code: def predict_segmentation(
      logits: torch.Tensor,
      mutually_exclusive: bool = False,
      threshold: float = 0.0,
  ) -> Any:
      """
      Given the logits from a network, computing the segmentation by thresholding all values above 0
      if multi-labels task, computing the `argmax` along the channel axis if multi-classes task,
      logits has shape `BCHW[D]`.
  
      Args:
          logits: raw data of model output.
          mutually_exclusive: if True, `logits` will be converted into a binary matrix using
              a combination of argmax, which is suitable for multi-classes task. Defaults to False.
          threshold: thresholding the prediction values if multi-labels task.
      """
      if not mutually_exclusive:
          return (logits >= threshold).int()
      if logits.shape[1] == 1:
          warnings.warn(
              "single channel prediction, `mutually_exclusive=True` ignored, use threshold instead."
          )
          return (logits >= threshold).int()
      return logits.argmax(1, keepdim=True)
  
imports: from __future__ import annotations
  import io
  import re
  import warnings
  from collections import OrderedDict
  from collections.abc import Callable, Mapping, Sequence
  from contextlib import contextmanager
  from copy import deepcopy
  from typing import Any
  import numpy
  import torch
  import torch.nn
  from monai.apps.utils import get_logger
  from monai.config import PathLike
  from monai.utils.misc import ensure_tuple, save_obj, set_determinism
  from monai.utils.module import look_up_option, optional_import, pytorch_after
  from monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor
label_desc: Use image node_id 5 for calling a global function with example usage: predict_segmentation(logits, mutually_exclusive, threshold) and returns: logits, unknown, unknown