code: def test_jacobian_loss_wrong_reduction_sample_wise_trick(
      self,
  ) -> None:
      model = BasicLinearModel2(5, 2)
      model.linear.weight = nn.Parameter(
          torch.arange(0, 10).view(2, 5).float()
      )
      loss_fn = nn.MSELoss(reduction="none")
  
      a = torch.stack((torch.ones(5), torch.ones(5) * 2))
      label = torch.Tensor([[9, 38], [18, 74]])
  
      with self.assertRaises(AssertionError):
          _compute_jacobian_wrt_params_with_sample_wise_trick(
              model, (a,), label, loss_fn
          )
  
imports: import torch
  import torch.nn
  from captum._utils.gradient import _compute_jacobian_wrt_params, _compute_jacobian_wrt_params_with_sample_wise_trick
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicLinearModel2, BasicLinearModel_Multilayer
label_desc: Use image node_id 19 for calling the Test obj's underlying member method code with example usage: obj.test_jacobian_loss_wrong_reduction_sample_wise_trick() without return types