class_docstr: A data structure holding DISK keypoints, descriptors and detection scores for an image. Since DISK detects a
  varying number of keypoints per image, `DISKFeatures` is not batched.
  
  Args:
      keypoints: Tensor of shape :math:`(N, 2)`, where :math:`N` is the number of keypoints.
      descriptors: Tensor of shape :math:`(N, D)`, where :math:`D` is the descriptor dimension.
      detection_scores: Tensor of shape :math:`(N,)` where the detection score can be interpreted as
                        the log-probability of keeping a keypoint after it has been proposed (see the paper
                        section *Method → Feature distribution* for details).
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code: def device(self) -> Device:
      return self.keypoints.device
  
file_path: kornia/kornia/feature/disk/structs.py
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import Any
  import torch.nn.functional
  from kornia.core import Device, Tensor
label_desc: Use image node_id 2 for calling the DISKFeatures obj's underlying member method code with example usage: obj.device() and returns: self