code: def _find_null_space_coords_2(kernel_dsts, cw_dst):
      """Solves case 2 from the paper; solve for 3 coefficients:
          [B11 B22 B33 B44 B12 B13 B14 B23 B24 B34]
            ^   ^           ^
      Args:
          kernel_dsts: distances between kernel vectors
          cw_dst: distances between control points
      Returns:
          coefficients to weight kernel vectors
      [1] Moreno-Noguer, F., Lepetit, V., & Fua, P. (2009).
      EPnP: An Accurate O(n) solution to the PnP problem.
      International Journal of Computer Vision.
      https://www.epfl.ch/labs/cvlab/software/multi-view-stereo/epnp/
      """
      beta = _solve_lstsq_subcols(cw_dst, kernel_dsts, [0, 4, 1])
  
      coord_0 = (beta[:, :1, :].abs() ** 0.5) * _binary_sign(
          beta[:, 1:2, :]
      )
      coord_1 = (beta[:, 2:3, :].abs() ** 0.5) * (
          (beta[:, :1, :] >= 0) == (beta[:, 2:3, :] >= 0)
      ).float()
  
      return torch.cat(
          (coord_0, coord_1, torch.zeros_like(beta[:, :2, :])), dim=1
      )
  
imports: import warnings
  from typing import NamedTuple, Optional
  import torch
  import torch.nn.functional
  from pytorch3d.ops import points_alignment, utils
label_desc: Use image node_id 13 for calling a global function with example usage: _find_null_space_coords_2(kernel_dsts, cw_dst) and returns: torch