code: def test_mean_std(self, device, dtype):
      points = torch.tensor(
          [[[0.0, 0.0], [0.0, 2.0], [1.0, 1.0], [1.0, 3.0]]],
          device=device,
          dtype=dtype,
      )
  
      points_norm, _ = epi.normalize_points(points)
      points_std, points_mean = torch.std_mean(points_norm, dim=1)
  
      assert_close(points_mean, torch.zeros_like(points_mean))
      assert (points_std < 2.0).all()
  
imports: from typing import Dict
  import pytest
  import test_common
  import torch
  from torch.autograd import gradcheck
  import kornia.geometry.epipolar
  import kornia.testing
  from kornia.testing import assert_close
label_desc: Use image node_id 3 for calling the TestNormalizePoints obj's underlying member method code with example usage: obj.test_mean_std(device, dtype) without return types