class_docstr: AnomalyModule to train, validate, predict and test images.
  
  Acts as a base class for all the Anomaly Modules in the library.
class_docstr_tok: ["AnomalyModule","to","train",",","validate",",","predict","and","test","images",".","Acts","as","a","base","class","for","all","the","Anomaly","Modules","in","the","library","."]
docstr: Assigns the normalization method to use.
docstr_tok: ["Assigns","the","normalization","method","to","use","."]
code: def _load_normalization_class(
      self, state_dict: OrderedDict[str, Tensor]
  ) -> None:
      """Assigns the normalization method to use."""
      if "normalization_metrics.max" in state_dict.keys():
          self.normalization_metrics = MinMax()
      elif "normalization_metrics.image_mean" in state_dict.keys():
          self.normalization_metrics = AnomalyScoreDistribution()
      else:
          warn("No known normalization found in model weights.")
  
imports: from __future__ import annotations
  import importlib
  import logging
  from abc import ABC
  from typing import Any, OrderedDict
  from warnings import warn
  import pytorch_lightning
  import torch
  from pytorch_lightning.callbacks import Callback
  from pytorch_lightning.utilities.types import EPOCH_OUTPUT, STEP_OUTPUT
  from torch import Tensor, nn
  from torchmetrics import Metric
  from anomalib.data.utils import boxes_to_anomaly_maps, boxes_to_masks, masks_to_boxes
  from anomalib.post_processing import ThresholdMethod
  from anomalib.utils.metrics import AnomalibMetricCollection, AnomalyScoreDistribution, AnomalyScoreThreshold, MinMax
label_desc: Use image node_id 15 for calling the AnomalyModule obj's underlying member method code with example usage: obj._load_normalization_class(state_dict) without return types