class_docstr: 2D Keypoints containing Nx2 or BxNx2 points.
  
  Args:
      keypoints: Raw tensor or a list of Tensors with the Nx2 coordinates
      raise_if_not_floating_point: will raise if the Tensor isn't float
docstr: Pad a bounding keypoints.
  
  Args:
      padding_size: (B, 4)
docstr_tok: ["Pad","a","bounding","keypoints",".","Args",":","padding_size",":","(","B",",","4",")"]
code: def unpad(self, padding_size: Tensor) -> "Keypoints":
      """Pad a bounding keypoints.
  
      Args:
          padding_size: (B, 4)
      """
      if not (
          len(padding_size.shape) == 2 and padding_size.size(1) == 4
      ):
          raise RuntimeError(
              f"Expected padding_size as (B, 4). Got {padding_size.shape}."
          )
      self._data[..., 0] -= padding_size[..., :1]  # left padding
      self._data[..., 1] -= padding_size[..., 2:3]  # top padding
      return self
  
imports: from typing import List, Optional, Tuple, Union, cast
  import torch
  from torch import Size
  from kornia.core import Tensor
  from kornia.geometry import transform_points
label_desc: Use image node_id 10 for calling the Keypoints obj's underlying member method code with example usage: obj.unpad(padding_size) and returns: self