code: def one_hot(
      labels: Tensor,
      num_classes: int,
      device: torch.device,
      dtype: torch.dtype,
      eps: float = 1e-6,
  ) -> Tensor:
      r"""Convert an integer label x-D tensor to a one-hot (x+1)-D tensor.
  
      Args:
          labels: tensor with labels of shape :math:`(N, *)`, where N is batch size.
            Each value is an integer representing correct classification.
          num_classes: number of classes in labels.
          device: the desired device of returned tensor.
          dtype: the desired data type of returned tensor.
  
      Returns:
          the labels in one hot tensor of shape :math:`(N, C, *)`,
  
      Examples:
          >>> labels = torch.LongTensor([[[0, 1], [2, 0]]])
          >>> one_hot(labels, num_classes=3, device=torch.device('cpu'), dtype=torch.int64)
          tensor([[[[1.0000e+00, 1.0000e-06],
                    [1.0000e-06, 1.0000e+00]],
          <BLANKLINE>
                   [[1.0000e-06, 1.0000e+00],
                    [1.0000e-06, 1.0000e-06]],
          <BLANKLINE>
                   [[1.0000e-06, 1.0000e-06],
                    [1.0000e+00, 1.0000e-06]]]])
      """
      if not isinstance(labels, Tensor):
          raise TypeError(
              f"Input labels type is not a Tensor. Got {type(labels)}"
          )
  
      if not labels.dtype == torch.int64:
          raise ValueError(
              f"labels must be of the same dtype torch.int64. Got: {labels.dtype}"
          )
  
      if num_classes < 1:
          raise ValueError(
              f"The number of classes must be bigger than one. Got: {num_classes}"
          )
  
      shape = labels.shape
      one_hot = zeros(
          (shape[0], num_classes) + shape[1:],
          device=device,
          dtype=dtype,
      )
  
      return one_hot.scatter_(1, labels.unsqueeze(1), 1.0) + eps
  
label_desc: Use image node_id 1 for calling a global function with example usage: one_hot(labels, num_classes, device, dtype, eps) and returns: unknown