code: def test_tensors_grouping(self):
      num_tensors_per_list = 10
      num_devices = torch.cuda.device_count()
      dtypes = (torch.float16, torch.float32, torch.float64)
      list1 = [
          torch.tensor(
              i,
              device=torch.device(
                  "cuda", random.randint(0, num_devices - 1)
              ),
              dtype=dtypes[random.randint(0, 2)],
          )
          for i in range(num_tensors_per_list)
      ]
      list2 = [None for _ in list1]
      list3 = [torch.rand_like(t) for t in list1]
      nested_tensorlists = [list1, list2, list3]
      grouped_tensors = (
          torch.utils._foreach_utils._group_tensors_by_device_and_dtype(
              nested_tensorlists, with_indices=True
          )
      )
      num_tensors_seen = 0
      for (device, dtype), (
          [l1, l2, l3],
          indices,
      ) in grouped_tensors.items():
          for t in itertools.chain(l1, l3):
              self.assertEqual(t.device, device)
              self.assertEqual(t.dtype, dtype)
              num_tensors_seen += 1
          self.assertEqual(len(l1), len(l2))
          self.assertTrue(all(p is None for p in l2))
          for i, index in enumerate(indices):
              self.assertEqual(l1[i], list1[index])
              self.assertEqual(l2[i], list2[index])
              self.assertEqual(l3[i], list3[index])
      self.assertEqual(num_tensors_seen, 2 * num_tensors_per_list)
  
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 23 for calling the TestForeach obj's underlying member method code with example usage: obj.test_tensors_grouping() without return types