code: def create_diag_sym3x3(device, n, noise=0.0):
      # Create purly diagonal matrices
      random_diag_3x3 = torch.randn((n, 3), device=device).diag_embed()
  
      # Make them 'almost' diagonal
      random_diag_3x3 += noise * TestSymEig3x3.create_random_sym3x3(
          device, n
      )
  
      return random_diag_3x3
  
imports: import unittest
  import torch
  from pytorch3d.common.workaround import symeig3x3
  from pytorch3d.transforms.rotation_conversions import random_rotations
  from .common_testing import get_random_cuda_device, TestCaseMixin
label_desc: Use image node_id 2 for calling the TestSymEig3x3 obj's underlying member method code with example usage: obj.create_diag_sym3x3(device, n, noise) and returns: random_diag_3x3