code: def test_dryrun_find_featuremap_dims(
      backbone: str, input_size: Tuple[int, int]
  ):
      """Use the function and check the expected output format."""
      layers = ["layer1", "layer2", "layer3"]
      model = FeatureExtractor(
          backbone=backbone, layers=layers, pre_trained=True
      )
      mapping = dryrun_find_featuremap_dims(model, input_size, layers)
      for lay in layers:
          layer_mapping = mapping[lay]
          num_features = layer_mapping["num_features"]
          assert isinstance(num_features, int), f"{type(num_features)}"
          resolution = layer_mapping["resolution"]
          assert isinstance(resolution, tuple), f"{type(resolution)}"
          assert len(resolution) == len(
              input_size
          ), f"{len(resolution)}, {len(input_size)}"
          assert all(
              isinstance(x, int) for x in resolution
          ), f"{[type(x) for x in resolution]}"
  
file_path: anomalib/tests/pre_merge/models/test_feature_extractor.py
imports: from tempfile import TemporaryDirectory
  from typing import Tuple
  import pytest
  import torch
  from torchvision.models import ResNet18_Weights, resnet18
  from torchvision.models.efficientnet import EfficientNet_B5_Weights
  from anomalib.models.components.feature_extractors import BackboneParams, FeatureExtractor, TorchFXFeatureExtractor, dryrun_find_featuremap_dims
  from tests.helpers.dummy import DummyModel
label_desc: Use image node_id 1 for calling a global function with example usage: test_dryrun_find_featuremap_dims(backbone, input_size) without return types