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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docstr: Calls :func:`torch.Tensor.to` on each tensor to move the keypoints, descriptors and detection scores to
  the specified device and/or data type.
  
  Args:
      *args: Arguments passed to :func:`torch.Tensor.to`.
      **kwargs: Keyword arguments passed to :func:`torch.Tensor.to`.
  
  Returns:
      A new DISKFeatures object with tensors of appropriate type and location.
docstr_tok: ["Calls",":","func",":","`","torch.Tensor.to","`","on","each","tensor","to","move","the","keypoints",",","descriptors","and","detection","scores","to","the","specified","device","and\/or","data","type",".","Args",":","*","args",":","Arguments","passed","to",":","func",":","`","torch.Tensor.to","`",".","*","*","kwargs",":","Keyword","arguments","passed","to",":","func",":","`","torch.Tensor.to","`",".","Returns",":","A","new","DISKFeatures","object","with","tensors","of","appropriate","type","and","location","."]
code: def to(self, *args: Any, **kwargs: Any) -> DISKFeatures:
      """Calls :func:`torch.Tensor.to` on each tensor to move the keypoints, descriptors and detection scores to
      the specified device and/or data type.
  
      Args:
          *args: Arguments passed to :func:`torch.Tensor.to`.
          **kwargs: Keyword arguments passed to :func:`torch.Tensor.to`.
  
      Returns:
          A new DISKFeatures object with tensors of appropriate type and location.
      """
      return DISKFeatures(
          self.keypoints.to(*args, **kwargs),
          self.descriptors.to(*args, **kwargs),
          self.detection_scores.to(*args, **kwargs),
      )
  
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 5 for calling the DISKFeatures obj's underlying member method code with example usage: obj.to() and returns: DISKFeatures