code: def test__transform(self):
      categories = ["apple", "pear", "pineapple"]
      labels = tv_tensors.Label(
          torch.tensor([0, 1, 2, 1]), categories=categories
      )
      transform = transforms.LabelToOneHot()
      ohe_labels = transform(labels)
      assert isinstance(ohe_labels, tv_tensors.OneHotLabel)
      assert ohe_labels.shape == (4, 3)
      assert ohe_labels.categories == labels.categories == categories
  
imports: import collections.abc
  import re
  import PIL.Image
  import pytest
  import torch
  from common_utils import assert_equal, make_bounding_boxes, make_detection_masks, make_image, make_video
  from torchvision.prototype import transforms, tv_tensors
  from torchvision.transforms.v2._utils import check_type, is_pure_tensor
  from torchvision.transforms.v2.functional import clamp_bounding_boxes, InterpolationMode, pil_to_tensor, to_pil_image
  from torchvision.tv_tensors import BoundingBoxes, BoundingBoxFormat, Image, Mask, Video
  import importlib.machinery
  import importlib.util
  from pathlib import Path
label_desc: Use image node_id 1 for calling the TestLabelToOneHot obj's underlying member method code with example usage: obj.test__transform() without return types