class_docstr: Anomaly Score Threshold.
  
  This class computes/stores the threshold that determines the anomalous label
  given anomaly scores. If the threshold method is ``manual``, the class only
  stores the manual threshold values.
  
  If the threshold method is ``adaptive``, the class initially computes the
  adaptive threshold to find the optimal f1_score and stores the computed
  adaptive threshold value.
code: def __init__(self, default_value: float = 0.5, **kwargs) -> None:
      super().__init__(num_classes=1, **kwargs)
  
      self.add_state(
          "value", default=torch.tensor(default_value), persistent=True
      )  # pylint: disable=not-callable
      self.value = torch.tensor(default_value)
  
file_path: anomalib/src/anomalib/utils/metrics/anomaly_score_threshold.py
imports: from __future__ import annotations
  import warnings
  import torch
  from torch import Tensor
  from torchmetrics import PrecisionRecallCurve
label_desc: Use image node_id 1 to create a new AnomalyScoreThreshold object from inherited base classes: PrecisionRecallCurve with example: obj = AnomalyScoreThreshold(default_value)