code: def test_normalizer(path=get_dataset_path(), category="shapes"):
      config = get_configurable_parameters(
          config_path="src/anomalib/models/padim/config.yaml"
      )
      config.dataset.path = path
      config.dataset.category = category
      config.metrics.threshold.method = "adaptive"
      config.project.log_images_to = []
      config.metrics.image = ["F1Score", "AUROC"]
  
      # run without normalization
      config.model.normalization_method = "none"
      seed_everything(42)
      results_without_normalization = run_train_test(config)
  
      # run with cdf normalization
      config.model.normalization_method = "cdf"
      seed_everything(42)
      results_with_cdf_normalization = run_train_test(config)
  
      # run without normalization
      config.model.normalization_method = "min_max"
      seed_everything(42)
      results_with_minmax_normalization = run_train_test(config)
  
      # performance should be the same
      for metric in ["image_AUROC", "image_F1Score"]:
          assert round(
              results_without_normalization[0][metric], 3
          ) == round(results_with_cdf_normalization[0][metric], 3)
          assert round(
              results_without_normalization[0][metric], 3
          ) == round(results_with_minmax_normalization[0][metric], 3)
  
file_path: anomalib/tests/pre_merge/utils/callbacks/normalization_callback/test_normalization_callback.py
imports: from pytorch_lightning import Trainer, seed_everything
  from anomalib.config import get_configurable_parameters
  from anomalib.data import get_datamodule
  from anomalib.models import get_model
  from anomalib.utils.callbacks import get_callbacks
  from tests.helpers.dataset import TestDataset, get_dataset_path
label_desc: Use image node_id 2 for calling a global function with example usage: test_normalizer(path, category) without return types