code: def test_gradient_based_loss():
      # Tests https://github.com/pytorch/ignite/issues/1674
      x = torch.tensor(
          [[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]], requires_grad=True
      )
      y_pred = x.mm(torch.randn(size=(3, 1)))
  
      def loss_fn(y_pred, x):
          gradients = torch.autograd.grad(
              outputs=y_pred,
              inputs=x,
              grad_outputs=torch.ones_like(y_pred),
              create_graph=True,
          )[0]
  
          gradients = gradients.flatten(start_dim=1)
  
          return gradients.norm(2, dim=1).mean()
  
      loss = Loss(loss_fn)
      loss.update((y_pred, x))
  
imports: import os
  from unittest.mock import MagicMock
  import pytest
  import torch
  from numpy.testing import assert_almost_equal
  from torch import nn
  from torch.nn.functional import nll_loss
  import ignite.distributed
  from ignite.engine import State
  from ignite.exceptions import NotComputableError
  from ignite.metrics import Loss, Precision
label_desc: Use image node_id 9 for calling a global function with example usage: test_gradient_based_loss() and returns: gradients