code: def maybe_convert_to_one_hot(
      target: torch.Tensor, model_output: torch.Tensor
  ) -> torch.Tensor:
      """
      This function infers whether target is integer or 0/1 encoded
      and converts it to 0/1 encoding if necessary.
      """
      target_shape_list = list(target.size())
  
      if len(target_shape_list) == 1 or (
          len(target_shape_list) == 2 and target_shape_list[1] == 1
      ):
          target = convert_to_one_hot(
              target.view(-1, 1), model_output.shape[1]
          )
  
      # target are not necessarily hard 0/1 encoding. It can be soft
      # (i.e. fractional) in some cases, such as mixup label
      assert (
          target.shape == model_output.shape
      ), "Target must of the same shape as model_output."
  
      return target
  
imports: import collections.abc
  import contextlib
  import json
  import logging
  import os
  import time
  from functools import partial
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  from classy_vision.generic.distributed_util import broadcast_object, is_primary
  from fvcore.common.file_io import PathManager
label_desc: Use image node_id 25 for calling a global function with example usage: maybe_convert_to_one_hot(target, model_output) and returns: target