code: def one_hot(
      index: Tensor,
      num_classes: Optional[int] = None,
      dtype: Optional[torch.dtype] = None,
  ) -> Tensor:
      r"""Taskes a one-dimensional :obj:`index` tensor and returns a one-hot
      encoded representation of it with shape :obj:`[*, num_classes]` that has
      zeros everywhere except where the index of last dimension matches the
      corresponding value of the input tensor, in which case it will be :obj:`1`.
  
      .. note::
          This is a more memory-efficient version of
          :meth:`torch.nn.functional.one_hot` as you can customize the output
          :obj:`dtype`.
  
      Args:
          index (torch.Tensor): The one-dimensional input tensor.
          num_classes (int, optional): The total number of classes. If set to
              :obj:`None`, the number of classes will be inferred as one greater
              than the largest class value in the input tensor.
              (default: :obj:`None`)
          dtype (torch.dtype, optional): The :obj:`dtype` of the output tensor.
      """
      if index.dim() != 1:
          raise ValueError("'index' tensor needs to be one-dimensional")
  
      if num_classes is None:
          num_classes = int(index.max()) + 1
  
      out = torch.zeros(
          (index.size(0), num_classes), dtype=dtype, device=index.device
      )
      return out.scatter_(1, index.unsqueeze(1), 1)
  
label_desc: Use image node_id 1 for calling a global function with example usage: one_hot(index, num_classes, dtype) and returns: out