code: def corresponding_cameras_alignment(
      batch_size: int,
      estimate_scale: bool,
      mode: str,
      cam_type=SfMPerspectiveCameras,
  ):
      device = torch.device("cuda:0")
      cameras_src, cameras_tgt = [
          init_random_cameras(cam_type, batch_size, random_z=True).to(
              device
          )
          for _ in range(2)
      ]
  
      torch.cuda.synchronize()
  
      def compute_corresponding_cameras_alignment():
          corresponding_cameras_alignment(
              cameras_src,
              cameras_tgt,
              estimate_scale=estimate_scale,
              mode=mode,
          )
          torch.cuda.synchronize()
  
      return compute_corresponding_cameras_alignment
  
file_path: pytorch3d/tests/test_cameras_alignment.py
imports: import unittest
  import numpy
  import torch
  from pytorch3d.ops import corresponding_cameras_alignment
  from pytorch3d.renderer.cameras import OpenGLOrthographicCameras, OpenGLPerspectiveCameras, SfMOrthographicCameras, SfMPerspectiveCameras
  from pytorch3d.transforms.rotation_conversions import random_rotations
  from pytorch3d.transforms.so3 import so3_exp_map, so3_relative_angle
  from .common_testing import TestCaseMixin
  from .test_cameras import init_random_cameras
label_desc: Use image node_id 4 for calling the TestCamerasAlignment obj's underlying member method code with example usage: obj.corresponding_cameras_alignment(batch_size, estimate_scale, mode, cam_type) and returns: compute_corresponding_cameras_alignment