code: def _transform(
      self, inpt: proto_tv_tensors.Label, params: Dict[str, Any]
  ) -> proto_tv_tensors.OneHotLabel:
      num_categories = self.num_categories
      if num_categories == -1 and inpt.categories is not None:
          num_categories = len(inpt.categories)
      output = one_hot(
          inpt.as_subclass(torch.Tensor), num_classes=num_categories
      )
      return proto_tv_tensors.OneHotLabel(
          output, categories=inpt.categories
      )
  
file_path: vision/torchvision/prototype/transforms/_type_conversion.py
imports: from typing import Any, Dict
  import torch
  from torch.nn.functional import one_hot
  from torchvision.prototype import tv_tensors
  from torchvision.transforms.v2 import Transform
label_desc: Use image node_id 2 for calling the LabelToOneHot obj's underlying member method code with example usage: obj._transform(inpt, params) and returns: proto_tv_tensors