code: def _multiclass_with_missing_class(
      *shape: Any, num_classes=NUM_CLASSES
  ):
      """Generate multiclass input where a class is missing.
  
      Args:
          shape: shape of the tensor
          num_classes: number of classes
  
      Returns:
          tensor with missing classes
  
      """
      x = torch.randint(0, num_classes, shape)
      x[x == 0] = 2
      return x
  
file_path: torchmetrics/tests/unittests/classification/inputs.py
imports: from typing import Any
  import pytest
  import torch
  from torch import Tensor
  from unittests import BATCH_SIZE, EXTRA_DIM, NUM_BATCHES, NUM_CLASSES, _GroupInput, _Input
  from unittests.helpers import seed_all
label_desc: Use image node_id 3 for calling a global function with example usage: _multiclass_with_missing_class() and returns: x