code: def right_to_left_epipolar_distance(
      pts1: Tensor, pts2: Tensor, Fm: Tensor
  ) -> Tensor:
      r"""Return one-sided epipolar distance for correspondences given the fundamental matrix.
  
      This method measures the distance from points in the left images to the epilines
      of the corresponding points in the right images as they reflect in the left images.
  
      Args:
         pts1: correspondences from the left images with shape
           :math:`(*, N, 2 or 3)`. If they are not homogeneous, converted automatically.
         pts2: correspondences from the right images with shape
           :math:`(*, N, 2 or 3)`. If they are not homogeneous, converted automatically.
         Fm: Fundamental matrices with shape :math:`(*, 3, 3)`. Called Fm to
           avoid ambiguity with torch.nn.functional.
  
      Returns:
          the computed Symmetrical distance with shape :math:`(*, N)`.
      """
      KORNIA_CHECK_IS_TENSOR(pts1)
      KORNIA_CHECK_IS_TENSOR(pts2)
      KORNIA_CHECK_IS_TENSOR(Fm)
  
      if (len(Fm.shape) < 3) or not Fm.shape[-2:] == (3, 3):
          raise ValueError(
              f"Fm must be a (*, 3, 3) tensor. Got {Fm.shape}"
          )
  
      if pts2.shape[-1] == 2:
          pts2 = convert_points_to_homogeneous(pts2)
  
      line2_in_1: Tensor = pts2 @ Fm
  
      return point_line_distance(pts1, line2_in_1)
  
file_path: kornia/kornia/geometry/epipolar/_metrics.py
imports: from torch import Tensor
  from kornia.core.check import KORNIA_CHECK_IS_TENSOR
  from kornia.geometry.conversions import convert_points_to_homogeneous
  from kornia.geometry.linalg import point_line_distance
label_desc: Use image node_id 4 for calling a global function with example usage: right_to_left_epipolar_distance(pts1, pts2, Fm) and returns: point_line_distance