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","."]
code: def __init__(self) -> None:
      super().__init__()
      logger.info("Initializing %s model.", self.__class__.__name__)
  
      self.save_hyperparameters()
      self.model: nn.Module
      self.loss: nn.Module
      self.callbacks: list[Callback]
  
      self.threshold_method: ThresholdMethod
      self.image_threshold = AnomalyScoreThreshold().cpu()
      self.pixel_threshold = AnomalyScoreThreshold().cpu()
  
      self.normalization_metrics: Metric
  
      self.image_metrics: AnomalibMetricCollection
      self.pixel_metrics: AnomalibMetricCollection
  
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 1 to create a new AnomalyModule object from inherited base classes: pl, ABC with example: obj = AnomalyModule()