code: def convert_to_one_hot(
      targets: torch.Tensor, classes
  ) -> torch.Tensor:
      """
      This function converts target class indices to one-hot vectors,
      given the number of classes.
  
      """
      assert (
          torch.max(targets).item() < classes
      ), "Class Index must be less than number of classes"
      one_hot_targets = torch.zeros(
          (targets.shape[0], classes),
          dtype=torch.long,
          device=targets.device,
      )
      one_hot_targets.scatter_(1, targets.long(), 1)
      return one_hot_targets
  
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 24 for calling a global function with example usage: convert_to_one_hot(targets, classes) and returns: one_hot_targets