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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code: def __init__(
      self,
      include_background: bool = True,
      scalar_reduction: str = "sum",
  ) -> None:
      super().__init__()
      self.include_background = include_background
      self.scalar_reduction = 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 1 to create a new LabelQualityScore object from inherited base classes: Metric with example: obj = LabelQualityScore(include_background, scalar_reduction)