class_docstr: The assumption is that the DL model makes better predictions than the provided label quality, hence the difference
  can be treated as a label quality score
  
  It can be combined with variance/uncertainty for active learning frameworks to factor in the quality of label along
  with uncertainty
  Args:
      include_background: Whether to include the background of the spatial image or channel 0 of the 1-D vector
      spatial_map: Boolean, if set to True, spatial map of variance will be returned corresponding to i/p image
      dimensions
      scalar_reduction: reduction type of the metric, either 'sum' or 'mean' can be used
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docstr: Args:
      y_pred: Predicted segmentation, typically segmentation model output.
          It must be N-repeats, repeat-first tensor [N,C,H,W,D].
  
  Returns:
      Pytorch tensor of scalar value of variance as uncertainty or a spatial map of uncertainty
docstr_tok: ["Args",":","y_pred",":","Predicted","segmentation",",","typically","segmentation","model","output",".","It","must","be","N-repeats",",","repeat-first","tensor","[","N",",","C",",","H",",","W",",","D","]",".","Returns",":","Pytorch","tensor","of","scalar","value","of","variance","as","uncertainty","or","a","spatial","map","of","uncertainty"]
code: def __call__(self, y_pred: Any, y: Any) -> torch.Tensor | None:
      """
      Args:
          y_pred: Predicted segmentation, typically segmentation model output.
              It must be N-repeats, repeat-first tensor [N,C,H,W,D].
  
      Returns:
          Pytorch tensor of scalar value of variance as uncertainty or a spatial map of uncertainty
  
      """
      return label_quality_score(
          y_pred=y_pred,
          y=y,
          include_background=self.include_background,
          scalar_reduction=self.scalar_reduction,
      )
  
file_path: MONAI/monai/metrics/active_learning_metrics.py
imports: from __future__ import annotations
  import warnings
  from typing import Any
  import torch
  from monai.metrics.utils import ignore_background
  from monai.utils import MetricReduction
  from .metric import Metric
label_desc: Use image node_id 2 for calling the LabelQualityScore obj's underlying member method code with example usage: obj.__call__(y_pred, y) and returns: label_quality_score