class_docstr: Area under per region overlap (AUPRO) Metric.
class_docstr_tok: ["Area","under","per","region","overlap","(","AUPRO",")","Metric","."]
docstr: Update state with new values.
  
  Args:
      preds (Tensor): predictions of the model
      target (Tensor): ground truth targets
docstr_tok: ["Update","state","with","new","values",".","Args",":","preds","(","Tensor",")",":","predictions","of","the","model","target","(","Tensor",")",":","ground","truth","targets"]
code: def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
      """Update state with new values.
  
      Args:
          preds (Tensor): predictions of the model
          target (Tensor): ground truth targets
      """
      self.target.append(target)
      self.preds.append(preds)
  
imports: from __future__ import annotations
  from typing import Any, Callable
  import torch
  from matplotlib.figure import Figure
  from torch import Tensor
  from torchmetrics import Metric
  from torchmetrics.functional import auc
  from torchmetrics.functional.classification import binary_roc
  from torchmetrics.utilities.data import dim_zero_cat
  from anomalib.utils.metrics.pro import connected_components_cpu, connected_components_gpu
  from .binning import thresholds_between_0_and_1, thresholds_between_min_and_max
  from .plotting_utils import plot_figure
label_desc: Use image node_id 2 for calling the AUPRO obj's underlying member method code with example usage: obj.update(preds, target) without return types