code: def test_norm(self):
      def f(t, x):
          return x
  
      t = torch.tensor([0.0, 1.0])
  
      # First test that tensor input appears in the norm.
      is_called = False
  
      def norm(state):
          nonlocal is_called
          is_called = True
          self.assertIsInstance(state, torch.Tensor)
          self.assertEqual(state.shape, ())
          return state.pow(2).mean().sqrt()
  
      x0 = torch.tensor(1.0)
      torchdiffeq.odeint(f, x0, t, options=dict(norm=norm))
      self.assertTrue(is_called)
  
      # Now test that tupled input appears in the norm
      is_called = False
  
      def norm(state):
          nonlocal is_called
          is_called = True
          self.assertIsInstance(state, tuple)
          self.assertEqual(len(state), 1)
          (state,) = state
          self.assertEqual(state.shape, ())
          return state.pow(2).mean().sqrt()
  
      x0 = (torch.tensor(1.0),)
      torchdiffeq.odeint(f, x0, t, options=dict(norm=norm))
      self.assertTrue(is_called)
  
      is_called = False
  
      def norm(state):
          nonlocal is_called
          is_called = True
          self.assertIsInstance(state, tuple)
          self.assertEqual(len(state), 2)
          state1, state2 = state
          self.assertEqual(state1.shape, ())
          self.assertEqual(state2.shape, (2, 2))
          return state1.pow(2).mean().sqrt()
  
      x0 = (torch.tensor(1.0), torch.tensor([[0.5, 0.5], [0.1, 0.1]]))
      torchdiffeq.odeint(f, x0, t, options=dict(norm=norm))
      self.assertTrue(is_called)
  
file_path: torchdiffeq/tests/norm_tests.py
imports: import contextlib
  import unittest
  import torch
  import torchdiffeq
  from problems import DTYPES, DEVICES, ADAPTIVE_METHODS
label_desc: Use image node_id 1 for calling the TestNorms obj's underlying member method code with example usage: obj.test_norm() and returns: x, state, state, state1