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.
code: def __init__(
      self,
      include_background: bool = False,
      symmetric: bool = False,
      distance_metric: str = "euclidean",
      reduction: MetricReduction | str = MetricReduction.MEAN,
      get_not_nans: bool = False,
  ) -> None:
      super().__init__()
      self.include_background = include_background
      self.distance_metric = distance_metric
      self.symmetric = symmetric
      self.reduction = reduction
      self.get_not_nans = get_not_nans
  
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 1 to create a new SurfaceDistanceMetric object from inherited base classes: CumulativeIterationMetric with example: obj = SurfaceDistanceMetric(include_background, symmetric, distance_metric, reduction, get_not_nans)