code: def test_from_axis_angle_has_grad(self):
      n_repetitions = 20
      data = torch.rand(n_repetitions, 3, requires_grad=True)
      matrices = axis_angle_to_matrix(data)
      mdata = matrix_to_axis_angle(matrices)
      quats = axis_angle_to_quaternion(data)
      mdata2 = quaternion_to_axis_angle(quats)
      (grad,) = torch.autograd.grad(mdata.sum() + mdata2.sum(), data)
      self.assertTrue(torch.isfinite(grad).all())
  
imports: import itertools
  import math
  import unittest
  from distutils.version import LooseVersion
  from typing import Optional, Union
  import numpy
  import torch
  from pytorch3d.transforms.rotation_conversions import axis_angle_to_matrix, axis_angle_to_quaternion, euler_angles_to_matrix, matrix_to_axis_angle, matrix_to_euler_angles, matrix_to_quaternion, matrix_to_rotation_6d, quaternion_apply, quaternion_multiply, quaternion_to_axis_angle, quaternion_to_matrix, random_quaternions, random_rotation, random_rotations, rotation_6d_to_matrix
  from .common_testing import TestCaseMixin
label_desc: Use image node_id 16 for calling the TestRotationConversion obj's underlying member method code with example usage: obj.test_from_axis_angle_has_grad() without return types