, therefore input-/output shapes are unknown
      self.unused = nn.Conv2d(1, 1, 1)
  
      self.example_input_array = (torch.rand(2, 3), torch.rand(2, 10))
  
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 1 to create a new UnorderedModel object from inherited base classes: LightningModule with example: obj = UnorderedModel()