code: def test_large_norm(self):
      def norm(tensor):
          return tensor.abs().max()
  
      def large_norm(tensor):
          return 10 * tensor.abs().max()
  
      for dtype in DTYPES:
          for device in DEVICES:
              for method in ADAPTIVE_METHODS:
                  if dtype == torch.float32 and method == "dopri8":
                      continue
  
                  with self.subTest(
                      dtype=dtype, device=device, method=method
                  ):
                      x0 = torch.tensor(
                          [1.0, 2.0], device=device, dtype=dtype
                      )
                      t = torch.tensor(
                          [0.0, 1.0], device=device, dtype=torch.float64
                      )
  
                      norm_f = _NeuralF(width=10, oscillate=True).to(
                          device, dtype
                      )
                      torchdiffeq.odeint(
                          norm_f,
                          x0,
                          t,
                          method=method,
                          options=dict(norm=norm),
                      )
                      large_norm_f = _NeuralF(
                          width=10, oscillate=True
                      ).to(device, dtype)
                      with torch.no_grad():
                          for norm_param, large_norm_param in zip(
                              norm_f.parameters(),
                              large_norm_f.parameters(),
                          ):
                              large_norm_param.copy_(norm_param)
                      torchdiffeq.odeint(
                          large_norm_f,
                          x0,
                          t,
                          method=method,
                          options=dict(norm=large_norm),
                      )
  
                      self.assertLessEqual(norm_f.nfe, large_norm_f.nfe)
  
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 3 for calling the TestNorms obj's underlying member method code with example usage: obj.test_large_norm() and returns: tensor, unknown