class_docstr: Mean and standard deviation of the anomaly scores of normal training data.
docstr: Update the precision-recall curve metric.
code: def update(
      self,
      *args,
      anomaly_scores: Tensor | None = None,
      anomaly_maps: Tensor | None = None,
      **kwargs,
  ) -> None:
      """Update the precision-recall curve metric."""
      del args, kwargs  # These variables are not used.
  
      if anomaly_maps is not None:
          self.anomaly_maps.append(anomaly_maps)
      if anomaly_scores is not None:
          self.anomaly_scores.append(anomaly_scores)
  
file_path: anomalib/src/anomalib/utils/metrics/anomaly_score_distribution.py
imports: from __future__ import annotations
  import torch
  from torch import Tensor
  from torchmetrics import Metric
label_desc: Use image node_id 2 for calling the AnomalyScoreDistribution obj's underlying member method code with example usage: obj.update() without return types