code: def test_foreach_l2_large_value_input(self, device, dtype, op):
      ord, N = 2, 10
      max_value = torch.finfo(dtype).max
      scaler = (
          torch.tensor([max_value])
          .sqrt()
          .to(device=device, dtype=dtype)
      )
      inputs = (
          [
              t * scaler
              for t in next(
                  iter(
                      op.sample_inputs(
                          device,
                          dtype,
                          requries_grad=True,
                          num_input_tensors=[N],
                          low=1,
                      )
                  )
              ).input
          ],
      )
      # make sure that the min. of squared L2 norm value per tensor is greater than the max value of `dtype`.
      self.assertTrue(scaler * scaler * N > max_value)
      fn, ref_fn, *_ = self._get_funcs(op)
      actual = fn(
          inputs,
          is_cuda=True,
          expect_fastpath=True,
          ord=ord,
          zero_size=False,
      )
      expect = ref_fn(inputs, ord=ord)
  
      if dtype == torch.float16:
          # making sure the reference L2 norm values are in the range of FP16.
          self.assertFalse(any(torch.isinf(e) for e in expect))
      else:
          self.assertTrue(
              all(
                  inputs[0][i].numel() == 0 or torch.isinf(e)
                  for i, e in enumerate(expect)
              )
          )
      self.assertEqual(expect, actual, equal_nan=False)
  
imports: from contextlib import nullcontext
  from numbers import Number
  import random
  import re
  import torch
  import unittest
  import itertools
  import weakref
  from torch.testing import make_tensor
  from torch.testing._comparison import default_tolerances
  from torch.testing._internal.common_cuda import TEST_MULTIGPU
  from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_ROCM, skipIfTorchDynamo, parametrize, gradcheck
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, onlyCUDA, ops, OpDTypes
  from torch.testing._internal.common_methods_invocations import foreach_unary_op_db, foreach_binary_op_db, foreach_pointwise_op_db, foreach_reduce_op_db, foreach_other_op_db
  from torch.testing._internal.common_dtype import all_types_and_complex_and, floating_types_and, floating_types, integral_types_and
label_desc: Use image node_id 18 for calling the TestForeach obj's underlying member method code with example usage: obj.test_foreach_l2_large_value_input(device, dtype, op) without return types