class_docstr: Mean and standard deviation of the anomaly scores of normal training data.
code: def __init__(self, **kwargs) -> None:
      super().__init__(**kwargs)
      self.anomaly_maps: list[Tensor] = []
      self.anomaly_scores: list[Tensor] = []
  
      self.add_state("image_mean", torch.empty(0), persistent=True)
      self.add_state("image_std", torch.empty(0), persistent=True)
      self.add_state("pixel_mean", torch.empty(0), persistent=True)
      self.add_state("pixel_std", torch.empty(0), persistent=True)
  
      self.image_mean = torch.empty(0)
      self.image_std = torch.empty(0)
      self.pixel_mean = torch.empty(0)
      self.pixel_std = torch.empty(0)
  
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 1 to create a new AnomalyScoreDistribution object from inherited base classes: Metric with example: obj = AnomalyScoreDistribution()