code: def load_cifar10_adaptive_lenet_model():
      """Load a pre-trained LeNet classification model (architecture at quantus/helpers/models)."""
      model = LeNetAdaptivePooling(input_shape=(3, 32, 32))
      model.load_state_dict(
          torch.load(
              "tests/assets/cifar10",
              map_location="cpu",
              pickle_module=pickle,
          )
      )
      return model
  
imports: from typing import Union, Optional, Dict
  import pytest
  from pytest_lazyfixture import lazy_fixture
  import numpy
  import torch
  import pickle
  from quantus.functions.explanation_func import explain
  from quantus.functions.mosaic_func import mosaic_creation
  from quantus.helpers.model.model_interface import ModelInterface
  from quantus.helpers.model.models import LeNetAdaptivePooling
  from quantus.metrics.localisation import AttributionLocalisation, AUC, Focus, PointingGame, RelevanceMassAccuracy, RelevanceRankAccuracy, TopKIntersection
label_desc: Use image node_id 24 for calling a global function with example usage: load_cifar10_adaptive_lenet_model() and returns: model