code: def _compute_norm_sign_scaling_factor(
      c_cam, alphas, x_world, y, weight, eps=1e-9
  ):
      """Given a solution, adjusts the scale and flip
      Args:
          c_cam: control points in camera coordinates
          alphas: barycentric coordinates of the points
          x_world: Batch of 3-dimensional points of shape `(minibatch, num_points, 3)`.
          y: Batch of 2-dimensional points of shape `(minibatch, num_points, 2)`.
          weights: Batch of non-negative weights of
              shape `(minibatch, num_point)`. `None` means equal weights.
          eps: epsilon to threshold negative `z` values
      """
      # position of reference points in camera coordinates
      x_cam = torch.matmul(alphas, c_cam)
  
      x_cam = x_cam * (
          1.0 - 2.0 * (oputil.wmean(x_cam[..., 2:], weight) < 0).float()
      )
      if torch.any(x_cam[..., 2:] < -eps):
          neg_rate = oputil.wmean(
              (x_cam[..., 2:] < 0).float(), weight, dim=(0, 1)
          ).item()
          warnings.warn(
              "\nEPnP: %2.2f%% points have z<0." % (neg_rate * 100.0)
          )
  
      R, T, s = points_alignment.corresponding_points_alignment(
          x_world, x_cam, weight, estimate_scale=True
      )
      s = s.clamp(eps)
      x_cam = x_cam / s[:, None, None]
      T = T / s[:, None]
      x_w_rotated = torch.matmul(x_world, R) + T[:, None, :]
      err_2d = _reproj_error(x_w_rotated, y, weight)
      err_3d = _algebraic_error(x_w_rotated, x_cam, weight)
  
      return EpnpSolution(x_cam, R, T, err_2d, err_3d)
  
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 7 for calling a global function with example usage: _compute_norm_sign_scaling_factor(c_cam, alphas, x_world, y, weight, eps) and returns: EpnpSolution