code: def inject_ignore_index(x: Tensor, ignore_index: int) -> Tensor:
      """Injecting the ignored index value into a tensor randomly."""
      if any(
          x.flatten() == ignore_index
      ):  # ignore index is a class label
          return x
      classes = torch.unique(x)
      idx = torch.randperm(x.numel())
      x = deepcopy(x)
      # randomly set either element {9, 10} to ignore index value
      skip = torch.randint(9, 11, (1,)).item()
      x.view(-1)[idx[::skip]] = ignore_index
      # if we accidentally removed a class completely in a batch, reintroduce it again
      for batch in x:
          new_classes = torch.unique(batch)
          class_not_in = [c not in new_classes for c in classes]
          if any(class_not_in):
              missing_class = int(np.where(class_not_in)[0][0])
              batch[
                  torch.where(batch == ignore_index)[0][0]
              ] = missing_class
      return x
  
imports: import pickle
  import sys
  from copy import deepcopy
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import pytest
  import torch
  from lightning_utilities import apply_to_collection
  from torch import Tensor, tensor
  from torchmetrics import Metric
  from torchmetrics.utilities.data import _flatten
  from unittests import NUM_PROCESSES
label_desc: Use image node_id 7 for calling a global function with example usage: inject_ignore_index(x, ignore_index) and returns: x, x