class_docstr: Compute Surface Distance between two tensors. It can support both multi-classes and multi-labels tasks.
  It supports both symmetric and asymmetric surface distance calculation.
  Input `y_pred` is compared with ground truth `y`.
  `y_preds` is expected to have binarized predictions and `y` should be in one-hot format.
  You can use suitable transforms in ``monai.transforms.post`` first to achieve binarized values.
  `y_preds` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]).
  
  Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
  
  Args:
      include_background: whether to include distance computation on the first channel of
          the predicted output. Defaults to ``False``.
      symmetric: whether to calculate the symmetric average surface distance between
          `seg_pred` and `seg_gt`. Defaults to ``False``.
      distance_metric: : [``"euclidean"``, ``"chessboard"``, ``"taxicab"``]
          the metric used to compute surface distance. Defaults to ``"euclidean"``.
      reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
          available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
          ``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
      get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).
          Here `not_nans` count the number of not nans for the metric, thus its shape equals to the shape of the metric.
docstr: Execute reduction logic for the output of `compute_average_surface_distance`.
  
  Args:
      reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
          available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
          ``"mean_channel"``, ``"sum_channel"``}, default to `self.reduction`. if "none", will not do reduction.
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code: def aggregate(
      self, reduction: MetricReduction | str | None = None
  ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
      """
      Execute reduction logic for the output of `compute_average_surface_distance`.
  
      Args:
          reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
              available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
              ``"mean_channel"``, ``"sum_channel"``}, default to `self.reduction`. if "none", will not do reduction.
  
      """
      data = self.get_buffer()
      if not isinstance(data, torch.Tensor):
          raise ValueError(
              "the data to aggregate must be PyTorch Tensor."
          )
  
      # do metric reduction
      f, not_nans = do_metric_reduction(
          data, reduction or self.reduction
      )
      return (f, not_nans) if self.get_not_nans else f
  
file_path: MONAI/monai/metrics/surface_distance.py
imports: from __future__ import annotations
  from collections.abc import Sequence
  from typing import Any
  import numpy
  import torch
  from monai.metrics.utils import do_metric_reduction, get_edge_surface_distance, ignore_background, prepare_spacing
  from monai.utils import MetricReduction, convert_data_type
  from .metric import CumulativeIterationMetric
label_desc: Use image node_id 3 for calling the SurfaceDistanceMetric obj's underlying member method code with example usage: obj.aggregate(reduction) and returns: unknown