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: Compute and save anomaly scores of the test set.
  
  Args:
      outputs: Batch of outputs from the validation step
docstr_tok: ["Compute","and","save","anomaly","scores","of","the","test","set",".","Args",":","outputs",":","Batch","of","outputs","from","the","validation","step"]
code: def test_epoch_end(self, outputs: EPOCH_OUTPUT) -> None:
      """Compute and save anomaly scores of the test set.
  
      Args:
          outputs: Batch of outputs from the validation step
      """
      self._collect_outputs(
          self.image_metrics, self.pixel_metrics, outputs
      )
      self._log_metrics()
  
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 9 for calling the AnomalyModule obj's underlying member method code with example usage: obj.test_epoch_end(outputs) without return types