code: def do_metric_reduction(
      f: torch.Tensor,
      reduction: MetricReduction | str = MetricReduction.MEAN,
  ) -> tuple[torch.Tensor | Any, torch.Tensor]:
      """
      This function is to do the metric reduction for calculated `not-nan` metrics of each sample's each class.
      The function also returns `not_nans`, which counts the number of not nans for the metric.
  
      Args:
          f: a tensor that contains the calculated metric scores per batch and
              per class. The first two dims should be batch and class.
          reduction: define the mode to reduce 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", return the input f tensor and not_nans.
  
      Raises:
          ValueError: When ``reduction`` is not one of
              ["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].
      """
  
      # some elements might be Nan (if ground truth y was missing (zeros))
      # we need to account for it
      nans = torch.isnan(f)
      not_nans = ~nans
  
      t_zero = torch.zeros(1, device=f.device, dtype=torch.float)
      reduction = look_up_option(reduction, MetricReduction)
      if reduction == MetricReduction.NONE:
          return f, not_nans.float()
  
      f[nans] = 0
      if reduction == MetricReduction.MEAN:
          # 2 steps, first, mean by channel (accounting for nans), then by batch
          not_nans = not_nans.sum(dim=1).float()
          f = torch.where(
              not_nans > 0, f.sum(dim=1).float() / not_nans, t_zero
          )  # channel average
  
          not_nans = (not_nans > 0).sum(dim=0).float()
          f = torch.where(
              not_nans > 0, f.sum(dim=0).float() / not_nans, t_zero
          )  # batch average
  
      elif reduction == MetricReduction.SUM:
          not_nans = not_nans.sum(dim=[0, 1]).float()
          f = torch.sum(
              f, dim=[0, 1]
          )  # sum over the batch and channel dims
      elif reduction == MetricReduction.MEAN_BATCH:
          not_nans = not_nans.sum(dim=0).float()
          f = torch.where(
              not_nans > 0, f.sum(dim=0).float() / not_nans, t_zero
          )  # batch average
      elif reduction == MetricReduction.SUM_BATCH:
          not_nans = not_nans.sum(dim=0).float()
          f = f.sum(dim=0).float()  # the batch sum
      elif reduction == MetricReduction.MEAN_CHANNEL:
          not_nans = not_nans.sum(dim=1).float()
          f = torch.where(
              not_nans > 0, f.sum(dim=1).float() / not_nans, t_zero
          )  # channel average
      elif reduction == MetricReduction.SUM_CHANNEL:
          not_nans = not_nans.sum(dim=1).float()
          f = f.sum(dim=1).float()  # the channel sum
      elif reduction != MetricReduction.NONE:
          raise ValueError(
              f"Unsupported reduction: {reduction}, available options are "
              '["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].'
          )
      return f, not_nans
  
imports: from __future__ import annotations
  import warnings
  from functools import lru_cache, partial
  from types import ModuleType
  from typing import Any, Iterable, Sequence
  import numpy
  import torch
  from monai.config import NdarrayOrTensor, NdarrayTensor
  from monai.transforms.croppad.dictionary import CropForegroundD
  from monai.transforms.utils import distance_transform_edt
  from monai.utils import MetricReduction, convert_to_cupy, convert_to_dst_type, convert_to_numpy, convert_to_tensor, deprecated_arg, deprecated_arg_default, ensure_tuple_rep, look_up_option, optional_import
label_desc: Use image node_id 2 for calling a global function with example usage: do_metric_reduction(f, reduction) and returns: f,  not_nans, f,  not_nans