code: def reproj_error(x_world, y, R, T, weight=None):
      # applies the affine transform, projects, and computes the reprojection error
      y_hat = torch.matmul(x_world, R) + T[:, None, :]
      y_hat = y_hat / y_hat[..., 2:]
      if weight is None:
          weight = y.new_ones((1, 1))
      return (
          ((weight[:, :, None] * (y - y_hat[..., :2])) ** 2).sum(dim=-1)
          ** 0.5
      ).mean(dim=-1)
  
file_path: pytorch3d/tests/test_perspective_n_points.py
imports: import unittest
  import torch
  from pytorch3d.ops import perspective_n_points
  from pytorch3d.transforms import rotation_conversions
  from .common_testing import TestCaseMixin
label_desc: Use image node_id 1 for calling a global function with example usage: reproj_error(x_world, y, R, T, weight) and returns: unknown